Ben Dhafer
email: dhafera@qmic.com

Arbia

Muhammad Mahtab Alam

Rabah Attia
email: rabah.attia@enit.rnu.tn

Elyes Ben Hamida
email: elyesb@qmic.com

Behavior of Wireless Body-to-Body Networks Routing Strategies for Public Protection and Disaster Relief

Keywords: Tactical Ad-hoc Networks, Public Safety and Emergency, Routing Protocols, IEEE 802.11, IEEE 802.15.4, IEEE 802.15.6, Performance Evaluation I

Critical and public safety operations require realtime data transfer from the incident area(s) to the distant operations command center going through the evacuation and medical support areas. Any delay in communication may cause significant loss. In some cases, it is anticipated that the existing communication infrastructures can be damaged or out-ofservice. It is thus required to deploy tactical ad-hoc networks to cover the operation zones. Routing data over the deployed network is a significant challenge with consideration to the operations conditions. In this paper we evaluate the performance of mutli-hop routing protocols while using different wireless technologies in an urban critical and emergency scenario. Using a realistic mobility model, Mobile Ad hoc, geographic based and data-centric routing protocols are evaluated with different communication technologies (i.e. WiFi IEEE 802.11; WSN IEEE 802.15.4; WBAN IEEE 802.15.6). It is concluded that, WiFi IEEE 802.11 is the best wireless technology with consideration to the packet reception rate and the energy consumption. Whereas, in terms of delay, WBAN IEEE 802.15.6 is the most efficient. With regards to the routing protocols, assuming that the location information is available, geographical based routing protocol with WiFi IEEE 802.11 performed much better compared to the others routing protocols. In case where the location information is unavailable, gradient based routing protocol with WBAN IEEE 802.15.6 seems the best combination.

INTRODUCTION

Disasters are increasing worldwide, impacting not only the economies and infrastructure but significant number of human lives. With regards to the emergency response to these disasters, the role of effective Public Safety Network (PSN) infrastructures (e.g. TETRA, LTE) is extremely vital. However, it is anticipated that, during and after the disasters, existing PSN infrastructures can be either completely damaged or oversaturated. Consequently, there is a growing demand for ubiquitous emergency response system which could be easily and rapidly deployed.

In this context, it is expected that Wearable Body Area Networks (W-BAN) can play a key role in not only providing an additional connectivity network but it can also be used to monitor the physiological status of the involved workforces and the surrounding environment [START_REF] Alam | Surveying Wearable Human Assistive Technology for Life and Safety Critical Applications: Standards, Challenges and Opportunities[END_REF]. W-BAN consists of smart, low-power, miniaturized devices such as sensors (i.e., to sense, transmit and receive data), actuator (to act on the perceive data) and coordinator (to act as a gateway to external networks). W-BAN communication architecture is composed of on-Body, Body-to-Body (or inter-Body) and off-Body communication networks. In inter-Body communications, a device coordinator with specific features could be responsible to communicate with the adjacent Wireless Body Area Networks (WBANs). A coordinator is generally considered as a resource-rich device, which can be a multi-standard node to interface with other technologies such as static Wireless Sensor Networks (WSNs), WiFi Access, Points or Broadband Cellular Networks (e.g., 4G, LTE, etc.) [START_REF] Alam | Device-to-Device Communication in Wearable Wireless Networks[END_REF]. The advent of such technology can thus be served as an ad-on to existing PSN infrastructures for improving the quality-of-service (QoS) and reliability.

In Public Protection and Disaster Relief (PPDR) scenarios, the network connectivity and data is a challenging problem due to the dynamic mobility and harsh environment [START_REF] Hamida | Analytical Evaluation of Virtual Infrastructures for Data Dissemination in Wireless Sensor Networks with Mobile Sink[END_REF]. It is envisioned that, in case of unavailable or out-of-range network infrastructures, the WBANs coordinators along with WBANs sensors can exploit cooperative and multi-hop bodyto-body communications to extend the end-to-end network connectivity. In this regards, for on-body networks, the Opportunistic and Mobility Aware Routing (OMAR) scheme is proposed in one of our earlier works [START_REF] Hamida | Short-Term Link Quality Estimation for Opportunistic and Mobility Aware Routing in Wearable Body Sensors Networks[END_REF]. The emphasis of this study is on exploration of suitable routing protocols for Body-to-Body Networks (BBN) under dynamic mobility scenario. To the best knowledge of the authors, there is currently no comprehensive study using realistic disaster mobility model and scenario that could be considered as a benchmark. In the recent proposed works [START_REF] Reina | A Survey on Multihop Ad Hoc Networks for Disaster Response Scenarios[END_REF] also, routing protocols are evaluated without consideration to the communication technologies, therefore, we as a first step, investigating various classes of routing protocols to analyze their performance in BBN context with different communication technologies.

In this paper, based on the operational and technical requirements of the emergency and rescue operations [START_REF]A NPSTC Public Safety Communications Report -Defining Public Safety Grade Systems and Facilities[END_REF], we investigate the behavior of multi-hop routing protocols with different wireless technologies for a fire emergency case inside a shopping mall. At first, a realistic mobility pattern and trace files are generated using a tool called BonnMotion [START_REF] Aschenbruck | BonnMotion :A Mobility Scenario Generation and Analysis Tool[END_REF]. These patterns and movements are geographically mapped and simulated over two incident areas inside the mall. The mobility trace file (which contains the nodes movements over space and time) is fed inside a packet-oriented network simulator called WSNet. This mobility model served as a realistic environment for the evaluation of various class of routing protocols. We considered proactive, reactive, gradient-based and geographical-based routing categories. The state-of-the-art individual protocol is selected among each category for a comparative study. For proactive, reactive, gradient-based and geographical-based routing protocols, Optimized Link State Routing protocol version 2 (OLSRv2) [START_REF] Clausen | RFC7181: The Optimized Link State Routing Protocol Version 2[END_REF], Ad-hoc On-Demand Distance Vector (AODVv2) [START_REF] Perkins | Dynamic MANET On-demand (AODVv2) Routing draft-ietf-manet-dymo-26[END_REF], Directed Diffusion (DD) [START_REF] Intanagonwiwat | Directed diffusion: a scalable and robust communication paradigm for sensor networks[END_REF] and Greedy Perimeter Stateless Routing (GPSR) [START_REF] Karp | GPSR: Greedy Perimeter Stateless Routing for Wireless Networks[END_REF] The remainder of this paper is organized as follows. In Section II, we present the related works of several categories of routing protocols; while in Section III, we describe the target application and mobility scenario along with the target routing protocols. In Section IV, we introduce the simulation parameters and we present the obtained simulation results. Finally, in Section V, we conclude the paper and discuss future research directions.

II. RELATED WORKS

In addition to the technologies inter-operability, coexistence and energy consumption issues investigated in Public Safety Networks (PSN) [START_REF] Alam | Surveying Wearable Human Assistive Technology for Life and Safety Critical Applications: Standards, Challenges and Opportunities[END_REF], Routing is an important challenge to raise in critical and emergency operations. Since there is no dedicated routing protocols for PSN, researchers are tending to evaluate the performance of diverse routing protocols such as Mobile Ad hoc Networks (MANET), Geographical Location based or Wireless Sensor Networks (WSN) routing protocols. The following sections survey existing classes of routing protocols in PSN.

A. Mobile Ad hoc Networks

Due to its flexibility to topology changes and its multi-hop routing aspect, Mobile Ad hoc networks are interesting to be investigated in PSN. Mobile Ad hoc routing protocols are divided into four main classes: Proactive, Reactive, Hybrid and Hierarchical. These routing protocols refer commonly to the link-state and distance vector algorithms, hence their classification is based on network discovery and the routing information update mechanism [START_REF] Abolhasan | A review of routing protocols for mobile ad hoc networks[END_REF].

Proactive routing protocols create and maintain continuously their routing tables, called also table-driven. In this routing class, nodes keep exchanging information to learn the network topology. Proactive protocols use one or more tables to store the topology information and routes. By these settled routes, optimization algorithms (such as Dijkstra in [START_REF] Yi | Implementation of multipath and multiple description coding in olsr[END_REF]) are applied to select best routes to use based on a selected metric. Proactive protocols differ on the technique used for neighbors sensing and topology update. The difference concerns also the messages used to: discover, maintain and share topology information and routes. Proactive routing protocols are appropriate with small networks. Due to the continuous broadcasted updates, routing overhead cause a considerable bandwidth consumption. The most widely known proactive routing protocols are: Optimized Link State Routing protocol (OLSR) [START_REF] Clausen | OLSR, IETF[END_REF], Wireless Routing Protocol (WRP), Destination-Sequenced Distance-Vector (DSDV) and recently the OLSR version 2 [START_REF] Clausen | RFC7181: The Optimized Link State Routing Protocol Version 2[END_REF]. In PSN, such routes discovery techniques and computation cause network overloading that impact negatively the bandwidth utilization and increase the power consumptions. Drawbacks of proactive routing protocols in PS are not only limited into energy inefficiency and bandwidth overload, the routing convergence delay caused by the intensive routes discovery broadcasts at the network startup is also considerable.

Unlike proactive protocols, reactive routing protocols look up for routes only when it is needed. The route discovery procedure is invoked when data packets are ready for sending. The route discovery mechanism in reactive routing protocols is the same: a source node starts by flooding a request message to all reachable nodes looking up for the destination, then, each node relay this request message until it reaches the destination. If the destination is reached, a reply message is sent back to the source node through the reverse route followed by the request. Most known on-demand routing protocols: Ad hoc On demand Distance Vector (AODV) [START_REF] Perkins | Ad-hoc on-demand distance vector routing[END_REF], Dynamic Source Routing protocol (DSR), Temporally Ordered Routing Algorithm (TORA) and more recently AODV version 2 (DYMO) [START_REF] Perkins | Dynamic MANET On-demand (AODVv2) Routing draft-ietf-manet-dymo-26[END_REF]. Reactive routing protocols in Mobile Ad hoc networks seem to solve the bandwidth utilization and energy efficiency issues based on on-demand routing request, but the delay caused by the route discovery before data transfer does not meet the PSN requirements.

In addition to Ad hoc routing protocol classes mentioned above, hybrid routing protocols class merge both techniques: reactive and proactive routing protocols. These routing protocols offer generally a combination between proactive routing for nearby nodes and reactive routing protocols for faraway nodes. In PSN, this type of protocols could be appropriate for large catastrophe zone with many incident areas, where intra-incident area is proactive and inter-incident area is reactive routing. Another routing protocol class called hierarchical or clustering routing protocols propose to divide the entire network into groups of sub-networks in order to facilitate network management functionalities, especially the routing process. One of the most known protocol from this class called Cluster Based Routing Protocol (CBRP) is recently investigated in [START_REF] Quispe | Behavior of Ad Hoc routing protocols, analyzed for emergency and rescue scenarios, on a real urban area[END_REF].

B. Data-Centric Routing Protocols

A gradient routing protocol is interesting to investigate in specific cases of PSN where all the data flow converges towards only one node (e.g. command center). The existing gradient routing approaches are designed for Wireless Sensor Networks as data-centric routing. Quite different from traditional address-centric routing considered as a flat routing, where all nodes have the same interest and importance in the network. In data-centric routing, a sink node collects all data from the other nodes in two main steps. The sink node starts by broadcasting a request to all neighbors until it reaches the concerned node. The response follows back the request path. A node forwarding data may aggregate its own data with the traveling data towards the sink node. Various gradient routing technique exist, most are variants of Directed Diffusion [START_REF] Intanagonwiwat | Directed diffusion: a scalable and robust communication paradigm for sensor networks[END_REF].

C. Geographical Location Based Routing Protocols

The drawbacks of Ad hoc networks in terms of continuous routes maintenance, storing of all network topology information into the nodes and network overload by unnecessary routes discovery (in case of proactive techniques), make further approaches come up to exceed these issues. Geographical based routing protocols are one of the proposed approaches. For more than ten years, geographical location based routing protocols avoided the technique of storing and sharing the network topology information. Routing decisions in geographic routing protocols are made hop-byhop, no end-to-end routes made as in Ad hoc, for that, nodes in geographic routing protocol network store only physically reachable nodes information [START_REF] Cadger | A Survey of Geographical Routing in Wireless Ad-Hoc Networks[END_REF].

Hence, no routes maintenance needed, because packets could follow different paths each time. In PSN, geographic location is an important parameter required regardless the routing approach used. Outdoor geographic locations could be simply obtained based on GPS technology. Indoor localization could be based on anchor nodes or simply tags sending their location. However, basing the routing decisions on geographic location must rely on a high precision available location technology, otherwise, erroneous locations lead on inefficient routing. Most known routing protocols in this class, early in 2000, Greedy Perimeter Stateless Routing (GPSR) [START_REF] Karp | GPSR: Greedy Perimeter Stateless Routing for Wireless Networks[END_REF] followed by Geographic Source Routing (GSR) and Spatially Aware packet Routing (SAR) [START_REF] Cha | A Survey of Greedy Routing Protocols for Vehicular Ad Hoc Networks[END_REF].

III. MOTIVATIONS AND SCENARIO:

A. Context and Motivations

This paper presents an ongoing research of the project Critical and Rescue Operations using Wearable Wireless sensor networks (CROW 2 ) [START_REF] Hamida | Wearable Body-to-Body Networks for Critical and Rescue Operations -The CROW 2 Project[END_REF]. The main objective of the project is to provide ubiquitous wireless communication and monitoring systems for emergency networks in disaster relief. It is anticipated that in emergency situation, the existing infrastructure network may be damaged (due to disaster itself), out-of-range or over saturated. In this realm, to extend the end-to-end network connectivity, WBANs coordinators can wirelessly interconnect the on-body sensors to external network infrastructures, by exploiting cooperative and multihop body-to-body or beyond-WBAN communications. Consequently, the on-field rescue members can be connected with external command center in midst of rescue operation.

Figure 1, illustrates the deployment and coordination of rescue teams in disaster environment which represent our rescue and emergency management application scenario. It is considered that each field team member is equipped with a set of wearable wireless sensor devices to monitor: i) its vital signs (e.g. heartbeats, blood pressure, stress level, etc.); ii) its motion and body posture (e.g. orientation, acceleration, heading, position, etc.); and iii) the surrounding environment (e.g. temperature, toxic gases, fire, light intensity, etc.). It is envisioned that these wearable WSNs will be heterogeneous in terms of supported data rates, sensors types, supported Quality of Services (QoS), communication ranges hardware capabilities, mobility and propagation conditions. Furthermore, these wearable WSNs can be deployed in harsh environments, thus requiring effective communication, coordination and monitoring capabilities.

B. Mobility Modeling and Disaster Scenario

Evaluating and simulating a routing protocol in a specific context, strongly depends on the accurate mobility models. Mobility models metrics are classified as follows [START_REF] Kumar | Mobility Metrics Based Classification & Analysis of Mobility Model for Tactical Network[END_REF]:

-Random based: no dependencies or restriction.

-Temporal dependencies: current movements depend on the past ones. -Spatial dependencies: movements depend on the movements of the surrounding units. -Geographical restrictions: geographic restriction on the movements. -Hybrid structure: Integration of two or more models.

An hybrid structured disaster area model designed by Aschenbruck et al. in [START_REF] Aschenbruck | Modelling mobility in disaster area scenarios[END_REF] divides the catastrophe area into four different sub-areas. First, incident site contains one or more incident area(s) that represent(s) the exact incident location (e.g., coordinates of aircraft crash, coordinates of a fire trigger, etc.). Second, casualties treatment area contains one or more patients waiting for treatment area and casualties clearing station. Then, the transport zone with ambulances and eventually rescue helicopter(s). The last sub-area is the hospital zone, which is often not represented, because size constraints, so arriving to the transport zone, casualties are considered cleared and safe. The last and important component in this model is the location of the command center responsible for conducting the rescue and emergency operations.

In this paper we investigate a disaster scenario (fire triggering as a case study) in the "Landmark" shopping mall in the State of Qatar as depicted in Figure 2. The mobility model used is generated by the BonnMotion tool. Let us first consider some logistic aspects for the mobility scenario. We consider that the incident is caused by a fire in two opposite sides in the mall (Figure 2). Then rescuers are called to react along with firefighters and medical teams. Firefighters are divided into 3 groups of vehicles with 26 firefighters in each group. Medical emergency teams that probably could reach the mall just after the incident, are consisting of 6 ambulances with 5 medical staff in each ambulance (30 personal in total). Further, police officers and civil defense personals are also considered to support the emergency teams (we have considered 18, to have a total of 100 rescuers). We consider all the rescuers as moving nodes and sending their gathered information to one main sink node placed at the main-gate of the mall (shown as in Figure 2). Data sent could be rescuers and/or victims health status, ambient rescuing conditions, special medical requests, etc., based-on simple data, voice, images and/or video. We provide, area perimeter coordinates, obstacles coordinates, number of nodes (i.e., personal in our case) in each incident area, transported nodes in each group of nodes, etc., as an input parameters to BonnMotion. As an output, we obtain a mobility trace file containing the movement of all the nodes during the observation time. The generated mobility trace file is used as an input for the comparative evaluation of the routing protocols (as illustrated in Figure 4) discussed in the following section.

C. Routing approaches evaluated 1) Proactive: OLSR version 2[8]

OLSRv2 is a proactive link state routing protocol that uses periodic local and global signaling for neighbor/link discovery and link state diffusion. Instead of OLSR (i.e., first version), which uses four messages types, OLSRv2 require only two messages types: HELLO and TC (internal and external with only one parser for all messages types). It uses also addresses compression with IPv6 support. Three main steps are followed in this routing approach: neighborhood discovery, Multi-Point Relay (MPR) selection, routing table calculation and maintenance.

2) Reactive: AODV version 2 [9]

In AODV, a node does not perform route discovery or maintenance until it is needed. A route discovery in AODV is initiated by the source node (S) through the broadcast of a specific route request (RREQ) to all its first hop neighbors. This route request is transferred by broadcasting until it reaches the destination. The RREQ records only the last_hop and the destination address.

AODV uses also the source and destination sequence number to detect and identify routes freshness. The RReq_ID is used to avoid processing duplicated requests (the pair Source_ID and RReq_ID is unique). A Time-To-Live (TTL) is also used by AODV to prevent an indefinite routing of a request. The Route Reply (RREP) in AODV follows the same route saved by the nodes while transferring the RREQ. This information will be deleted after a timer is expired if a RREP is not received. Besides, AODVv2 records the entire traveled path into the RREQ packet header. It removes also the unnecessary RREP and Hello messages. AODVv2 is energy aware and selects routes based on the low energy index.

3) Gradient based: Directed Diffusion[10]

Directed Diffusion (DD) is a data-centric routing protocol designed for WSN. With respect to WSN main requirements, DD is energy efficient, scalable and robust [START_REF] Singh | Routing Protocols in Wireless Sensor Networks -A Survey[END_REF]. The routing mechanism in DD follows three steps. At startup, the sink node requests to gather data from one or more nodes. So, the sink node broadcasts the requests called interests towards the concerned node(s). Then, the routes (or gradients) are set up by selecting non-redundant route towards the sink node. This process starts with a low data rate specified by the sink node; afterwards, this data rate is reinforced by the sink node itself through one selected node. The reinforcement then is propagated throughout all the nodes. [START_REF] Karp | GPSR: Greedy Perimeter Stateless Routing for Wireless Networks[END_REF] GPSR uses the nodes location and the wireless connectivity. It uses two forwarding techniques: greedy forwarding and perimeter forwarding. In greedy forwarding, packets from source node to destination are forwarded throughout the geographically closest next hop towards the destination. When a greedy forwarding is impossible, the protocol routes the packets in the surrounding perimeter of the destination. GPSR returns to the greedy forwarding early when a local maxima (local parameter) is reached. GPSR maintains only its location and locations of neighbors.

4) Geographic based: GPSR

IV. PERFORMANCE EVALUATION

A. Simulations setup

In this section, proactive (i.e., OLSRv2), reactive (i.e., AODV), geographic based (i.e., GPSR) and gradient based (i.e., DD) routing protocols are evaluated using a realistic disaster mobility model as explained in Section III.B. In addition, we consider various communication technologies including WiFi (i.e., IEEE 802.11 standard), WBAN (i.e., IEEE 802.15.6 standard) and WSN (i.e., IEEE 802.15.4 standard). These wireless technologies (i.e., MAC and PHY layers), are selected especially to analyze and evaluate interbody communication (i.e., realized through WBAN coordinator). Subsequently, each of these technologies is implemented using above selected routing protocols for comprehensive evaluation. We are using an event-driven, packet-oriented network simulator called WSNet (version 3.0), for systems level simulations. The simulations are executed based on a realistic mobility model for 100s. We considered 10 iterations for every simulation and the 95% confidence intervals are provided. All the parameters at each layer are configured though an XML configuration file, where we vary the routing protocols for each technology. Concerning the performance metrics, we consider the Packet Reception Rate (PRR), Communication Delay and Energy Consumption as the main metrics. The complete simulation process as shown in Figure 5 is a set of operations iterated for 10 times to converge to the realistic behavior of the evaluated routing protocols. At first, the mobility generation tool BonnMotion with specific parameters of the studied disaster scenario generates Mobility trace file. The output file is then converted into a proper format before being parsed by the simulator WSNet. A routing protocol is then selected with a specific communication technology and an initial payload. These parameters are entered through the "XML" configuration file.

1) Application and Routing Layers

At the application layer, we consider 99 moving nodes (i.e. WBANs coordinators) inside the shopping mall sending data packets to one sink node (i.e. command center), here node 0, which is placed at the main gate of the mall. Distance between nodes, movements, directions and speed are calculated according to the mobility model. A Constant Bitrate Rate (CBR) application is used to generate the traffic (with one packet/s), with available data payload ranging from 2 bytes to 256 bytes. At the network layer, a routing protocol detailed in Section III-C is selected as illustrated in the simulation process (cf. Figure 5). The routing layer receives the packets from the application layer, depending on the routing protocol; all the configuration parameters are equally affected. Each routing approach will be evaluated with individual technology detailed in Table 1. Finally, IEEE 802.15.6 (for WBAN) CSMA/CA MAC protocol with immediate acknowledgment policy is implemented. We have exploited the higher emergency level feature of this standard (i.e. 2) for the transmitted packets. The maximum back-off is set as 5 and re-transmission limit is 3. Along with the selected MAC layers, corresponding modulation schemes, physical configuration parameters including transmit power levels and corresponding current consumptions (of the widely used radio transceivers i.e., cc2420 for WSN/WBAN, and cc3100 for WiFi) are detailed for various states in Table 1.

2) MAC and Physical Layers

B. Simulation Results

In this section, the performance of OLSRv2, AODVv2, GPSR and DD are investigated with the WiFi, WSN and WBAN technologies (i.e. IEEE 802.11, IEEE 802.15.4 and IEEE 802.15.6 standards). Parameters settings were configured for the context of PSN.

1) Packet Reception Rate (PRR)

The results of average PRR for the four selected protocols and three technologies are shown in Figure 4. Generally, the evaluated routing protocols perform much better using WiFi in comparison to the others technologies, overall it achieves more than 92% of PRR. In particular, with low payloads (i.e. 2 and 16 bytes), only DD PRR is below 96% whereas, OLSRv2, AODVv2 and GPSR are all able to achieve above 99% PRR. Starting from 64 bytes and higher payload, DD performance also starts improving to exceed others protocol performance as can be seen in Figure 4-a. GPSR and OLSRv2 showed similar performance. AODVv2 has best performance with WiFi at 128 bytes payload, and performs similar as DD with 256 bytes payload. Finally, GPSR shows slightly lower performance with more than 128 bytes payload. This is due to the perimeter forwarding technique which may occur several times due to the obstacles located in the mobility model.

For the case of WSN IEEE 802. 15.4 (ZigBee), in general, all the protocols are under-performed as shown in Figure 4-b. As the best case, 33% of average PRR was achieved using GPSR. Even with the lowest payloads, the performance remained very low. OLSRv2 was not able to deliver any packet at all with the various payload values. WSN is a short-range communication technology, with high mobility nodes such as defined in the mobility model and according to the nodes density in the shopping mall; evaluated routing protocols are unable to perform well with WSN. Additionally, by using CSMA/CA MAC without any acknowledgment policy the performance further degrades.

WBAN (i.e., IEEE 802. 15.6) is mainly an intra-BAN communication technology, but recently, research trends are tend to evaluate this standard in inter-BAN context [START_REF] Alam | Interference Mitigation and Coexistence Strategies in IEEE 802.15.6 based Wearable Body-to-Body Networks[END_REF]. For this reason, we are investigating IEEE 802.15.6 standard to achieve the potential limits studied in [START_REF] Alam | Device-to-Device Communication in Wearable Wireless Networks[END_REF]. In WBAN generally, most of the protocols perform much better in comparison to WSN technology. In particular, GPSR outperforms the other routing approaches again, it achieves up to 88% PRR under low payloads (2 and 16 bytes). However, by increasing the payload, GPSR starts to gradually degrade in performance same as the case in other technologies however,GPSR remains the best protocol. OLSRv2 has the worst performance, whereas both DD and AODVv2 also reach below 50% average PRR with 256 bytes. As we analyzed the performance given by combining one of the evaluated routing protocols with WBAN IEEE 802.15.6, GPSR meets the disaster scenario requirements with low payload, the rest of protocols are inconclusive. It is necessary to notice that while considering WBAN technology, the low data rate is a limitation in terms of image and video transfer.

Finally, the evaluated routing protocols used with WiFi are convincingly better than the two counterparts in terms of average PRR. Only, GPSR performed well with WBAN.

2) Latency:

We considered latency as the average packet delay between the source node and the final destination over a multi-hop BBN. Generally, the results of the delay are inter-related with PRR, if PRR is higher then, delay will be lower. Focusing onto WiFi technology, with low payloads (i.e. 2 and 16 bytes), all routing protocols delay is below 80ms which satisfy our application context. Figure 6-a shows an exponential increase in delay for AODVv2 and GPSR starting from16 bytes of payload, while OLSRv2 delay remains slightly lower than 80ms until 16 bytes. DD is the most efficient and has almost negligible delay among all protocols and therefore is considered as the most effective protocol in terms of delay using WiFi technology. In comparison to the other evaluated protocols, DD has very low calculation for data routing. While, AODVv2 with an on-demand routes lookup technique and GPSR which also bases its routing table on geographic locations calculations which require more time to route calculation before data transfer which adds an additional significant delay. Figure 6-b, shows the average delay for WSN. It is clear that comparatively, it is extremely (i.e., 100 times) better than WiFi. AODVv2, DD and GPSR all perform very well even at higher payloads sizes. Exceptionally, OLSRv2 performance is the worst one since it was unable to transmit any packet and had zero average PRR, therefore the delay is irrelevant for it. Although, DD shows slightly more delay comparing to the other two protocols, but overall it is below 100ms as well.

With WBAN technology, in addition to the respectable PRR recorded by GPSR with low payload, all the routing approaches perform much better in term of delay with WBAN technology than with the rest of the communication technologies. Results in Figure 6-c, show that delays are very low in comparison to the delays recorded with WiFi and WSN technologies.

Finally, concerning the delay, WBAN and WSN outperformed WiFi in most of the protocols. Only in case of GPSR with WBAN, the results are comparable and it is the most effective protocol for optimized delay performance.

3) Energy Consumption:

The energy consumption for each transmitted packet is calculated as follows, Epacket = Tpacket × 3Volts × ImA where, Tpacket is the duration in ms which is based on the effective packet length (including all the PHY and MAC headers [START_REF] Alam | Device-to-Device Communication in Wearable Wireless Networks[END_REF]). The current consumption values for two different considered radio transceivers are mentioned in Table 1.For WiFi, approximately linear increase in energy consumption is observed with an increase in payload size for all the protocols as shown in Figure 7-a. There is hardly any difference between the protocols for 2 and 16 bytes of payload. However, for higher payloads it is notable that AODVv2 consumes the highest energy, whereas GPSR is the most energy efficient protocol.

In general, all routing protocols have performed much better with WSN for energy efficiency. By increasing the payloads, the energy consumption decreases for AODVv2, DD and GPSR. It seems that OLSRv2 performed very well in terms of energy consumption, but since it does not able to transmit any data packet, this reflection is not for a successful transmission and hence irrelevant In addition, the behavior of OLSRv2 is a reflection of continuous routes maintenance operations, which increases energy consumption even without packet receptions. Despite of that OLSRv2 improves OLSR features, though, it still not scalable and destined for small networks with small data traffic. Using WBAN, routing protocols, except AODVv2 and GPSR have similar pattern, i.e., energy consumption slightly decrease with the increase in payload. OLSRv2 performs better than the other routing protocols. A slight difference of energy consumption is noticed between the evaluated routing protocols. Finally, in terms of energy efficiency, WBAN communication technology is most suitable with the evaluated routing protocols.

To summarize, definitely WiFi is the most relevant for the reliable communication but at an expense of significant

Fig. 1

 1 Fig. 1 On-Body and Body-to-Body Communications.

Fig. 2

 2 Fig. 2 Overview of the Disaster Scenario in the Landmark Shopping Mall

Fig. 3

 3 Fig. 3 Disaster Area Nodes Locations, Areas and Obstacles.

Fig. 4 .

 4 Fig. 4. Simulation Methodology.

  At the MAC layer, we are employing unified distributed CSMA/CA protocol for the three wireless technologies. It includes DCF IEEE 802.11 (for Wifi) which employs a CSMA/CA with binary exponential back-off algorithm. It uses CTS/RTS control signals for better reliability. IEEE 802.15.4based CSMA/CA (for WSN) is implemented with maximum back-off exponent set as 3; maximum back-off is 5 without any re-transmission.

Fig. 1 .

 1 Fig. 1. Average Packet Reception Rate for AODVv2, OLSRv2, DD and GPSR using the three WiFi, WSN and WBAN Technologies.

Fig. 2 .

 2 Fig. 2. Average Communication Delay for AODVv2, OLSRv2, DD and GPSR using the three WiFi, WSN and WBAN Technologies.

Fig. 3 .

 3 Fig. 3. Average Energy Consumption for AODVv2, OLSRv2, DD and GPSR using the three WiFi, WSN and WBAN Technologies.

  protocols are selected respectively. In addition, multi-standard technologies including IEEE 802.11 (WiFi), Zigbee / IEEE 802.15.4, and IEEE 802.15.6 (WBAN) are investigated for the Body-to-Body Network coordinator. Finally extensive network simulations are conducted and the results of average successful Packet Reception Rate (PRR), Packet Delay and Energy Consumption are presented.

TABLE 1 .

 1

		LIST OF SIMULATIONS PARAMETERS AND
		CORRESPONDING VALUES
				Battery Parameters
	Standards	MAC Layer	PHY Layer		(mA)
				TX	RX	IDLE
			Modulation
	WiFi IEEE 802.11	CSMA/CA ACK DCF with	BPSK, Sensitivity Power = 0dBm, = -92dBM, TX	160	53	0.69
			2.4GHz
			Modulation O-
	WSN IEEE 802.15.4	CSMA/CA ACK without	QPSK, Sensitivity Power = 0dBm, = -85dBM, TX	17.4	19.7	0.9
			2.4GHz
			Modulation
	WBAN IEEE 802.15.6	CSMA/CA with ACK	DQPSK, Power = 0dBm, Sensitivity = -85dBM, TX	17.4	19.7	0.9
			2.4GHz

ACKNOWLEDGMENT

This publication was made possible by NPRP grant # [6-1508-2-616] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

increase in delay. In this aspect WBAN technology is most effective and by using GPSR routing protocol it can be a considerable option for BBN. GPSR with both WiFi and WBAN is able to achieve high packet reception and consumes relatively much lower energy with low delay. Referring to our network topology (i.e., converge cast) and mobility scenario, GPSR is one of the most favorable protocol as reflected in Table 2. Finally, for small-scale rescue and critical operations using BBN, both WiFi and WBAN can be considered based on the given constraint, either PRR or delay.

V. CONCLUSION

Although in recent years, broadband networks such as LTE has emerged as viable option for PSN, but there is always a potential risk that such infrastructure-based network could be damaged or oversaturated during the disaster and emergency situations. In this context, Wearable Wireless Sensor Networks could not only be served as an add-on to existing PSN but can also help to monitor the critical health and environment status during the rescue and evacuation process. In this paper, a particular emphasis is given to the emerging body-to-body communication whilst evaluating best wireless technologies and routing strategies under realistic mobility scenario for public safety and disaster relief operations. Three technologies (i.e., WiFi IEEE 802.11, WSN IEEE 802.15.4 and WBAN IEEE 802.15.6) and four different class of routing protocols are considered including mobile Ad hoc (i.e., OLSRv2 and AODVv2), data centric (directed diffusion) and geographical location-based (GPSR). It is concluded that WiFi is the best technology for both packet reception ratio and energy efficiency performance metric. Whereas, as far as the packets delay is concerned, WBAN is the most effective technology. Among the protocols, by assuming that we have location information, then GPSR performed the best in comparison to all other protocols using WiFi IEEE 802.11. The only exception is with delay results, where DD outperformed all other protocols. If location information is not available, then DD especially with WBAN IEEE 802.15.6 can be considered as a favorable choice. However, it is important to note that WBAN has maximum payload limit of 256 bytes which limits it to the transmission of real-time audio or video.


Ben Dhafer
email: dhafera@qmic.com

Arbia

Muhammad Mahtab Alam

Rabah Attia
email: rabah.attia@enit.rnu.tn

Elyes Ben Hamida
email: elyesb@qmic.com

Data Dissemination Strategies for Emerging Wireless Body-to-Body Networks based Internet of Humans

Keywords: Wireless Body-to-Body Networks (WBBN), Internet of Humans (IoH), data dissemination, clustered, distributed, IEEE 802.15.6

With the recent advent of Internet of Humans (IoH), wireless body-to-body networks (WBBNs) are emerging as the fundamental part of this new paradigm. In particular with reference to newly emerging applications, the research trends on data routing and dissemination strategies have gained a great interest in WBBN. In this paper, we present the performance evaluation of the clustered and distributed data dissemination approaches in tactical WBBN. We used a realistic radio-link and biomechanical mobility model for on-body motions, and group mobility model for WBBN to effectively realize rescue and emergency management application scenario. In this regard, we are using the newly proposed IEEE 802.15.6 standard targeted for body area networks. Extensive (IEEE 802.15.6 standard compliance) network level, packet oriented simulations are conducted in WSNet simulator. During the simulations, various payloads, frequencies (narrow-band) and modulation techniques are exploited. We based our performance evaluation on relevant metrics according to the operational requirements for tactical networks such as packet reception ratio, latency, energy consumption and hop count. The results showed a trade-offs between clustered-based and distributed-based dissemination approaches. With regards to packet delay, distributed approach provided the best performance. However, in terms of average packet reception ratio (PRR), clustered-based approach achieves up to 97% reception and remained the best strategy. Whereas, the results of the hop count and energy consumption are almost comparable in both schemes.

I. INTRODUCTION

The internet of things (IoT) is emerging as a key enabling technology for the next generation of inter-connected world. It is based on the concept of unique addressable objects, which can virtually and seamlessly connect to each other any time and everywhere [START_REF] Ashton | That 'Internet of Things' Thing[END_REF]. These objects are equipped with various types of sensors to track useful information about these objects. In addition, these objects can sense the environment and communicate autonomously without human interventions [START_REF] Aggarwal | THE INTERNET OF THINGS: A SURVEY FROM THE DATA-CENTRIC PERSPECTIVE[END_REF]. Further, the data generated by these objects can be very useful to provide innovative solutions for telecommunication, health-care, energy and many other sectors.

The concepts of pervasive and ubiquitous computing are related to the IoT, in the sense that all of these paradigms are enabled by large-scale embedded sensor devices [START_REF] Aggarwal | THE INTERNET OF THINGS: A SURVEY FROM THE DATA-CENTRIC PERSPECTIVE[END_REF]. Recently, this concept is leading towards connected human beings also known as Internet of Humans (IoH) [START_REF] Bloom | Trends: The Internet of Humans (not Things)-Sensors[END_REF], where the personal and wearable devices are always connected enabling seamlessly humans connectivity.

We are interested to exploit the concept of IoH in the context of emergency management especially rescue and critical operations where multiple rescue members are interconnected to realize effective communication and coordination for timely evacuation. In such scenarios, often the existing infrastructure is either itself damaged or oversaturated. It is envisioned that, wearable wireless sensor networks can aid for not only proper evacuation but also monitor the physiological signals of the team members [START_REF] Ben Hamida | Wearable Body-to-Body Networks for Critical and Rescue Operations -The CROW² Project[END_REF]. One of the fundamental questions for effectively realizing not only on-body but also body-to-body communications is that, which architecture (from the point-of-view of data dissemination) could be most suited? Often the operating conditions and environment are very harsh, dynamic mobility, time varying radio channel; wireless fading and body shadowing adds further hurdles for reliable wireless communication.

To address above mentioned problem, in this paper we present a reliability-aware comparative evaluation of the data disseminated strategies using wearable wireless on-body (WBAN) and body-to-body networks (WBBN). In particular, fully distributed (i.e., any sensor on one body can communicate to any sensor on an other body) and clustered-based dissemination approach (i.e., on-body sensor can only communicate with their own on body coordinator, which effectively being resource rich device enables body-to-body communication) are evaluated. A packet-oriented network simulator is developed to analyze the varying behavior of the two strategies under IEEE 802.15.6 standard compliance. The simulation platform includes enhanced IEEE 802.15.6 proposed channel models by adding space and time variations to have accurate pathloss model. To mimic the application scenario, dynamic mobility patterns are generated based on walking, running, siting and standing movements. Biomechanical mobility model provides deterministic channel variations for WBAN, whereas group mobility is used for WBBN channel. In addition, carrier sense multiple access collision avoidance (CSMA/CA) medium access control protocol and dynamic MANET on demand (DYMO) routing protocol completes the WBAN and WBBN system. Finally, several performance metrics are considered for comparing the two data disseminating approaches. It includes packet reception ratio (PRR), packet delay, energy consumption and hop counts results.

Rest of the paper is organized as follows; first, a brief application scenario context and motivations are presented in Sec. II, followed by related works in Sec. III. Data dissemination strategies are explained in Sec. IV, and finally simulation results are presented in Sec. V, followed by a conclusion.

II. CONTEXT AND MOTIVATIONS

This paper presents an ongoing research of the project Critical and Rescue Operations using Wearable Wireless sensor networks (CROW 2 ) [START_REF] Ben Hamida | Wearable Body-to-Body Networks for Critical and Rescue Operations -The CROW² Project[END_REF]. The main objective of the project is to provide ubiquitous wireless communication and monitoring systems for emergency networks in disaster relief. It is anticipated that in emergency, the existing infrastructure network may be damaged (due to disaster itself), out-of-range or over saturated. In this realm, to extend the end-to-end network connectivity, WBANs coordinators can wirelessly interconnect the on-body sensors to external network infrastructures, by exploiting cooperative and multi-hop bodyto-body or beyond-WBAN communications. Consequently, the tactical teams led by the commander, should forward their information among each other to reach the team leader, which is connected to a command center or Internet.

Tactical operations, with respect to the different functions, are: military operations, law enforcement, emergency medical and health services, border security, environment protection, fire-fighting, search & rescue and emergency crisis. Other classification according to occurrence, space and time are:

-Routine or day-to-day operations: EMS (Emergency Medical Services: e.g. heart attack), fire, law enforcement. -Multi-discipline, multi-jurisdiction: for example, explosion in chemical or nuclear plant.

Planning, triggering and conducting tactical operations depends on varied circumstances such as type of threats (e.g. natural disaster, war, etc.), location (e.g. land: rural, urban, mountain; sea: ocean, coastline; air; underground), weather conditions, etc. These circumstances are closely related to the mission requirements and the effective communication is one of the relevant challenges in tactical operations. Real-time information (i.e. text, images, videos) transfer is needed before, during and even after the operation. This information is required in different tiers. For example, involved personal are concerned at the first level, then, the on-the-field teams' leaders and finally the command center. Hence, communication network must cover all these cited commanding and executing levels. However, it is anticipated that, during and after a crisis or a disaster, existing networking infrastructures can be either completely damaged or oversaturated. Therefore, a tactical network should be instantly deployable to connect the crisis area (i.e. engaged staff on the field) to a distant command center and internet. In this framework, we identify two types of communication regarding the engaged personals: (i) On-Body communications: data flow coming from on-body nodes (i.e. sensors, GPS, etc.) going to the coordinator (i.e. sink node) and vice-versa. (ii) Body-to-Body communications: data flow going from Body to another Body. Our interest in this work is to consider two dissimilar data dissemination strategies while routing data from simple On-Body nodes to the team leader's coordinator which is connected to the large network and eventually Internet. We consider cluster-based and distributed dissemination strategies which are detailed in Section IV.

III. RELATED WORKS

In the literature, diverse data dissemination protocols have been proposed for Wireless Sensor Networks (WSNs) [START_REF] Hamida | Analytical Evaluation of Virtual Infrastructures for Data Dissemination in Wireless Sensor Networks with Mobile Sink[END_REF]. WSN is composed by a certain number of sensor devices distributed on an area of interest. Sensor devices are severely constrained in terms of memory, computation capabilities, wireless range and battery power. Sensors (i.e., source-node) sense the environment physical measurements and send them towards a sink (i.e., destination-node). The sensing process could be either triggered by the source-node (i.e., through periodic sensing), or depending on the events (i.e., Event driven) or requested by the sink (i.e., Query Based). Data dissemination strategies for WSN are adopted recently for the Wireless Body-to-Body Networks (WBBNs) with major restrictions [6] [7].

Based on different strategies, sensed data is disseminated towards the sink node. These strategies are classified with respect to: (i) type of disseminated data, (ii) depending on the destination(s), where both uses the concept of virtual infrastructure [START_REF] Hamida | Strategies for data dissemination to mobile sinks in wireless sensor networks[END_REF]. Further for (i), there are three categories: data dissemination (where the sensed measurements are disseminated), meta-data dissemination (where the sensed measurements are stored locally and a meta-data is disseminated), and Sink location dissemination (where the locations are stored into nodes information, and then data is disseminated depending on events). For (ii), dissemination strategies are categorized as: single node (disseminated information is stored in one node), out-of-group nodes (the information is disseminated out of a defined group of nodes), a set of nodes (information is depicted into a set of nodes). Most known data dissemination protocols in WSNs are Directed Diffusion (DD), Geographic Hash Table (GHT), Two-Tier Data Dissemination (TTDD), Railroad, Locators, etc.

It is important to note that, in Emergency and Critical networks, WSN could be a part of a WBAN. A WBAN is a set of miniaturized devices (i.e. sensors, GPS, RFid tags/readers) wirelessly interconnected and attached (or implanted) into body (human, animal, etc.). All these devices are connected with a sink node (i.e. coordinator). Despite the fact that some of the above WSNs protocols were evaluated in a single WBAN context, WBAN still have considerable particularities against WSN [START_REF] He | Lightweight and Confidential Data Discovery and Dissemination for Wireless Body Area Networks[END_REF]. First, mobility in WBAN is more important than WSN (i.e. WSN are considered stationary) therefore, link failure consideration among devices is relevant. Second, in critical operations, devices battery lifetime used in WBAN, is not a crucial requirement (during operations batteries could be replaced or recharged) instead of scattered sensors (in case of WSN) where battery must operate for long time (few years). These particularities impact requirements of data dissemination protocols. Classic data dissemination strategies within single WBAN were based on links lifetime. However, recent dissemination mechanisms tend to be more opportunistic and posture-aware due to the high WBAN dynamic variations especially in tactical operations. Opportunistic dissemination techniques prove energy preservation and network lifetime increase [START_REF] Ben Hamida | Short-term link quality estimation for Opportunistic and Mobility Aware Routing in wearable body sensors networks[END_REF].

Moreover, probabilistic routing protocols use the historical link quality estimation and the inertial sensor data to make the best relaying decision.

Further researches consist on evaluating Ad hoc routing protocols in a scale of single WBAN. Asogwa et al. in [START_REF] Asogwa | Experimental Analysis of AODV, DSR and DSDV Protocols Based on Wireless Body Area Network[END_REF] evaluated Ad hoc On-demand Distance Vector (i.e. AODV), Dynamic Source Routing (i.e. DSR) and Destination-Sequenced Distance-Vector (i.e. DSDV) routing techniques. The obtained results showed that AODV and DSR have good reliability and performed much better in terms of energy efficiency. Likewise, according to Murthy et al. [START_REF] Murthy | Investigations on the Routing Protocols for Wireless Body Area Networks[END_REF], AODV is the most efficient routing protocol for intra-BAN in terms of energy efficiency and QoS.

At extended level, Ad hoc routing techniques were also used to cover Body-to-Body communications [START_REF] Bohannan | QoS Enhancement and Performance Evaluation of ad-hoc routing protocols for rural public safety[END_REF]. Even more recent, an interesting layer-2 (i.e., MAC Layer) data forwarding strategy proposed by Kolios et al. with reference to a specific Emergency Ad hoc Network (i.e., ERN) [START_REF] Kolios | Qualifying explore and exploit for efficient data dissemination in emergency adhoc networks[END_REF]. Explore and Exploit (i.e., EnE) data dissemination strategy is based on new topology-related metric, Local Centrality (i.e., LC). LC computes a node importance rank that classifies the nodes based on their topological properties. Alert Messages (i.e., AM) will be disseminated through the nodes with highest LC. Indeed, no routing calculations, building and maintenance is needed, thus, no network protocol is implemented. LC information is stored into the layer-2 headers. According to the authors, EnE requires trivial communication overhead and includes smart forwarders selection.

To conclude, existing data dissemination approaches in WBBN are primarily based on the operational context (i.e., use cases: critical, emergency, delay-tolerant, etc.), next, on the type of the data to disseminate (i.e., location, data, metadata). MANET are evidently evaluated in WBBN, however, data dissemination strategies depending on operational requirements are not yet investigated. An important operational requirement in tactical operations consist on; the team leader has to be able to receive, follow and feedback the operation commanding center of all the information provided by his team. For this, clustered or distributed approaches are investigated in the following section.

IV. DATA DISSEMINATION STRATEGIES

During the last decade, most of the studies were focused on to the feasibility of the MANETs in tactical networks. This tendency is justified by the fact that the tactical operations happens in rural and populated areas where networking infrastructures are either absent or shattered, which comply with the specifications of the tactical operations. Furthermore, due to its flexibility to topology changes and its multi-hop routing aspect, Mobile Ad hoc networks are an interesting candidate to be investigated in the tactical WBANs. In this regard, one of the experimental works evaluated MANETs in rescue and critical operations [START_REF] Richard | Ad Hoc Routing for Rural Public Safety[END_REF]. However, we introduce the following network architecture which is based on the principle that each team member has to send all the information as One-Way-Converge-Cast traffic towards the unique team leader.

1) Network Architecture

As mentioned in Section III, proposed approaches for data dissemination were either classified by type of the disseminated information or based on nodes status (energy, connectivity, etc.). The objective of this paper is to evaluate the performance of two data dissemination strategies (clustered and distributed) with specific simulation setup detailed in the next section. The disseminated information towards the team leader could reach it in two different ways:

-Clustered Data Dissemination (CDD): From one-WBAN nodes to their coordinator (i.e., embedded on the same WBAN), and then from that coordinator to the adjacent coordinators until reaching the team leader's coordinator (i.e., a coordinator is a sink node responsible for gathering data from the other On-Body nodes, it is characterized by powerful capabilities comparing to the other nodes). Thus, each WBAN could operate in a single frequency. Fig. 1a shows in the green color, a communication link between the sensor nodes and a coordinating node during the data dissemination process, whereas, in red color, the Coordinator-to-Coordinator data disseminations are highlighted.

-Distributed Data Dissemination (DDD): From one-BAN nodes to the any reachable adjacent nodes (simple node or coordinator), until reaching the team leader's coordinator. Consequently, all nodes need to share same frequency which could raise an interference issue. Fig. 1b depicts the distributed data dissemination, any node could send its data to any node, however, the final destination is always the coordinator of the Team Leader.

2) Routing Protocol

Without loss of generality, and as discussed in Section III, AODV was found as the most efficient routing protocol in intra-BAN communications. However, despite the latest trends to evaluate Ad hoc routing techniques in critical and emergency environments, few studies have been conducted to evaluate these strategies with realistic mobility model. In one of our recent work, we evaluated four routing protocols (i.e., proactive, reactive, gradient-based and geographical-based) in emergency and critical context. So, we found that AODV version 2 showed a considerable behavior in this context. In contrast, this work does not assess routing protocols but the way data is disseminated in intra and inter-WBANs. We consider AODV version 2 as the routing protocol in our following investigations.

AODV version 2 (i.e., DYMO) [START_REF] Perkins | Dynamic MANET On-demand (AODVv2) Routing -draft-ietf-manetaodvv2-10[END_REF] routing protocol is considered as an enhancement of AODV, with recourse to some of the features of DSR. Indeed, DYMO uses 'path accumulation' from DSR and removes unnecessary Route Reply (RREP), precursor lists and Hello messages (aka Route exploration messages). From AODV, DYMO keeps sequence number, hop count and RERR. DYMO has two main operations: route discovery and route management. In DYMO routing protocol, the route discovery process starts with the RREQ (if no route to destination exists in the source routing table), then, each time the RREQ is forwarded throughout the network, forwarding node will attach its address to the RREQ message. Once the destination reached, The RREP will be sent in unicast to the source node following the accumulated path. DYMO is an energy efficient protocol, then if one node has low energy it does not participate in the route discovery process since it may be disconnected until the RREP is sent back. Obviously, while sending data, if the link is break or the destination node is no more available, the intermediate node multicasts a RERR to only concerned nodes by the failed links. Upon the reception of the RERR, the routing table entry containing the unavailable node will be deleted. Hereafter, a new route discovery will be initiated when a destination is needed.

V. SIMULATION RESULTS

In this section, we investigate the performance behavior of the previously discussed data dissemination strategies and routing protocol under realistic WBBN channel and mobility modeling. The detailed simulation parameters and models are firstly described, followed by the obtained simulation results in terms of packet reception ratio, energy consumption, communication delays and routes hop count.

1) Radio Link and Mobility Modeling

Accurate radio link and mobility modeling is key to the system performance of wearable tactical network introduced in previous section. Our radio-link modeling metric is based on SINR (signal-to-interference-noise-ratio), which takes into account the mutual interference from multiple WBANs [START_REF] Alam | Interference Mitigation and Coexistence Strategies in IEEE 802.15.6 based Wearable Body-to-Body Networks[END_REF]. This metric rely on accurate path loss calculations using enhanced IEEE 802.15.6 channels models [START_REF] Alam | Towards Accurate Mobility and Radio Link Modeling for IEEE 802.15.6 Wearable Body Sensor Networks[END_REF]. Then, bit error rate is calculated based on the specific modulation schemes (i.e., DQPSK and DBPSK) proposed in the IEEE 802.15.6 standard, followed by the evaluation of packet error rate (PER) [START_REF] Alam | Interference Mitigation and Coexistence Strategies in IEEE 802.15.6 based Wearable Body-to-Body Networks[END_REF].

In WBANs there are different mobility patterns depending upon the posture positions during sitting, standing, walking, running swimming etc., scenarios. In addition, body shadowing, orientation and rotations make the radio-link consistently time-varying. Our modeling methodology is based on real-time mobility traces from the motion captured system which provides diverse mobility patterns such as walking, sitting, standing and running. These mobility patterns coincide with our application scenario as explained in Sec. II, which are imported in a packet-oriented network simulator (called WSNet) for complete system design and performance analysis. Further, we have developed bio-mechanical models (for on-body) communication and group mobility model (for body-to-body) communications which reflect and satisfy our application context. The detailed steps of the biomechanical modeling and transformation are explained in [START_REF] Alam | Towards Accurate Mobility and Radio Link Modeling for IEEE 802.15.6 Wearable Body Sensor Networks[END_REF]. In this paper we have considered three levels of hierarchy as shown in Fig. 2. At the top level, there are 12 bodies (WBANs), then, 3 WBANs form a small group (for inter-WBAN mobility) and finally each body consists of five on-body nodes. Concerning the separation distance between these WBANs, the WBANs inside a small WBANs group are separated by 8 meters, whereas, 20 meters separation is considered between the groups. Five on-body nodes are placed as; head (node 1), right shoulder (node 2), right wrist (node 3), stomach (node 0), and right ankle (node 4).

B. Simulations Setup

We consider the reference scenario of Section IV, in which twelve wearable body area networks (WBANs) are evolving in a tactical group formation. The bodies' mobility patterns include sitting, standing, walking (i.e., 0.5m/s) and running (i.e., 3m/s). We consider two different nodes architectures based on the aforementioned data dissemination strategies. In the distributed data dissemination strategy, all on-body sensors (including WBANs coordinators) are running on top of an IEEE 802.15.6 compliant MAC and PHY layers, with the power consumption characteristics of the CC2420 RF transceiver [START_REF]Chipcon Products from Texas Instruments[END_REF]. Concerning the PHY layer parameters, the transmission power was set to 0dBm, two frequencies were evaluated (i.e., 2450MHz and 900MHz), and for each frequency two different data rates are considered, i.e. 101.2Kbps (DBPSK) and 404.8Kbps (DQPSK) for 900MHz, and 121.4Kbps (DBPSK) and 971.4Kbps (DQPSK) for 2450 MHz. The MAC layer is based on the CSMA/CA protocol with immediate acknowledgement, where all WBANs nodes are operating under the same channel frequency. On top of the MAC layer, the AODV version 2 (DYMO) was implemented with a neighbor discovery frequency of 3s and a timeout of 9s. Finally, a Constant Bit Rate (CBR) application is generating data traffic on all WBANs nodes using different data payloads (i.e. from 16Bytes to 256 Bytes) and frequencies (i.e. 250ms, 500ms and 1s). Fig. 3 shows the node architectures (for both sensors and coordinator) under distributed and clustered approaches. In the clustered data dissemination strategy, each WBAN coordinator device is based on a multi-standard communication stack, where one MAC/PHY interface is used to communicate with the on-body sensors through a dedicated channel frequency (each WBAN uses a different channel frequency to avoid interferences with other WBANs), whereas the second MAC/PHY interface is used to communicate with the surrounding WBANs coordinator using a same channel frequency. In this case, the communication between the onbody sensors and their coordinator is performed using CSMA/CA, whereas AODV version 2 is only implemented at the coordinator node to discover the surrounding coordinator devices from the other WBANs, and to route the collected data to the WBANs group leader. We considered 10 iterations for each simulation scenario, and the 95% confidence intervals were computed and reported in the below simulation results.

C. Results

1) Packet Reception Ratio (PRR)

Fig. 4, shows the results of average PRR against varying payload (i.e., from 16-to-1024 bytes) transmitted per second for the application layer by each of the four sensors and coordinator connected on the body. In addition, 900 MHz and 2450 MHz narrow-band frequencies are utilized with lowest and highest data rates as specified earlier. In general it can be seen that clustered-based approach achieves much better PRR under both frequencies with DQPSK (i.e., highest rate). Whereas, DBPSK (i.e., lowest data rate), in distributed approach achieves the lowest performance in both frequency under all payloads variations. Further, it can be seen in both Fig. 4a and Fig. 4b, that there is a gradual decrease in PRR performance with an increase in the payloads.

In specific, with low payload, clustered approach achieves almost 97% PRR; however, the performance degrades relatively more with the higher payloads especially when operating at 900 MHz frequency. The best performance of the clustered-based strategy at the maximum payload (i.e., 256 bytes) is with DQPSK at 2450 MHz, where the PRR drops up to 75%. On the other hand, distributed approach with the highest rate is comparable with clustered approach (lowest rate) at 900 MHz, though it performs slightly better in 2450 MHz frequency. However, the results are always below 80% PRR even at 2 bytes of payload.

2) Latency

As per the packet delay performance, Fig. 5 shows the average of packet transmission delay. Payload is varied as from 16-to-1024 bytes are transmitted per second. As well, 900 and 2450 MHz are the utilized frequencies. Generally, the results of the delay are inter-related with PRR, if PRR is higher then, delay will be lower due to higher successful transmissions and lower retransmissions. It is clear that both clustered and distributed-based approaches have similar behavior with DQPSK with different variation of the payload and frequencies. Best average delay is given by the distributed-based approach with 64 bytes payload at both utilized frequencies. Accordingly to the PRR, worst performance is noticed for distributed-based dissemination strategy for all payload values and frequencies. Specifically, with low payload and high rate (i.e. DQPSK), distributed and clustered-based approaches latency is interesting with a delay under 10ms. In contrast, for distributed dissemination strategy, delay is infinite with DPSK, which is relatively expected from the PRR average (≈ 0%). DBPSK in clustered-based approach, has a linear increase to reach 50ms with highest payload. (i.e. 1024 bytes).

3) Energy Consumption

Concerning the energy consumption, Fig. 6 shows the energy consumption for clustered and distributed data dissemination approaches with low and high rate (i.e. DBPSK and DQPSK). The energy consumption is shown with two graphs respectively for 900 and 2450 MHz as utilized frequencies. The energy consumption for each transmitted packet is calculated as follows, Epacket = Tpacket × 3Volts × ImA where, Tpacket is the duration in ms which is based on the effective packet length (including all the PHY and MAC headers [START_REF] Alam | Performance evaluation of IEEE 802.15.6 MAC for Wearable Body Sensor Networks using a Space-Time dependent radio link model[END_REF]).

It can be seen that in general similarly for both utilized frequencies, with DQPSK (i.e. highest rate) energy consumption follows the same curve for the two investigated data dissemination strategies. Distributed approach with DBPSK for both frequencies (900 and 2450 MHz) shows the lowest values for energy consumption, this is explained by the null PRR average depicted in Fig. 4. Actually, there is no packets sent in this case (Distributed with DBPSK), so the energy consumption will be consequently the lowest. Clearly, clustered approach with DBPSK consumes slightly low energy compared to DQPSK for both dissemination approaches. However, even though clustered approach with DBPSK is performing with lowest energy consumption, according to the delay discussed based on Fig. 5, is not the most performant approach. Finally, DQPSK digital modulation has the same energy consumption behavior for both dissemination strategies.

(a) (b) I. shows the hop count for different data dissemination approaches with high and low rate and the utilized frequencies detailed above. Hop count is an important metric in tactical networks. Hence, it is considered as the relevant routing decisive parameter. In general, with the digital modulation DQPSK and both utilized frequencies, clustered and distributed dissemination approaches have almost the same hop count average (i.e., from 2,24 to 2,48). With DBPSK, clustered dissemination approach has the same hop count average with both frequencies. Distributed approach with DBPSK with both utilized frequencies is not considered based on the PRR average. Specifically, digital modulation DQPSK is most appropriate for clustered and distributed dissemination approaches in terms of hop count. To conclude, Fig. 7 and Fig. 8 depict the network topology obtained with the clustered and distributed dissemination approaches (2450 MHz, DQPSK, and Payload of 16 bytes). Figures show clearly that number of hops for most of the nodes is much better with the distributed approach (Fig. 7). However, the PRR average (for 2450 MHz, DQPSK and Payload of 16 bytes) is more important with clustered approach (i.e., 89%). So, there is a clear trade-off between both data dissemination approaches.

VI. CONCLUSIONS

This paper provides a comparison between clustered and distributed data dissemination strategies in Wireless Body-to-Body tactical networks. Currently there are very few studies which have evaluated the performance of such emerging networks especially in the context of emergency management and rescue operations. We evaluated the performance of WBBN architecture using realistic radio-link and mobility models, in addition, enhanced IEEE 802.15.6 standard proposed channel models are utilized from our recent ongoing research. Extensive simulations are conducted to evaluate the performance of data dissemination strategies in WBBN using two modulation techniques (DQPSK and DBPSK) and with two different frequencies (900 and 2450 MHz). Our results showed that there is a trade-off between the two approaches. For example, regarding PRR, clustered based data dissemination approach performed much better than the distributed under varying payloads with both 900 MHz and 2450 MHz operating frequencies. In particular, using DQPSK modulation (which provides highest data rates) achieves up to 97% PRR. Whereas, concerning the delay, although clustered-based dissemination strategy using DQPSK provides a considerable options, but distributed approach maintain lowest delay in all configurations and is considered as the best approach. In terms of energy consumption, with DQPSK clustered and distributed-based approaches have similar evolution for different given payloads. Hence, it is concluded that for small tactical teams (i.e., 12 member), with DQPSK modulation technique and for the two utilized frequencies, clustered-based data dissemination approach is appropriate for Body-to-Body communications.

Figure 1

 1 Figure 1 (a): Clustered and (b) Distributed data dissemination strategy

Figure 2 .

 2 Figure 2. Tactical Wireless Body-to-Body Network Scenario.

Figure 3 (

 3 Figure 3 (a) Clustered approach where one frequency is used per BAN and a different frequency is used for inter-WBAN. (b) Distributed approach where same frequency is used from any node to any node (even coordinator)

Figure 4 .Figure 6 .

 46 Figure 4. Average Packet Reception Ratio.

Figure 7 .

 7 Figure 7. Network topology obtained with the clustered routing approach (2450Mhz, DQPSK, and Payload of 16 bytes).

Figure 8 .

 8 Figure 8. Network topology obtained with the distributed routing approach (2450Mhz, DQPSK, and Payload of 16 bytes).

TABLE I .

 I HOPS COUNT STATISTICS (COMPUTED ACROSS ALL DATA

		PAYLOADS AND ITERATIONS)
	PHY Layer	Routing Layer	Min	Hops Count Average Max
	2450 Mhz +	Distributed	1	2.44	7
	DQPSK	Clustered	1	2.24	6
	2450 Mhz +	Distributed	N/A	N/A	N/A
	DBPSK	Clustered	1	1.21	3
	900 Mhz +	Distributed	1	2.48	7
	DQPSK	Clustered	1	2.25	5
	900 Mhz +	Distributed	N/A	N/A	N/A
	DBPSK	Clustered	1	1.26	4

ACKNOWLEDGMENT

This publication was made possible by NPRP grant # [6-1508-2-616] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.