Fotis Foukalas
email: fotisf@dtu.dk

Paul Pop

Fabrice Theoleyre
email: theoleyre@unistra.fr

Carlo Alberto Boano
email: cboano@tugraz.at

Chiara Buratti
email: c.buratti@unibo.it

Dependable Wireless Industrial IoT Networks: Recent Advances and Open Challenges

Keywords: Industrial Internet of Things, Wireless Communication, Dependability

Industrial Internet of Things (IIoT) networks are considered the large-scale deployment of IoT devices for industrial applications such as smart manufacturing, harvesting and supply chain management. The Internet of Things (IoT) devices are typically connected over a wireless medium, given the large geographical distribution area and the increasing demand for flexible installations. In some cases, a combination of wired and wireless connectivity can be assumed as common practice. In both scenarios, wireless communications for IIoT networks is a fundamental component of the system architecture that needs to satisfy stringent requirements such as reliable connectivity and minimal delays. Therefore, the dependability of wireless communications for IIoT networks should be carefully studied to provide new solutions, which can guarantee that applications can meet their real-time and reliability requirements while optimizing the control capability of the overall network. This paper focuses on the dependable wireless communications in the IIoT networks, where wireless control and monitoring tasks need to meet stringent real-time and reliability constraints. After reviewing recent solutions and discussing their suitability for IIoT networks, we highlight the yet open challenges that needs to be tackled by both academia and industry.

I. INTRODUCTION

Recent advances in wireless communication have increased the pervasiveness of wireless devices and enabled a plethora of new application domains. The use of wireless technologies is also expected to revolutionize industrial applications and to enable the creation of an Industrial Internet of Things (IIoT). Wireless IIoT solutions typically target industrial automation applications, such as the monitoring and control of various devices in a factory. Taking into account the industry 4.0 vision about deploying robotics for smart digital factories, wireless IIoT networks are becoming more interesting from a research and innovation point of view. On the one hand, with the growing number of wireless technologies and protocols for IIoT applications, it is becoming increasingly more important to benchmark their performance in order to understand which solutions are suitable for a given IIoT scenario and to allow meeting stringent application requirements. On the other hand, providing dependability is intrinsically challenging when deploying wireless IIoT networks.

In systems engineering, dependability is the ability to provide services that can defensibly be trusted within a given time-period [START_REF] Avizienis | Basic concepts and taxonomy of dependable and secure computing[END_REF]. Examples of dependability attributes are availability, reliability, safety, integrity and maintainability. In the next sections, we review several articles that are related to wireless IIoT networks and that address the aforementioned dependability attributes, highlighting recent trends and open challenges (Sect. II). We then discuss recent efforts in benchmarking IIoT systems and comparing their dependability (Sect. III). We further compare decentralized IIoT architectures with centralized ones (Sect. IV). Finally, we introduce recent work on long-range wireless industrial networks (Sect. V).

II. DEPENDABLE WIRELESS IIOT NETWORKS: OVERVIEW AND OPEN CHALLENGES

A. Dependability overview in wireless IIoT networks

Industrial automation of smart digital factories represents the most challenging objective for future IIoT networking use cases. It mainly includes monitoring and control. In case of wireless IIoT networks, the design requirements include real-time monitoring and wireless industrial control [START_REF] Lu | Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems[END_REF]. Due to wireless fluctuations, both requirements are challenging to meet; however, solutions have been already provided. A common wireless IIoT network for monitoring and control is depicted in Fig. 1, where many sensor devices transmit measurements to the wireless domain controller. This is known as convergecast scheduling and it is used by WirelessHART extensively [START_REF] Zhang | Performance Bounds and Latency-Optimal Scheduling for Convergecast in WirelessHART Networks[END_REF]. In such a wireless IIoT network deployment, the latency-optimal convergecast scheduling problem is important to solve, where each sensor device has one packet to transmit to the controller, and the objective is to collect the data from all sensor devices at the controller in the minimum time. Further, the channel-constrained latency-optimal convergecast scheduling problem is also important to study, in which the number of channels available for convergecast is limited.

We now discuss about the availability, reliability, safety, integrity and maintainability attributes in IIoT-related studies: A key insight in this work is that we can map the multichannel fixed priority transmission-scheduling problem for WirelessHART networks to the fixed priority real-time CPU scheduling on a global multiprocessor platform.

• Reliability: when using Industrial Wireless Sensor Networks (IWSNs) in industrial applications such as realtime monitoring, it is important to consider both the sampling period and the end-to-end deadline [START_REF] Yang | Assignment of Segmented Slots Enabling Reliable Real-Time Transmission in Industrial Wireless Sensor Networks[END_REF]. The sampling period determines how often the monitoring application measures and transmits a new sample. The end-to-end deadline is measured from the time a packet is generated to the time it reaches its final destination. • Safety: Yang et al. [START_REF] Yang | Safe-WirelessHART: A Novel Framework Enabling Safety-Critical Applications Over Industrial WSNs[END_REF] present a novel framework for the support of safety-critical applications over mesh topology and multiple hops. The framework changes the timetriggered safety functions in IEC 61784-3-3 to eventtriggered, which are more efficient for IWSNs (Fig. 2).

To provide high priority for emergency safety events, an alarm-based method is also proposed for IWSNs. The most important metric for safety-critical applications is the safety function response time (SFRT). • Integrity: in IIoT networks, integrity is often related to fault-tolerance in WSNs [START_REF] Hu | Fault-tolerant clustering topology evolution mechanism of wireless sensor networks[END_REF]. Fault detection in WSNs is a technique that identifies a fault when it occurs and pinpoints the type of fault and its location. Fault detection techniques can be classified into centralized, distributed, and hybrid. In centralized approaches, many algorithms are based on machine learning techniques. • Maintainability: this attribute typically deals with the fault tolerance provided by cloud and fog computing architectures in IIoT services [START_REF] Li | Service Popularity-Based Smart Resources Partitioning for Fog Computing-Enabled Industrial Internet of Things[END_REF]. For example, in [START_REF] Terry | Toward a new approach to IoT fault tolerance[END_REF], the authors discuss an IoT platform that provides application fault tolerance through connection to multiple operating hubs. Devices can automatically connect to such operating hubs reporting similar events.

B. Open challenges

Several open challenges in terms of the aforementioned dependability attributes can be identified within a common core issue in several wireless IIoT use cases: the co-design of control, networking and computing in such a distributed networking environment [START_REF] Hu | A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective[END_REF]. IIoT networks are devoted to seamlessly interconnect complex industrial systems and to improve the reliability, efficiency, and productivity of industrial systems. The control, networking, and computing have already been identified as three key systems in IIoT networks. The integrated design of control, networking, and computing is hence an important and promising direction for future research [START_REF] Hu | A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective[END_REF]. Such a co-design is also mentioned as coupling between wireless communications: control therefore motivates a cyber-physical co-design approach that integrates wireless networks, and control designs. The coupling between realtime communication and control requires a cyber-physical co-design approach for a holistic optimization of control performance [START_REF] Lu | Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems[END_REF]. To this direction, authors in [START_REF] Li | Service Popularity-Based Smart Resources Partitioning for Fog Computing-Enabled Industrial Internet of Things[END_REF] proposed the implementation architecture of the SPSRP scheme, which first decoupled the computing control layer from the computing layer, and provided a programmable interface for IIoT operators. The computing control layer was decoupled from the computing layer and a programmable resources partitioning interface was provided. This novel architecture facilitated the IIoT to be more scalable to embrace industrial situation awareness. In addition to the co-design, we could distinguish the following open challenges:

• A timely analysis and processing of the massive data is also important to the time sensitive industrial systems. • Visualization, virtualization and interoperability are key factors to enabling efficient management, effective utilization and timely maintenance. • The mobility patterns and traffic patterns shall be considered in the design of wireless networks for industrial systems.

• An effective design and efficient deployment schemes, the mobility and network traffic patterns of machine devices in IIoT shall be understood, and all possible mobility scenarios should be covered. • Traffic scheduling schemes can manage a large amount of traffic with the consideration of system performance metrics and requirements, including latency, utilization of network resources. • Real-time information delivery that each traffic flow shall be delivered with latency guarantee requirements.

III. BENCHMARKING IIOT SYSTEMS AND PROTOCOLS

Over the last decade, an increasing number of low-power wireless technologies and communication protocols have been developed in order to satisfy the requirements of a wide range of IoT applications. These technologies are largely different in nature: devices making use of IEEE 802.15.4, Bluetooth Low Energy, and ANT+ radios, for example, enable short-range multi-hop communications, whilst devices embedding LoRa, NB-IoT, Sigfox, and Weightless transceivers can be used to build long-range wide area networks. All these technologies specify different signal management functions, modulation schemes, data rates, channel bandwidths and separations. This results in largely-diverse performance and makes it hard to quantify or compare (i) the suitability for a given IIoT application, as well as (ii) the achievable dependability in terms of reliability, timeliness, and availability (energy-efficiency). Indeed, several communication protocols have been proposed in conjunction with these technologies to sustain a dependable performance, e.g., in noisy RF environments. Such protocols range from multi-hop routing solutions making use of timeslotted channel hopping [START_REF] Shi | DiGS: Distributed Graph Routing and Scheduling for Industrial Wireless Sensor-Actuator Networks[END_REF], [START_REF] Tavakoli | Dependable Interference-Aware Time-Slotted Channel Hopping for Wireless Sensor Networks[END_REF], [START_REF] Hosni | Self-healing Distributed Scheduling for Endto-end Delay Optimization in Multihop Wireless Networks with 6TiSCH[END_REF], to solutions based on synchronous transmissions and constructive interference [START_REF] Istomin | Interferenceresilient Ultra-low Power Aperiodic Data Collection[END_REF], [START_REF] Mager | Feedback Control Goes Wireless: Guaranteed Stability over Lowpower Multi-hop Networks[END_REF], [START_REF] Chang | Constructive Interference in 802.15.4: A Tutorial[END_REF]. With so many options available, choosing the best constellation for an IIoT application with stringent dependability requirements in terms of reliability, availability, and timeliness can be very complex. This is especially true, as the parametrization of a given protocol for a specific technology strongly affects the achievable performance. Moreover, the set of configurable parameters to be chosen can be quite large, which exacerbates the problem even further.

A. Lack of Benchmarks for IIoT Protocols and Systems

Traditionally, benchmarking suites help system designers and industry practitioners to select the best combination of communication technology, network stack, and protocol parameters, to meet the desired application requirements. However, to date, there is a severe lack of benchmarks that evaluate the performance of low-power wireless networking protocols [START_REF] Duquennoy | A Benchmark for Low-power Wireless Networking[END_REF]. Existing IIoT benchmarks, such as EEMBC's IoTMark-BLE [START_REF]IoTMark-BLE: an EEMBC Benchmark[END_REF] and Cisco's TPCx-IoT [START_REF]The TPCx-IoT Benchmark for IoT Gateway Systems[END_REF] focus only on the energy-efficiency (availability) of BLE edge devices and on the data aggregation as well as storage capabilities of IoT gateways, respectively. Other benchmarks evaluate distributed stream processing systems hosted on cloud data-centers [START_REF] Shukla | RIoTBench: A Real-time IoT Benchmark for Distributed Stream Processing Platforms[END_REF] or specifically analyse the dependability (timeliness) of different IoT platforms as a function of the employed processor [START_REF] Kruger | Benchmarking Internet of Things Devices[END_REF]. When it comes to the performance of (I)IoT communication protocols, there is a severe lack of benchmarking suites. Low-power wireless protocols and systems are often tested experimentally on a large-scale using public testbeds with tens of nodes such as FlockLab [START_REF] Lim | FlockLab: A testbed for distributed, synchronized tracing and profiling of wireless embedded systems[END_REF], Indriya [START_REF] Doddavenkatappa | Indriya: A lowcost, 3D wireless sensor network testbed[END_REF], and FIT IoT-LAB [START_REF] Adjih | FIT IoT-LAB: A Large Scale Open Experimental IoT Testbed[END_REF]. The extent to which the results for one system hold in another setup, however, remains rather unclear [START_REF] Boano | Towards a Benchmark for Low-power Wireless Networking[END_REF].

The problem is threefold: on the one hand, experimentation with wireless communications is intrinsically hard to reproduce and repeat, due to the strong impact of the surrounding environmental conditions as well as of the employed experimental setup. On the other hand, there is a lack of a common framework describing the configuration used to experimentally test the communication performance of the wireless system at hand. Such a configuration includes the test scenario (e.g., parameters such as traffic pattern and load) as well as the test environment (e.g., the density of the network in the testbed employed for the experiments) [START_REF] Boano | Towards a Benchmark for Low-power Wireless Networking[END_REF]. Furthermore, there is no well-defined methodology specifying how to plan, execute, and report on the obtained experimental results. Such a methodology should specify which metrics should be computed, which (and how much) data should be collected, the minimum number of trials to be performed, as well as how results should be processed and synthesized [START_REF] Jacob | Towards a Methodology for Experimental Evaluation in Low-Power Wireless Networking[END_REF]. As a result, the performance of low-power wireless networking protocols is hardly comparable, and little is known about the dependability of low-power wireless protocols used to build IIoT applications. Furthermore, there is also a lack of quantitative comparisons of protocol performance in the presence of harsh environmental conditions, which are typical of industrial environments and often cause repeated failures hard to troubleshoot [START_REF] Wetzker | Troubleshooting Wireless Coexistence Problems in the Industrial Internet of Things[END_REF]. For example, temperature variations are known to affect communication performance [START_REF] Bannister | Wireless Sensor Networking for Hot Applications: Effects of Temperature on Signal Strength, Data Collection and Localization[END_REF], [START_REF] Boano | Mitigating the Adverse Effects of Temperature on Low-Power Wireless Protocols[END_REF], [START_REF] Boano | Impact of Temperature Variations on the Reliability of LoRa: An Experimental Evaluation[END_REF], whereas radio interference typically increases packet loss, endto-end latency, and energy consumption [START_REF] Boano | External radio interference[END_REF] -affecting in turn key dependability attributes of IIoT systems such as reliability, timeliness, and availability.

B. Recent Efforts in Benchmarking IoT Systems and Protocols

To fill this gap, the community has started to join forces in order to enable a more repeatable and reproducible experimental validation of low-power wireless IoT networking systems. For example, the Association for Computing Machinery (ACM) has introduced a badging system to make sure that the published research is documented, consistent, exercisable, and reproducible [START_REF]Artifact Review and Badging[END_REF]. Moreover, an increasing number of scientific venues explicitly solicits the open availability of datasets and tools [START_REF]Data: Acquisition To Analysis[END_REF] as complementary materials. These initiatives show an important trend emphasizing the need for objective and reproducible comparisons, which can serve as a reference for the evaluation of products from the IoT industry. The 6TiSCH Open Data Action (SODA) [START_REF] Vucinić | SODA: 6TiSCH Open Data Action[END_REF] is a research project that aims to provide a reference benchmark for 6TiSCH solutions in order to automate and facilitate the comparison Beyond these efforts, the IoTBench initiative [START_REF]IoTBench: A Community Effort to Better Evaluate and Compare Low-Power Wireless Networks[END_REF], composed of several independent academic and industrial partners, explicitly aims to provide a set of tools and methods for benchmarking low-power IoT communication systems. Initial work within IoTBench has paved the way towards a common methodology for experimental evaluation [START_REF] Jacob | Towards a Methodology for Experimental Evaluation in Low-Power Wireless Networking[END_REF] and a common framework describing the test configuration of wireless networking experiments [START_REF] Boano | Towards a Benchmark for Low-power Wireless Networking[END_REF]. Furthermore, parallel efforts within IoTBench have focused on developing (i) low-cost testbeds with benchmarking capabilities [START_REF] Schuß | A Competition to Push the Dependability of Low-Power Wireless Protocols to the Edge[END_REF] and (ii) tools enabling the repeatable generation of harsh RF conditions [START_REF] Schuß | JamLab-NG: Benchmarking Low-Power Wireless Protocols under Controlable and Repeatable Wi-Fi Interference[END_REF]. These have been used -among others -to run public events to quantitatively benchmark the dependability of state-of-the-art wireless IIoT systems in harsh RF environments [START_REF] Boano | EWSN Dependability Competition: Experiences and Lessons Learned[END_REF], and to stress-test the performance of Bluetooth Low Energy (BLE) in the presence of radio interference [START_REF] Spörk | Improving the Timeliness of Bluetooth Low Energy in Noisy RF Environments[END_REF], as we describe next. Low-cost testbeds with benchmarking capabilities. In order to allow the creation of low-cost infrastructures suitable to benchmark the performance of IIoT communication systems, Schuß et al. have developed D-Cube [START_REF] Schuß | A Competition to Push the Dependability of Low-Power Wireless Protocols to the Edge[END_REF]. The latter is a lowcost tool that can be used to build or extend IoT testbeds such that one can accurately measure in hardware key dependability metrics such as end-to-end delay, reliability, and power consumption (availability), as well as to graphically visualize their evolution in real-time. D-Cube allows an automated, seamless, and fully user-customizable execution of low-power wireless networking evaluations, as shown in Fig. 3. Besides defining input parameters that directly characterize the configuration used to experimentally test the communication performance, e.g., (i) traffic parameters such as pattern and load; (ii) system parameters such as network density; (iii) experiment parameters such as duration and number of runs; D-Cube also allows a user to define output metrics quantifying the performance of the system under test. Such output metrics match the dependability attributes of interest, e.g., the IIoT system's reliability and timeliness, as well as availability (i.e., the overall energy consumption). Furthermore, D-Cube has been enriched with a binary patching unit that allows to split traffic pattern and node identities from the application specifications, which potentially allows an automated testing of different protocol parameters and configurations [START_REF] Schuß | Moving Beyond Competitions: Extending D-Cube to Seamlessly Benchmark Low-Power Wireless Systems[END_REF].

Tools to generate repeatable radio interference. The last years have seen a significant improvement in the performance of IIoT communication protocols and network stacks: to date, indeed, achieving 100% reliability in clean RF environments is possible at a relatively low price in terms of latency and energy [START_REF] Duquennoy | Five-Nines Reliable Downward Routing in RPL[END_REF], [START_REF] Herrmann | Mixer: Efficient Many-to-All Broadcast in Dynamic Wireless Mesh Networks[END_REF]. Achieving a high performance also in the presence of harsh RF conditions (that are typical of industrial environments), however, still remains a grand challenge. The main challenge here is to study and compare the performance of different IIoT communication protocols in the presence of RF interference. Indeed, there is a lack of tools enabling the controllable and repeatable generation of interference using, for example, Wi-Fi devices (the most prolific wireless technology operating in the 2.4 GHz ISM band). To fill this gap, Schuß et al. have developed JamLab-NG [START_REF] Schuß | JamLab-NG: Benchmarking Low-Power Wireless Protocols under Controlable and Repeatable Wi-Fi Interference[END_REF], a tool that enables the fine-grained control of individual linklayer transmissions of a Wi-Fi device. JamLab-NG avoids the uncontrollable delays introduced by the network stack, the operating system, and the clear channel assessment procedure. Furthermore, it allows to control radio settings such as transmission speed and packet length: these would traditionally be adapted by the radio firmware automatically -making it impossible to repeat the generated interference patterns [START_REF] Schuß | JamLab-NG: Benchmarking Low-Power Wireless Protocols under Controlable and Repeatable Wi-Fi Interference[END_REF].

Benchmarking the performance of IIoT solutions. D-Cube and JamLab-NG have been used together to run the EWSN (Embedded Wireless Systems and Networks) dependability competition series [START_REF] Boano | EWSN Dependability Competition: Experiences and Lessons Learned[END_REF]. The latter is an international event organized to quantitatively benchmark the dependability of stateof-the-art academic and industrial IoT solutions for multi-hop data collection and multi-hop dissemination in the presence of harsh Wi-Fi interference. Fig. 4 shows exemplary results from the 2018 edition for the reliability and timeliness of different IoT solutions in the presence of Wi-Fi interference resembling long bursts of fixed duration on a pre-defined channel. One can observe that solutions based on synchronous transmissions such as OFPCOIN [START_REF] Ma | Competition: Using Enhanced OFPCOIN to Monitor Multiple Concurrent Events under Adverse Conditions[END_REF], Chaos [START_REF] Nahas | Competition: Aggressive Synchronous Transmissions with In-network Processing for Dependable All-to-All Communication[END_REF], BigBangBus [START_REF] Escobar | Competition: BigBangBus[END_REF], and Crystal [START_REF] Trobinger | Competition: CRYSTAL Clear: Making Interference Transparent[END_REF] do achieve a high reliability with minimal endto-end latency despite the congested RF environment. Further results can be found in [START_REF]EWSN 2018 Dependability Competition -Official blog[END_REF]. In the 2019 edition, D-Cube was used to evaluate the performance of various IoT solutions using a different set of input parameters, such as the size of packets, the number of source and destination nodes, as well as the traffic load and period. Furthermore, JamLab-NG has been used to study the reliability of BLE in the presence of Wi-Fi interference [START_REF] Spörk | Improving the Timeliness of Bluetooth Low Energy in Noisy RF Environments[END_REF], showing that an adaptation of the existing solutions is necessary to meet strict timeliness requirements -an important observation for BLE-based IIoT systems operating in congested RF environments.

IV. DECENTRALIZED VS. CENTRALIZED IIOT ARCHITECTURES: OPEN CHALLENGES Industrial networks consist of a large set of sensors and actuators that exchange packets together, or via a set of border routers (Fig. 5). To provide strict guarantees, these networks rely on a strict transmission schedule to avoid collisions and to provide a high reliability. As wireless transmissions are unreliable by nature, most standards rely on slow frequency hopping techniques to improve the reliability [START_REF] Watteyne | Reliability through frequency diversity: Why channel hopping makes sense[END_REF]. As a result, one packet and its retransmissions do not use the same channel, reducing the probability to be affected repetitively by external interference.

Thus, to provide a set of guarantees, one needs to carefully build a 2D scheduling matrix [START_REF] Hermeto | Scheduling for IEEE802.15.4-TSCH and Slow Channel Hopping MAC in Low Power Industrial Wireless Networks[END_REF]. The channel offsets (frequency) are represented vertically, while the timeslots are represented horizontally (Fig. 5). A cell in the matrix denotes, for a pair of devices, the timeslot and the channel offset used to transmit a packet. For safety-critical applications, the schedule has to be constructed such that it provides an upper-bound for the end-to-end delay. Typically, cells for retransmissions are contiguous in Fig. 5 to reduce the delay.

Because IIoT rely on wireless transmissions, the scheduling algorithm has to respect half-duplex constraints, a device cannot receive or transmit during the same timeslot through different channel offsets [START_REF] Palattella | On optimal scheduling in duty-cycled industrial IoT applications using IEEE 802.15.4e TSCH[END_REF]. Similarly, the scheduler has to assign the same cell only two non-interfering transmitters.

Thus, we face to two possible approaches: Centralized: a central entity (aka the Controller) has a complete view of the network's characteristics and requirements, and computes a global schedule. Then, the controller notifies individually each device with the part of the schedule it has to follow (as receiver or transmitter); Distributed: when a pair of nodes wants to exchange packets, they need to define autonomously the timeslots and channel offsets to use. They have to avoid creating collisions with already scheduled transmissions in the vicinity.

A. Open Problems for Centralized Scheduling

Centralized scheduling algorithms can construct a schedule able to respect a set of guarantees. For instance, we may provision additional bandwidth for retransmissions to respect end-to-end reliability constraints [START_REF] Dobslaw | End-to-end reliability-aware scheduling for wireless sensor networks[END_REF]. Tools exist to verify that a schedule can respect weakly hard real-time constraints [START_REF] Schmitt | Sensor network calculus -a framework for worst case analysis[END_REF].

1) Monitoring: Constructing an efficient schedule requires to have a precise view of the network properties. For instance, the scheduler needs: reliability: each link has to monitor the Packet Error Rate so that end-to-end reliability can be esimated; traffic: the controller needs to know when packets are generated, and which is the destination; network topology: to select the right set of forwarding devices in multihop topologies, all links have to be reported.

Practically, this represents a huge amount of control traffic, which has to be delivered reliably to the controller. To reduce the load, control information can be piggybacked in the data traffic, to increase the packet length instead of the number of packets [START_REF] Gaillard | Monitoring kpis in synchronized ftdma multi-hop wireless networks[END_REF]. To the best of our knowledge, no research paper describes experimental results reporting the efficiency of a centralized schedule executed on top of a large scale multihop topology.

2) Network Formation and Convergence: To bootstrap, the devices which join the network need to have specific transmission opportunities to join the controller. In the Software Defined Network architecture, a specific control plane exists, forwarding all the packets to the controller that configures later the network to forward the data packets [START_REF] Thubert | 6TiSCH centralized scheduling: When SDN meet IoT[END_REF]. Typically, besteffort cells are reserved for the network formation. However, a large number of devices implies also a large convergence delay since the Slotted-Aloha method leads to many collisions [START_REF] Hermeto | On the (over)-reactions and the stability of a 6TiSCH network in an indoor environment[END_REF]. Reliability is only achieved after the network has converged, i.e., each data flow has dedicated resources.

3) Integrity (fault-tolerance): Notifying the controller that needs to recompute a novel schedule requires a significant amount of time. Thus, fault-tolerance has to be considered directly when constructing the schedule. Graph routing techniques creates a collection of paths toward the border routers [START_REF] Song | WirelessHART: Applying wireless technology in realtime industrial process control[END_REF]. By allocating the same resource through different paths, the network can handle a node's failure without affecting reliability. Anycast scheduling consists in exploiting the broadcast nature of wireless transmissions to schedule several receivers for a transmission to handle a fault [START_REF] Hosni | Adaptive k-cast Scheduling for highreliability and low-latency in IEEE802.15.4-TSCH[END_REF].

After having detected a fault, the controller needs a long time (e.g., a few minutes) to update its network level view, to recompute a novel schedule, and to push the updates reliably to all the devices. Thus, faults have to be sufficiently interspaced to let the network re-converge.

B. Open Problems for Distributed Scheduling

For a distributed strategy, we need two complementary mechanisms: a routing protocol so that each device knows a next hop toward the destination (often, the border router). RPL [START_REF] Thubert | RPL: IPv6 routing protocol for low-power and lossy networks[END_REF] represents the most common standard routing protocol for low-power and lossy networks; scheduling techniques to decide which cells to use for any pair of transmitter/receiver. Typically, a protocol such as 6P defines the way to negotiate the cells to use [START_REF] Wang | 6TiSCH operation sublayer (6top) protocol (6p)[END_REF].

1) Reliability (collision Avoidance):

Because each pair of device has only a partial knowledge of the cells previously allocated, distributed scheduling may lead to collisions. Several existing mechanisms try to identify collisions by identifying the cells that exhibit an abnormally low Packet Delivery Ratio [START_REF] Muraoka | Simple distributed scheduling with collision detection in TSCH networks[END_REF]. However, these solutions assume stable conditions (external interference, link reliability) with periodic traffic. In particular, bursty traffic makes the collisions less repetitive, i.e., only one part of the cells are busy, introducing a bias in the reliability estimation. Typically, detecting collisions for backup cells may be very challenging, although they are absolutely necessary to respect high reliability constraints.

2) Safety: Most critical applications require that a packet is delivered within a given deadline. Unfortunately, tackling such an end-to-end delay constraint is particularly challenging in multi-hop topologies, since fixing the instant of emission for the first hop may create a domino effect [START_REF] Gaillard | Kausa: Kpi-aware scheduling algorithm for multi-flow in multi-hop iot networks[END_REF]. Only a few scheduling algorithms have been proposed to tackle this problem [START_REF] Hosni | Self-healing distributed scheduling for endto-end delay optimization in multihop wireless networks with 6TiSCH[END_REF]. They consist in reserving consecutive slots in priority for the different devices along a path. In the same way, additional slots have to be reserved consecutively for retransmissions. However, such approach also increases the number of collisions, and complicates significantly the schedule's updates. Indeed, allocating another cell for retransmissions may require to re-schedule the rest of the path toward the destination. Fig. 6: End-to-end PDR when using the number of transmissions as routing metric with static (centralized) vs. dynamic (distributed) approaches.

C. Experimental Evaluation

We exploit here the dataset obtained in an indoor testbed, monitoring continuously 267 links during 90 minutes 1 . In particular, we split the whole experiment in different time windows of different size (i.e., the learning duration). We focus here on the reliability characteristic, measuring the endto-end reliability when not considering retransmissions with static vs. dynamic routes (Fig. 6). For the static case, the routes are computed once, by minimizing the cumulative number of transmissions, when using the first time window to compute the routing cost. For the dynamic case, routes are updated at the end of each time window, with the statistics from the last time window. We can see that using time windows that are too short (e.g., 3 seconds) is inefficient: the statistics are not sufficiently smoothed and the end-to-end reliability is quite low. However, 30 seconds are sufficient to compute stable link quality metrics, and to identify reliable paths. Besides, the static and dynamic cases provide a very similar reliability in that case. Thus, exploiting static schedules seems reasonable, given that the link characteristics are sufficiently stable for so long durations.

D. Perspectives

Mixing distributed and centralized solutions seems to represent a promising solution. Recently, MABO-TSCH [START_REF] Gomes | Mabo-tsch: Multihop and blacklist-based optimized time synchronized channel hopping[END_REF] has combined a centralized scheduling algorithm with a localized process to decide which channels to use for each transmission. The bundle concept of 6TiSCH [START_REF] Thubert | 6TiSCH centralized scheduling: When SDN meet IoT[END_REF] allows the controller to allocate blocks of cells to each device. Then, each device is in charge of managing its bundles, allocating transmission opportunities to its neighbors. Collisions can be avoided if the controller allocates the bundles to different areas. By properly distributing one part of the decisions, we may be able to allocate enough resources to enable safety. The network would also be more fault-tolerant to guarantee the integrity, by taking local decisions.

V. DEPENDABLE WIRELESS SOLUTIONS FOR THE IIOT

In the past years, Zigbee over IEEE 802.15.4 [START_REF]Standard for Local and metropolitan area networks-Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs)[END_REF] has dominated the IoT scene, with application even to (some) industrial contexts. The advantages of IEEE 802.15.4-based solutions over the Wi-Fi family of standards lay on better energy efficiency, which is of particular relevance when nodes are equipped with tiny batteries, as in the case of IIoT applications requiring nodes to be deployed over machines where cables have to be avoided. The IEEE 802.15.4, used over the 2.4 GHz ISM band, allows transmissions at a bit rate of 250 kbit/s, which can be useful for a number of industrial applications, especially when monitoring machines equipped with nodes that cannot be connected to the energy grid. However, this technology is based on distributed protocols, both at the medium access control (MAC) layer and at the network level, making its dependability arguable. It suffers from several limitations that degrade its performance: the unbounded delay, MAC unreliability and interference robustness [START_REF] Nikoukar | Low-Power Wireless for the Internet of Things: Standards and Applications[END_REF]. To overcome these limitations, 6TiSCH has been proposed, and can provide a relevant solution for short-range, multihop topologies.

In the past few years, a number of solutions have emerged in the category of long range communication systems. We differentiate among those who have been standardized by 3GPP (like LTE-M and NB-IoT [START_REF] Ratasuk | NB-IoT system for M2M communication[END_REF], [START_REF] Feltrin | Narrowband IoT: A Survey on Downlink and Uplink Perspectives[END_REF]) and work over frequency bands used by telecom operators, and those that use ISM (license-exempt) bands, typically at 868 or 900 MHz, like LoRa [START_REF] Feltrin | LoRaWAN: Evaluation of Link-and System-Level Performance[END_REF] and Sigfox. LoRa is a proprietary solution that can be implemented by a network provider that ensures coverage through the LoRaWAN protocol stack and architecture, or through a dedicated ad-hoc protocol suite designed to fulfill specific requirements. LoRa defines a transmission technique which is very flexible: through the configuration of the Spreading Factor (SF) parameter, nodes can trade transmission ranges with bit rate. The smaller is the SF, the higher is the bit rate while the range decreases, increasing reliability. However, due to the limited bandwidth available in the 868 or 900 MHz ISM bands, the bit rate is never larger than a few tens of kbit/s. Very recently, the company that holds LoRa's patents has delivered a solution running at 2.4 GHz, which is characterized by the same degree of flexibility, while using larger bandwidths (hence providing higher bit rates), resulting in lower end-to-end delays. In other words, LoRa at 2.4 GHz can provide the same performance offered by Zigbee over IEEE 802.15.4, with the possibility of using the SF as a parameter to trade dynamically range with data rates. ¿¿¿¿¿¿¿ ca2486a5a76a98cf73dd0acfdd69d758bc71b4e6 A. LoRaIN: A LoRa-Based Dependable Solution for IIoT Since January 2018, when the SX1280 chip implementing LoRa at 2.4 GHz was delivered by Semtech, a number of institutions are prototyping multi-hop protocols to be used over Fig. 7: The LoRaIN Architecture. such transmission technique. Among them, the University of Bologna, under commitment by a local industry, is designing a reliable, flexible and robust protocol stack for dependable IIoT. In particular, the scope is to conceive a system where sensor nodes (up to 1000) are deployed over a machine, with a density of up to 50 nodes/sqm, where part or all of them apply harvesting techniques to accumulate the energy needed to communicate the sensor measurements and receive commands or strings of bytes to be recorded in the local memory.

The proprietary protocol (denoted as LoRaIN: LoRa Industrial Network) is based on a hierarchical network architecture (see Fig. 7), where a Level 1 Gateway (G1), linked to the HMI (Human Machine Interface) of the industrial machine, manages the commands coming from it, and implements the algorithms used to control the network of sensor nodes (denoted as tags). The G1 node is connected through Ethernet to few Level 2 Gateways (G2s), each of them being responsible for the communication with the tags in a different area, denoted as cluster. Communication through tags and G2 happen via the LoRaIN protocol. Every G2 uses two frequency bands: one is common to all G2s and it is used for tag association purposes, while the other is different from cluster to cluster, to ensure that inter-cluster interference is avoided. Within a cluster, up to 50 tags communicate according to a schedule defined by G1 and managed by G2s.

A centralized approach is used to manage the access to the channel: G2s periodically send beacon packets to maintain synchronization and to assign time slots to nodes as soon as a request of communication is started at the HMI and forwarded to G2s via G1. Each communication frame (where a frame is the time interval between two subsequent beacons) is split into sub-intervals: one is left for the tag to harvest the energy needed for the following steps, one for the uplink and/or downlink communication. Every beacon describes the structure of the following frame, which is dynamically defined according to the commands received from the HMI, and the current network state. The duration of frames (inter-beacon time), and of the two sub-intervals (for energy harvesting and for communication) are dynamically computed (at G1) depending on the commands received, the amount of bytes to be sent/received.

Through the time slots scheduling, intra-cluster interference is completely absent, as for the inter-cluster interference is avoided through the implementation of a frequency reuse strategy. The latter approach ensures that the network of tags is highly reliable. Moreover, LoRaIN takes advantage of the degree of flexibility offered by LoRa, by chosing SF according to the path losses measured from the tags to the relevant G2. The more the cluster G2 is deployed in a proper position, the better are the links and higher the bit rates, with consequent smaller delays in the provision of the monitored data.

As far as delays are concerned, the ability of LoRaIN to provide data from the tags with stringent delay requirements (compatible with a bit rate no larger than approximately 250 Kbit/s), depends on the interplay between the type of energy harvesting technique implemented and the amount of data to read/write. For those tags that are battery-driven, the first subinterval of each frame can be eliminated and the HMI can receive data from a selected tag within about 200 ms, a delay compatible to many monitoring applications. Delays are larger when the miniaturization of tags does not allow the use of a (even tiny) battery. The main issue that is subject to some level of unpredictability, thus representing a threat to the level of reliability/dependability, is external interference.

B. LoRaIN: Testing Reliability

In this subsection, the reliability of the network is tested in terms of Packet Error Rate (PER), being the percentage of packets lost in the LoRaIN network (working at 2.4 GHz), when affected by the interference generated by an IEEE 802.11g network.

For experiments we have used the TP-Link TL-WA830RE as access point, configured to work in IEEE 802.11g mode with maximum available transmit power and a maximum rate allowed of 54 Mbit/s. To generate a constant Wi-Fi occupancy we used two laptops working as video streaming client and server (VLC Media player was running at both laptops). An HTTP (HyperText Transfer Protocol) server was set up to broadcast an mp4 video in on-demand fashion. The experimental setup is shown in Fig. 8: Server and AP were connected exploiting an Ethernet cable, while the client was communicating to the AP via the wireless connection through IEEE 802.11g. As for LoRa, we used the SX1280 [69] devices; the transmitter-receiver distance was set to 3.2 m and both devices were at an height of 0.5 m from the floor.

In order to characterize the interference level suffered by the LoRa receiver, we estimated the Signal-to-Interference Ratio (C/I) as follows. A Wi-Spy device was located near the LoRa receiver to measure the interfering power, I, while the useful received power, C, was directly measured by the LoRa receiver using the received signal strength indicator (RSSI). Finally, we used the Chanalyzer to measure the channel utilization, defined as the percentage of time the received signal was In Fig. 9 we show the measured PER as a function of LoRa's SF for different values of C/I. The channel occupancy was 20%. The carrier frequency of LoRa signal was set to 2,452 GHz, that is the central frequency of channel 9 used by the IEEE 802.11g network (completely overlapped channels). The bandwidth selected for LoRa transmission was set to 203 kHz. As expected, the PER presents a maximum value due to the following. On the one hand, by increasing the spreading factor, the time on air of LoRa increases, bringing to larger collision probability; on the other hand, large values of spreading factor result in a modulation which is more robust to interference. In fact, when setting SF =12 the PER becomes zero, except for the case C/I = -38 dB, where the SIR is too low and the PER cannot reach zero. In conclusion, by properly setting the spreading factor, LoRaIN demonstrated to have high reliability; however a trade-off between robustness to interference and end-to-end delay should be found, since the increasing of SF results in longer time-on-air, and therefore delays. However, in all cases delays are bounded.

VI. CONCLUSIONS

This paper provides an overview on the recent advances and open challenges in building dependable wireless IIoT networks. We first describe efforts in benchmarking wireless IIoT networking technologies and protocols. Particular studies have been discussed and the dependability aspects of such technologies have been highlighted. We then present a study on decentralized and centralized IIoT architectures and an overview of recent efforts in the area of long-range wireless technologies that are suitable for IIoT settings. As we point out throughout the paper, there are still several open challenges that need to be addressed in order to deploy dependable wireless systems in future industrial automation applications: only by solving those one can exploit the flexibility offered by wireless technology and build a reliable and safe IIoT.

Fig. 1 :

 1 Fig. 1: Many-to-one communication in wireless IIoT networks.

Fig. 2 :

 2 Fig. 2: Functional safety and response time modeling in wireless IIoT networks.

Fig. 3 :

 3 Fig.3: Benchmarking an IIoT system using a testbed infrastructure based on D-Cube (adapted from[START_REF] Schuß | Moving Beyond Competitions: Extending D-Cube to Seamlessly Benchmark Low-Power Wireless Systems[END_REF]).

Fig. 4 :

 4 Fig. 4: Exemplary results obtained during the EWSN 2018 dependability competition, comparing the reliability and timeliness of different IoT protocols.

Fig. 5 :

 5 Fig. 5: Network topology and scheduling matrix for an industrial network.

Fig. 8 :

 8 Fig. 8: Interference Evaluation: Experimental setup.

Fig. 9 :

 9 Fig. 9: Packet Error Rate as a function of the SF in a LoRa network affected by the interference generated by an IEEE 802.11g network, for different SIR values.

https://github.com/ftheoleyre/fitiotlab-multichannel-dataset


Loïc Constantin

Lisha Fan

Mael Pontoreau

Fei Wang

Bai Cui

Jean-Luc Battaglia

Dr Jean-François Silvain
email: jean-françois.silvain@icmcb.cnrs.fr

Yong Feng Lu

Loic Constantin

Additive manufacturing of copper/diamond composites for thermal management applications

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Prof. Yongfeng Lu; Address: Electrical and Computer Engineering Department, University of Nebraska-Lincoln, E430P Nebraska Hall, Lincoln, NE 68588-0511; Tel: 402-472-8323; Fax: 402-472-4732; Email: ylu2@unl.edu.

KEYWORDS:

Additive manufacturing, composites, copper, diamond, interphase ABSTRACT: Copper (Cu)/diamond (D) composites have been extensively studied as the nextgeneration thermal management materials due to its excellent thermal property. However, the presence of D particles makes processing these composites difficult. Additive manufacturing (AM) provides an intriguing method to overcome this issue because of its high degree of freedom to fabricate complex shapes. In this letter, we demonstrated the feasibility to additively manufacture

INTRODUCTION

The fabrication of components with high thermal conductivity (TC), a low coefficient of thermal expansion (CTE), and intricate shapes is crucial for the development of highly efficient heatexchanging components for microelectronic industries [START_REF] Vasiliev | Heat pipes in modern heat exchangers[END_REF][START_REF] Neugebauer | Additive manufacturing boosts efficiency of heat transfer components[END_REF]. Creating a composite material consisting of two highly thermally conductive elements, such as copper (Cu) and diamond (D), may fulfill these unmet needs [START_REF] Qu | Review of metal matrix composites with high thermal conductivity for thermal management applications[END_REF]. However, the lack of chemical affinity between Cu and D together with the difficulty of processing D-based materials into complex shape present significant challenges [START_REF] López | The solubility of C in solid Cu[END_REF][START_REF] Silvain | Copper-Carbon and Aluminum-Carbon Composites Fabricated by Powder Metallurgy Processes[END_REF].

Extensive research has been devoted to achieving highly thermally conductive metal matrix composites (MMCs) by promoting interfacial bonding between Cu and D. Cu/D composites with a low CTE and a high TC were reported resulting from a synergic effect [START_REF] Schubert | Interfacial characterization of Cu/diamond composites prepared by powder metallurgy for heat sink applications[END_REF][START_REF] Zhang | Enhanced thermal conductivity in copper matrix composites reinforced with titanium-coated diamond particles[END_REF][START_REF] Silvain | A review of processing of Cu/C base plate composites for interfacial control and improved properties[END_REF]. However, conventional processing methods (e.g., sintering and infiltration) impose several limitations when sophisticated geometries are required. The recent development of laser additive manufacturing (AM) has attracted attention due to the ability to fabricate sophisticated shapes of metal and alloy parts with properties similar to bulks [START_REF] Tran | 3D Printing of Highly Pure Copper[END_REF][START_REF] Zhang | Selective Laser Melting of Cu10Zn alloy powder using high laser power[END_REF][START_REF] Sabelle | Scanning pattern angle effect on the resulting properties of selective laser sintered monolayers of Cu-Sn-Ni powder[END_REF][START_REF] Frazier | Metal Additive Manufacturing: A Review[END_REF][START_REF] Constantin | Laser sintering of coldpressed Cu powder without binder use[END_REF]. Nevertheless, to our best knowledge, AM of Cu/D composites remains less explored.

In this letter, the feasibility of additive manufacture using Cu/D composites was investigated by laser engineering net shaping (LENS), as illustrated in Figure 1 (c). The Cu/D composites were printed using a composite powder mixture of 95 vol.% Cu, and 5 vol.% D coated with a graded TiO2-TiC layer. It was shown that an energy density of 1200 J/mm 3 led to the formation of dense Cu/coated-D composites (96%) with a strong bonding throughout the Ti-based interphase.

Additionally, the printed composites with Ti-based interphase show a 10% enhancement of the TC compared to the same composite processed by conventional powder metallurgy.

MATERIALS AND METHODS

Diamond powders with a mean diameter of 105 μm (Eastwind Diamond Abrasives) were coated with a graded TiO2-TiC layer using a molten salt process (details are provided in the supplementary information (SI)). A 5 vol.% of coated D was mixed with spherical Cu powder with a mean diameter of 93 μm (99.5% US Research Nanomaterials, Inc.). AM was performed using an Optomec, Inc., LENS 3D Hybrid Machine Tool System. The laser power and scan speed were varied between 700 and 1000 W, 4.25 and 21.2 mm/s, and the layer thickness and hatch distance were kept constant to 0.2 and 0.3 mm respectively (details in the SI).

The crystalline phases and chemical structures of the coated D were analyzed both by X-ray diffraction (XRD, Bruker-AXS D8 DISCOVER Diffractometer, Cu Kα = 0.154 nm) and micro-Raman (inVia ™ , Renishaw). The volume percentage of D in the printed composites was estimated by image analyses using ImageJ. The density of the printed samples was measured using the Archimedes method (METTLER TOLEDO AT201). The microstructures of the parts printed were characterized using a scanning electron microscope (ESEM ™ , FEI Quanta ™ 200 Environmental SEM) and a high-resolution transmission electron microscope (HRTEM, FEI Tecnai Osiris ™ , 200 kV). The TC was measured via the periodic photothermal radiometry method (details in the SI and elsewhere [START_REF]Signal noise ratio improvement technique for bulk thermal diffusivity measurement[END_REF]).

RESULTS AND DISCUSSION

The realization of dense Cu/D composites with high TC requires the formation of strong interphase between the Cu and D because of their incompatibility [START_REF] Silvain | Copper-Carbon and Aluminum-Carbon Composites Fabricated by Powder Metallurgy Processes[END_REF][START_REF] Kang | Preparation of copper-diamond composites with chromium carbide coatings on diamond particles for heat sink applications[END_REF]. Our previous study on titanium (Ti)based coating on carbon fibers via a molten salt process has demonstrated an enhanced wettability of molten Cu on a graded TiO2-TiC layer compared to a pure TiC layer. The same process has been applied on D particles [START_REF] Constantin | Design of tailored oxide-carbide coating on carbon fibers for a robust copper/carbon interphase[END_REF]. The formation of a graded layer was confirmed by Raman spectroscopy mapping, as shown in Figure 1 (a) and(b), where the yellow and red colors represent TiC and TiO2, respectively, and by XRD of the coated D, as shown in Figure S3 [START_REF] Lohse | Raman spectroscopy as a tool to study TiC formation during controlled ball milling[END_REF][START_REF] Zhu | Composites of TiO2 Nanoparticles Deposited on Ti3C2 MXene Nanosheets with Enhanced Electrochemical Performance[END_REF]. Coated D particles were then mixed with Cu powder (Figure 1(d)). The printing parameters were explored to form a dense Cu/D composite. It was observed that scan speeds between 8.7 and 21.7 mm/s and laser power levels between 700 and 1000 W, mainly resulted in parts with rough surfaces (Figure 2 (a) and (d)). The poor surface finish has arisen because the Cu powder was insufficiently melted which inhibits its ability to fuse completely [START_REF] Maamoun | Effect of Selective Laser Melting Process Parameters on the Quality of Al Alloy Parts: Powder Characterization, Density, Surface Roughness, and Dimensional Accuracy[END_REF]. Nevertheless, a narrow processing window was identified for obtaining parts with smooth surface finish: a scan speed of 12.7 mm/s and laser powers ranging from 900 to 925 W (Figure 2 (b) and(d)). At a low scan speed (4.3 mm/s) and high laser powers (800 to 1000 W), the printed parts exhibit a spherical shape, not a designed cubic shape (Figure 2 (c) and(d)). The deformation of the printed cube is due to the elevated temperature during the printing process. [START_REF] Rape | Alloy development for highly conductive thermal management materials using copper-diamond composites fabricated by field assisted sintering technology[END_REF], a TiC interphase [START_REF] Azina | Effect of titanium and zirconium carbide interphases on the thermal conductivity and interfacial heat transfers in copper/diamond composite materials[END_REF], and a graded TiO2-TiC interphase.

The diamond volume percentage in the Cu/coated-D composite printed was measured to be about 25 vol.%. compare to 5 vol.% in the powder feeding. A significant increase in the diamond volume percentage was found in the printed composites. One possible explanation is that during the laser-matter interaction, Cu particles were ejected or evaporated from the molten pool, leading to an increased concentration of coated-D in the final composites (Figure 2 (f)). Figure 2 (e) shows the relationship between the sample density and energy density [START_REF] Jadhav | Influence of selective laser melting process parameters on texture evolution in pure copper[END_REF]. It is noted that an energy density of about 1200 J/mm 3 yields a density of 96% with a TC of about 330 W/m.K. The TC of the Cu/TiO2-TiC-D (330 W/mK) in this work is 10% and 20% higher than the TC reported for Cu/D (276 W/mK) [START_REF] Maamoun | Effect of Selective Laser Melting Process Parameters on the Quality of Al Alloy Parts: Powder Characterization, Density, Surface Roughness, and Dimensional Accuracy[END_REF] and Cu/TiC-D (302 W/mK) [START_REF] Rape | Alloy development for highly conductive thermal management materials using copper-diamond composites fabricated by field assisted sintering technology[END_REF], respectively.

The interfacial bonding between the matrix and the reinforcement is the key to obtain dense and high TC composites. The D quality is a crucial point to study the feasibility to laser print 3D Cu/D composites due to the potential D graphitization during the printing process. The D quality was evaluated after being immersed in a salt bath (coating process) and exposed to a high-power laser (printing process) as shown in Figure 4 (a) -(c). The as-received diamond powder showed a sharp peak around 1331.5 cm -1 , with a full width at half maximum (FWHM) of 1.9 cm -1 , confirming its high degree of crystallinity (Figure 4 (d) and (e)) [START_REF] Donato | High quality CVD diamond: a Raman scattering and photoluminescence study[END_REF][START_REF] Constantin | Effects of Laser Photolysis of Hydrocarbons at 193 and 248 nm on Chemical Vapor Deposition of Diamond Films[END_REF]. Diamond particles were recollected and reevaluated after the salt coating process and the printing process. As shown in

Figure 1 .

 1 Figure 1. (a) SEM micrograph of a D particle coated with a TiO2-TiC layer, with a Raman mapping in the insert; (b) Raman spectra of TiO2, TiC, and a TiO2-TiC mixture from the Raman mapping; (c) Schematic of the LENS process; and (d) SEM micrograph of the Cu/D powder mixture.

Figure 2 .

 2 Figure 2. SEM micrographs of Cu/D composite surfaces printed at an energy density of (a) 708, (b) 1180, and (c) 1764 J/mm 3 ; (d) qualitative quality of Cu/D vs. printing parameters; (e) sample density vs. laser energy density; (f) illustration of particle ejection during LENS; and (g) thermal conductivities of Cu-D composites with no interphase[START_REF] Rape | Alloy development for highly conductive thermal management materials using copper-diamond composites fabricated by field assisted sintering technology[END_REF], a TiC interphase[START_REF] Azina | Effect of titanium and zirconium carbide interphases on the thermal conductivity and interfacial heat transfers in copper/diamond composite materials[END_REF], and a graded

Figure 3 (

 3 c) shows a cross-sectional micrograph of the interface between Cu and D, where the coated-D was previously embedded in the Cu matrix (Figure 3 (a) and (b)). As shown in the SEM micrograph, an intimate contact between the Cu matrix and the coated-D particle was found, suggesting a strong connection. The HRTEM micrographs at the Cu-TiO2 and TiO2-TiC interfaces demonstrate a smooth transition between layers with no pores, confirming a strong bonding (Figure 3 (d) and (e)). Additionally, HRTEM of the Cu, TiO2, and TiC layers in

Figure 3 (

 3 Figure 3(f)-(h) shows the crystalline structures of the (111) Cu, (101) TiO2 and (111) TiC planes with an interlayer distance of 0.21, 0.35, and 0.25 nm, respectively [23,24].

Figure 3 .

 3 Figure 3. (a)-(b) SEM micrographs of the Cu/D surface. TEM micrographs of (c) crosssectional view of Cu-(TiO2-TiC)-D, (d) Cu/TiO2 interface, and (e) TiO2/TiC interface. (f)-(h) HRTEM micrographs of Cu, TiO2, and TiC, respectively.

Figure 4 (

 4 d) and (e), the processed diamond particles still exhibit a sharp diamond peak, suggesting no graphitization occurred during the high-temperature coating and printing processes. The diamond peak position and FWHM values retrieved from the Raman spectra show no obvious peak broadening, confirming the multi-step processing did not degrade the crystallinity of the diamond particles.

Figure 4 .

 4 Figure 4. SEM micrographs of D particles at different stages of (a) as received, (b) after salt coating, and (c) after laser printing. (d) Raman spectra of an as-received D particle and D particles after salt coating and laser printing, and (e) FWHM and position of the diamond peaks measured from the D particles as-received, after salt coating, and after laser printing.

Acknowledgment

Manufacturing and characterization analyses were performed at the NanoEngineering Research Core Facility (part of the Nebraska Nanoscale Facility), which is partially funded from the Nebraska Research Initiative. Dr B. Cui acknowledges the financial support from the NASA Nebraska Space Grant. The authors would like to thank Jamie Eske for her help on the English editing.

CONCLUSION

This work demonstrates the feasibility of additive manufacturing using Cu/D composites. An energy density of about 1200 J/mm 3 is necessary to make dense composites with smooth surfaces and high TC (330 W/m.K). The introduction of Ti-based coating on the diamond particles before printing enabled the formation of strong interphase between the matrix Cu and reinforcement D, facilitating the printing of dense Cu/D composites. In addition, the quality of the post-printing diamond was found to be close to the original one, making the process suitable for manufacturing high-TC materials.

AUTHOR INFORMATION

Corresponding Authors

*Jean-Francois Silvain: jean-francois.silvain@icmcb.cnrs.fr and Yong Feng Lu: ylu2@unl.edu

Author Contributions

All authors approved the final version.


Inès Hosni
email: ines.hosni@hotmail.fr

Fabrice Théoleyre
email: theoleyre@unistra.fr

Adaptive k-cast scheduling for high-reliability and low-latency in IEEE802.15.4-TSCH

Keywords: IEEE802.15.4-TSCH, k-cast transmissions, duocast, scheduling algorithms, high-reliability, low jitter

The Industrial Internet of Things tends now to emerge as a key paradigm to interconnect a collection of wireless devices. However, most industrial applications have strict requirements, especially concerning the reliability and the latency. IEEE802.15.4-TSCH represents currently a promising standard relying on a strict schedule of the transmissions to provide such guarantees. The standard ISA-100.11a-2011 has proposed the concept of duocast, where a pair of receivers are allocated to the same transmission opportunity to increase the reliability. In this paper, we generalize this approach to involve k different receivers, and we explore the impact of this technique on the performance of the network. We propose an algorithm assigning several receivers for each transmission to increase the probability that at least one device receives correctly the packet. By exploiting a multipath topology created by the routing layer, we are able to reduce the number of transmissions while still achieving the same reliability. We consequently increase the network capacity, and reduce significantly the jitter. Our simulation results highlight the relevance of this k-cast technique in TSCH for the Industrial Internet of Things.

Introduction

Industry 4.0 is promised as the next industrial revolution, where digital automation aims to reduce the cost and to maximize the flexibility [START_REF] Li | A review of industrial wireless networks in the context of Industry 4.0[END_REF]. In particular, the Industrial Internet of Things (IIoT) aims to connect a large set of industrial objects to the Internet. In this context, a real-time infrastructure has to provide high-reliability for the wireless transmissions.

While the Internet of Things focused so far on best-effort solutions, industrial applications have often strict requirements concerning the reliability and the delay [START_REF] Al-Anbagi | A Survey on Cross-Layer Qualityof-Service Approaches in WSNs for Delay and Reliability-Aware Applications[END_REF]. We need consequently specific MAC protocols able to provide strict guarantees. Deterministic approaches are particularly relevant, allocating a fixed bandwidth to each device or flow. These solutions can provide also flow isolation, where each flow receives a certain bandwidth, dedicated for its transmissions.

IEEE802.15.4-TSCH relies on channel hopping to increase the robustness to external interference and fading [START_REF]IEEE Standard for Low-Rate Wireless Personal Area Networks (LR-WPANs)[END_REF]. TSCH adopts a deterministic approach, and schedules carefully the transmissions to avoid the collisions. More precisely, it allocates a set of different cells for interfering transmitters to reduce the contention while still avoiding collisions. A cell is defined by a pair of timeslot and channel offset. The channel offset is translated into a physical frequency at the beginning of the timeslot, to follow a pseudo-random sequence.

Many centralized and distributed scheduling algorithms [START_REF] Hermeto | Scheduling for IEEE802.15.4-TSCH and slow channel hopping MAC in low power industrial wireless networks: A survey[END_REF] have been proposed so far for TSCH. To cope with unreliable links, the scheduling process often uses overprovisionning, and reserves several of cells so that a packet can be retransmitted. Unfortunately, more cells mean that the delay and the jitter may increase [START_REF] Papadopoulos | Leapfrog Collaboration: Toward Determinism and Predictability in Industrial-IoT applications[END_REF]. This over-provisioning also reduces the network capacity if the traffic is large and/or the links are very unreliable because of e.g. external interference.

IEEE802.15.4-TSCH is largely inspired from ISA-100.11a-2011 [START_REF]Wireless systems for industrial automation:process control and related applications[END_REF]. ISA has proposed a duocast mechanism to increase the reliability: two receivers are assigned to a given transmission. A transmission is considered a failure only if both receivers failed to decode correctly the packet.

In this paper, we aim to investigate the relevance of using k-cast scheduling, i.e. a cell is assigned to one transmitter, and k possible receivers (ordered by their priority). Subslots allow each receiver to acknowledge the transmission if none of the receivers with a larger priority sent an ack. The contributions of this paper are threefold:

1. we propose a scheduling algorithm tailored for k-cast transmissions. We propose to minimize the energy consumption for both transmitting and receiving the packets. Each node selects the set of parents to assign to a given cell to minimize this energy consumption; 2. we investigate the impact of the k-cast technique on the amount of duplicated packets. Indeed, the receivers may receive all the packets, but not the ack they transmit. In this case, the packets are duplicated wasting energy and bandwidth; 3. we implemented our scheduling algorithm and the k-cast technique to evaluate its performance. We highlighted this technique helps to improve the reliability with a limited impact on the energy consumption.

Related Work

IEEE 802.15.4-2015 has proposed the TSCH mode, which relies on a strict schedule of the transmissions [START_REF]IEEE Standard for Low-Rate Wireless Personal Area Networks (LR-WPANs)[END_REF]. The slotframe contains a fixed number of timeslots, during which at most one frame and its acknowledgment are transmitted. Each timeslot is labelled with an Absolute Sequence Number (ASN) which counts the number of timeslots since the PAN coordinator started. Based on the schedule, a node can decide its role (transmitter/receiver/sleeping mode) at the beginning of each timeslot. IEEE 802.15.4-2015 TSCH implements a channel hopping approach to combat external interference and signal fading and, thus, to achieve high reliability [START_REF] Watteyne | Reliability through frequency diversity: Why channel hopping makes sense[END_REF]. For this purpose, each cell in the schedule is defined by a pair of timeslot and channel offset. At the beginning of each timeslot, the actual frequency to use is derived from the channel offset and the ASN.

IEEE802.15.4-TSCH supports two medium access approaches:

shared cells are allocated to a group of nodes (e.g. for broadcast). A packet is for the first time transmitted without contention. If it corresponds to a unicast packet, and no ack is received, a random backoff is then used. This backoff corresponds to the number of cells to skip before the retransmission; dedicated cells are allocated to non-interfering transmitters. Thus, the transmitter just starts the transmission after a fixed offset from the beginning of the timeslot.

Improving the reliability

Several techniques have been proposed to improve the reliability in this kind of deterministic architecture. Over-provisioning consists in reserving additional cells to retransmit the packets if the transmission has failed. For instance, the number of cells may be inversely proportional to the Packet Delivery Ratio for the considered radio link [START_REF] Theoleyre | Experimental Validation of a Distributed Self-Configured 6TiSCH with Traffic Isolation in Low Power Lossy Networks[END_REF]. Dobslaw et al. [START_REF] Dobslaw | End-to-End Reliability-aware Scheduling for Wireless Sensor Networks[END_REF] propose rather to fortify a centralized schedule by allocating additional cells to the most unreliable links until the deadline constraint cannot be anymore respected. Hashimoto et al. [START_REF] Hashimoto | End-to-end reliability-and delay-aware scheduling with slot sharing for wireless sensor networks[END_REF] propose to allocate shared cells for the retransmissions. However, shared cells are prone to collisions, impacting negatively the reliability of retransmissions.

Multipath also helps to improve the end-to-end reliability [START_REF] Kafi | A Survey on Reliability Protocols in Wireless Sensor Networks[END_REF]. Papadopoulos et al. [START_REF] Papadopoulos | Leapfrog Collaboration: Toward Determinism and Predictability in Industrial-IoT applications[END_REF] propose to use multiple parents, replicating the packets over the two paths. With overhearing, several receivers try to decode a packet, increasing the reliability. A node which receives several times the same packet (identified with a sequence number) must drop the subsequent copies. However, the authors focus on a fault-tolerance scenario: the link quality drops suddenly on the primary path. We propose rather to address the normal case, where radio links may be unreliable on average.

Opportunistic routing consists in choosing several next hops. The destination address is transformed in anycast so that the same transmission can be received by any receiver. Typically, opportunistic routing helps to improve the efficiency in asynchronous wireless networks [START_REF] Wang | Optimizing opportunistic routing in asynchronous wireless sensor networks[END_REF]. In IEEE802.15.4-TSCH, all the receivers wake-up synchronously. However, opportunistic routing is still efficient to improve the reliability: it is sufficient that one of the receivers decodes correctly the packet. Huynh et al. demonstrated that opportunistic scheduling helps to improve theoretically the reliability when exploiting a Rayleigh fading channel [START_REF] Huynh | On the interest of opportunistic anycast scheduling for wireless low power lossy networks[END_REF].

6TiSCH

6TiSCH has defined a set of protocols to execute IPv6 above IEEE802.15.4-TSCH. It relies on RPL (Routing over Low-Power Lossy Networks) to construct the routing topology [START_REF] Winter | Rpl: Ipv6 routing protocol for low-power and lossy networks[END_REF]. A node uses a link quality metric and the rank of its parent to compute its own rank, denoting its virtual distance from the sink. Typically, the link quality is estimated via ETX (Expected Transmission Count), the average number of transmissions before the packet is acknowledged by the receiver. By default, RPL uses only one preferred parent (next hop toward the sink), but has been extended to support also multi-parent routing [START_REF] Iova | Using multiparent routing in RPL to increase the stability and the lifetime of the network[END_REF].

Scheduling the transmissions has attracted much attention in the past [START_REF] Hermeto | Scheduling for IEEE802.15.4-TSCH and slow channel hopping MAC in low power industrial wireless networks: A survey[END_REF]. The 6P protocol [START_REF] Wang | 6top protocol (6p). draft 12[END_REF] is in charge of negotiating the cells to use. The transmitter sends a request to the receiver to decide how many, and which cells to use. Then, 6TiSCH relies on a so-called Scheduling Function (SF) to decide which and how many cells to use between each pair of nodes. 6TiSCh supports both centralized (e.g. TASA [START_REF] Palattella | On Optimal Scheduling in Duty-Cycled Industrial IoT Applications Using IEEE802.15.4e TSCH[END_REF]) and distributed (e.g. SFx [START_REF] Dujovne | 6TiSCH Experimental Scheduling Function (SFX)[END_REF]) scheduling algorithms. For instance, SFx [START_REF] Dujovne | 6TiSCH Experimental Scheduling Function (SFX)[END_REF] maintains the number of cells at least equal to the number of packets to forward, and relies on an hysteresis function to limit the number of oscillations.

3 k-cast scheduling: choosing the right set of parents Let us consider the example illustrated in figure 1. A node A has two ordered parents P 1 and P 2 . During the first timeslot, P 1 receives correctly the packet and acknowledges it. During the second one, the transmission to P 1 fails, but P 2 is able to decode the packet: it should be able to acknowledge it to avoid another transmission. Typically, the ack of P 2 is transmitted if the medium is still idle after a timeout: it means P 1 is not currently transmitting an ack. We aim here to investigate which strategy to adopt to assign several receivers to the same cell (k-cast) to improve the reliability. With perfect links, k-cast transmissions are useless since the primary parent always receives the packet correctly. The other parents will have to awaken to receive the packet although they are useless. On the other hand, unreliable links may be combatted when several receivers can all decode the same packet.

Besides, opportunistic scheduling would be inefficient if the radio link among parents is very unreliable. In that case, a parent may not hear that a previous parent is currently transmitting an ack: it will start to transmit its own ack, which will create a collision, and retransmissions. Moreover, both parents are convinced they both have to forward the packet: a duplicated packet has been created.

Number of cells to provision for a given set of parents

Let us first compute the number of cells a node has to reserve with its parent. We rely on RPL to construct the routes [START_REF] Winter | Rpl: Ipv6 routing protocol for low-power and lossy networks[END_REF] (cf section 2.2). Then, the scheduling algorithm has to decide how many cells to reserve with its parent. We decide here to minimize the energy consumption, i.e. energy for reception and transmission so that at least one parent has received correctly the data packet.

Let denote by P the set of parents selected by the node n to forward its packets. We compute the Packet Delivery Ratio (PDR) for this node, i.e. average number of transmissions before one parent has acknowledged correctly the packet. In most implementations, each node divides the number of packets it transmits by the number of acks it receives. When a packet is transmitted to several receivers / parents, the transmission has failed if none is able to send an ack:

P DR n→P = 1 - p∈P (1 -P DR(p)) (1)
where P DR(p) denotes the average Packet Delivery Ratio toward the parent p, i.e. ratio of unicast packets received correctly by p.

We force the scheduling function to reserve a sufficient number of cells to achieve a given minimum reliability (e.g. P DR th = 99%) within the current slotframe. The number of transmission opportunities (n txop (n → P)) We denote by n txop (n → P) the number of transmission opportunities to reserve to guarantee a minimum reliability. Typically, n txop (n → P) must contain one copy and all its retransmissions to have a minimum Packet Delivery Ratio:

1 -(1 -P DR n→P ) ntxop(n→P) ≥ P DR th (2)
n txop (n → P) = log(P DR th ) log (1 -(1 -P DR n→P )) (3)
Finally, we have to provision enough cells to forward all the packets:

nbCells = n txop (n → P) * n pkts (n → P) (4)
where nbCells denotes the the number of cells reserve from n to its parents, and n pk (n → P) denotes the average number of packets that the node n has to forward to its parents within a slotframe. Cells are then removed and inserted dynamically in the schedule to respect this reliability. To avoid oscillations, we use an hysteresis function, similarly to the scheduling function SFx [START_REF] Dujovne | 6TiSCH Experimental Scheduling Function (SFX)[END_REF].

Energy Consumption to choose the right set of parents

We should not allocate systematically all the parents to a given cell. Indeed, medium link qualities do not require to have so many receivers: only the first parents will be actually useful, the other ones will waste their energy for idle listening. Inversely, allocating an insufficient number of receivers has a negative impact on the reliability and the delay: the source node needs more retransmissions to deliver the packet and to receive an ack.

Let us compute the energy consumption associated to a correct reception of a packet transmitted by a node n. For a sake of simplicity, a node assumes the link quality with its different parents may differ, but all the parents have paths with similar qualities toward the border router. P keeps on denoting the set of parents selected by the node n to forward its packets.

Thus, the energy consumption (in transmission mode) is finally:

E tx (n) = n txop (n → P) * E tx (5)
where E tx denotes the energy in TX mode for one cell.

Each parent has to be awake during all these cells. However, only some of them are actually busy, the other ones only correspond to idle listening. Thus, the energy to receive the packets of n can be estimated as:

E rx (n) = n pkts (n → P) * E rx + (nbCells -n pkts (n → P)) * E idle (6)
where E rx (resp. E idle ) denotes the energy in RX (resp. IDLE) mode for one cell.

We have consequently to compute the set of parents which minimizes the energy consumption

E total (n) = E rx (n) + E tx (n) (7)
Thus, the energy consumed by a node depends on the Packet Delivery Ratio toward its parents, and on the number of cells to provision to guarantee a given reliability. Inserting more parents increases the Packet Delivery Ratio and thus decreases the energy to transmit one packet. On the contrary, it also increases the energy consumed to receive the packets (idle and rx) for the parents. We will propose in the next section a strategy to decide which and how many parents to select.

Adaptive and Localized Scheduling Strategy

We propose here a scheduling algorithm adapted for k-cast. In particular, we have to decide how many and which parents should be allocated for each transmitting cell. Using a bad link instead of a good link is never relevant: a node should always use its best parents.

We propose a greedy approach to decide which cells to schedule (algo. 1). A node n applies the following approach:

1. n ranks its parents according to their PDR in descending order (line 2); 2. greedily, it inserts the next best parent in the list of forwarders (lines 5-6); 3. if the energy consumption (for both RX and TX) is reduced, then we continue the greedy allocation, and we consider the next parent. Else, the node n finally assigns the current list of forwarders (except the last parent) to its cells. In other words, the algorithm stops when a local minimum is achieved. To decide how many cells have to be allocated, the node n uses the eq. 4.

Theorem 1. This greedy strategy allocates the set of parents which minimizes the energy consumption.

Proof. Let us follow here a proof by contradiction. We order the parents by their decreasing PDR value. Our algorithm terminates with the set P. If the set P is not optimal, we replace one parent p of P by another parent p with a smaller energy consumption. The reception energy of p is at least equal to those of p since the number of cells with p cannot be smaller (eq. 6) since p provides a larger PDR than p.

We can prove similarly that when the algorithm discards the k th parent because it starts to increase the energy consumption, another parent with a lower PDR cannot decrease further the energy consumption.

Performance Evaluation

We evaluate here the performance of IEEE802.15.4TSCH with k-cast relying on openwsn3 . We re-use here the approach of SFx [START_REF] Dujovne | 6TiSCH Experimental Scheduling Function (SFX)[END_REF] to define how many cells have to be reserved. Each node computes dynamically the number of cells to use according to the number of packets to forward, and the Packet Delivery Ratio in the cells. Then, we decide how many parents have to be allocated to a given cell. Our implementation is fully available at https://github.com/ineshos/ 6TSCH/tree/master/python/ScheduleSimulator/simulator. We rely here on simulations, and control the physical Packet Delivery Ratio (complement of the PER) of the links (Tab. 1). We assume the physical PDR remains fixed for the whole simulation. We first focus on a simple topology to evaluate the behavior of one flow, and then we study the performance in a random topology.

Simple forwarding scenario

We first consider the topology illustrated in figure 2, where the physical Packet Delivery Ratio (i.e. the complement of the Packet Error Rate) has been fixed statically. The source generates 100 packets for the sink, 2 hops away. Using more parents allows the network to increase the reliability: a single transmission may be received by any of the parents (Fig. 3a). However, using more than 6 parents seems to be inefficient. Indeed, the bad parents have only a marginal impact: they receive seldom the packets correctly. Besides, such parents would forward a packet only if all the previous parents (with a better link quality) have not transmitted an ack. This will occur very infrequently, and their impact on the reliability is very limited.

Figure 3b illustrates the number of duplicated packets (the packets which are received several times by the sink). With only two receivers, the amount of duplicates can be neglected (less than 2% of the packets). With many receivers, the number of duplicates becomes significant, and has a negative impact on the energy consumption: redundant and useless packets are forwarded by the different parents.

Figure 3c illustrates the delay. Because of the anycast transmission, we let any receiver forward the packet. Thus, the delay decreases with more receivers. Selecting many receivers has always a positive impact on the delay, even if it also increases the energy consumption.

Finally, we measured the number of cells that SFx reserves in the schedule (Fig. 4). The number of shared cells varies between 12 and only 3 cells for the whole network (20 devices), depending on the volume of traffic to forward. We remind that using dedicated cells requires to reserve at least one cell for each device (thus 20 cells). Some additional cells would even be required to cope with retransmissions with non perfect radio links, which is the case here for most of the radio links. Thus, using shared cells is particularly efficient to reduce the number of allocated cells.

Random Topology

We now consider a random topology of 20 nodes, and assign a physical PDR to each radio link, uniformly distributed between 20 and 90%. This way, we verify that k-cast transmissions help to improve the efficiency even in more complex topologies.

We first measure the number of packets received when transmitting 100 packets (Fig. 5a). We make the same remarks as for the simple topology case: k-cast keeps on improving the reliability, to the same extent. Thus, the k-cast approach helps to improve globally the network-layer PDR: almost all packets are correctly delivered to the sink.

We measure then the amount of duplicated packets with a variable traffic (Fig. 5b). The amount of duplicated packets is linear with the inter-packet time. Thus, the solution is scalable and robust to the traffic conditions.

Finally, we consider the energy consumption in Figure 5c. Using more receivers decreases at the beginning the energy consumption since less retransmissions are required to deliver a packet. In particular, selecting 3 parents allows the network to reduce its energy consumption by almost 30% compared with the unicast approach. On the contrary too many receivers means that we increase idle listening, which has a negative impact on the energy consumption when the parent has a marginal contribution on the delivery (i.e. it is seldom involved in the transmission). We obtain the optimal energy efficiency with 5 parents. Obviously, this optimal number of receivers depends on the ability of the device to turn fast its radio off when a cell is unused. Thus, it depends on the radio chipset specifications.

Conclusion & Perspectives

We propose a distributed algorithm to exploit k-cast scheduling: several receivers are assigned to the same transmission, to improve the probability of success. Our algorithm computes the set of parents to schedule so that the energy consumption is minimized, considering both the RX and TX mode. Our simulations show kcast helps to improve the reliability with a low energy consumption. The amount of duplicated packets is reasonable when a small number of parents (e.g. 2 or 3) are assigned to the same cell.

In the future, we plan to investigate the actual cost of implementing such k-cast feature in TSCH. In particular, a larger number of acks means often a longer timeslot, which may waste energy. Besides, we also plan to investigate the number of duplicated packets which may be generated by complex topologies (hidden terminals, asymmetrical links, etc.)

Fig. 1 :

 1 Fig. 1: Duocast Transmission (with two different parents)

Fig. 2 :

 2 Fig. 2: Simple topology with multiple forwarding nodes with different link qualities.

Fig. 3 :

 3 Fig. 3: Performance of a source with multiple parents with different link qualities.

Fig. 4 :

 4 Fig. 4: Impact of the traffic load on the number of allocated cells.

Fig. 5 :

 5 Fig. 5: Random topology -20 nodes, physical PDR picked uniformly between 10 and 90%.

  Algorithm 1: Parent Selection for Scheduling

		Data: set of parents (P arents)
		Result: set of forwarders (F W )
		// initialization
	1 F W ← ∅;
	2 rank(P arents, PDR);
	3 energy ← ∞;
		// adds a new parent in the forwarder set
	4 repeat
		// picks the first parent in the list
	5	p ← popF irst(Parents);
	6	F W ← F W ∪ {p};
		// updates the energy cost according to eq. 7
	7	energyOld ← energy;
	8	energy ← computeEnergy(FW);
		// repeat until the energy starts to increase
	9 until energy > energyOld;
		// removes the last forwarder added in the list
	10 removeLast(FW);

11 return (F W );

Table 1 :

 1 Default values of the parameters in our simulations.

http://openwsn.org provides an opensource implementation of IEEE802.15.4-TSCH