Dimitris Vardalis

Christos-Alexandros Sarros

Vassilis Tsaoussidis

Buffering... energy for mobile devices: A "store and rendezvous" approach *

Keywords: Energy efficiency, 802.11, internetworking, DTN Receiver 1 Receiver 2 Receiver 3

We exploit the traffic shaping potential of network storage and improve energy efficiency for mobile devices through the creation of idle communication intervals. We model the activity patterns between the WIRED/wireless gateway and the wireless battery-powered receiver, and employ a rendezvous mechanism that utilizes periods of inactivity created by the traffic shaping function of the network. In case multiple receivers are simultaneously active, a scheduling algorithm limits overlaps of buffer flushes. Our scenarios are based on the DTN paradigm, however, our approach is not DTN-specific. The presented simulation study involves three main types of Internet traffic (i.e. file transfer, streaming and web browsing) and demonstrates that our proposed scheme achieves significant energy conservation for mobile receivers involving, under most circumstances, only mild performance cost.

Introduction

Mobile devices have become a powerful tool for communication, storage and entertainment, and often undergo several hours of daily use, which stretches their energy storing capabilities. They are equipped with increasingly more powerful CPUs, larger memory capacities, faster wireless network adapters, and a set of applications that require internetworking capabilities. However, advances in battery technology have not been able to match the increased energy demand. For applications that heavily rely on the networking subsystem, the related functions may account for as much as 60% of the total power necessary for the mobile device operation [START_REF] Acquaviva | Remote power control of wireless network interfaces[END_REF]. Consequently, improving the energy efficiency of the networking subsystem has drawn significant attention from the research community and has led to the implementation of energy-saving features in a number of wireless networking applications.

The ubiquitous 802.11 standard [START_REF]Local and metropolitan area networks -Specific requirements, Part[END_REF] confronts the energy efficiency problem by providing a power-save mechanism, which is, however, limited by the probabilistic nature of incoming data as well as the relatively small buffer space at the access point. This has led many researchers into looking for alternative methods, typically involving buffering at a "base station" (operating at the network layer and above) rather than an "access point" (operating at the MAC layer), thus highlighting that the device operation extends in higher network layers.

Our solution to the energy efficiency problem demonstrates the potential of Delay/Disruption-Tolerant Networking (DTN) [START_REF] Cerf | Delay-Tolerant Networking Architecture[END_REF] to shape internetwork traffic in a manner that allows mobile devices to balance their energy expenditure with minimal cost on throughput. We employ DTN in order to improve the energy efficiency of mobile devices in an infrastructure, wired-cum-wireless Internet setting with a last-hop 802.11 connection. The DTN functionality is extended with a rendezvous mechanism (first described here [START_REF] Vardalis | Energy-efficient internetworking with DTN[END_REF]) that allows mobile receivers to switch their Wireless Network Interface Cards (WNIC) to the sleep state during idle intervals.

In the current work, we study the operation of our DTN overlay for various types of Internet activity in terms of both the energy efficiency improvements for the mobile devices as well as possible deterioration of the user experience. Additionally, we explore alternative rendezvous scheduling aimed at minimizing transmission overlaps when multiple devices are active, further enhancing the device energy efficiency. In order to better interpret the simulation outcome, we also provide a simple mathematical formulation of the buffering process.

The rest of the paper is organized as follows: In section 2 we present related work focusing on energy efficient networking and DTN. In section 3 we describe our proposed solution and provide a mathematical formulation of the buffering mechanism. In section 4 we present the experimental methodology and in section 5 the simulation results. Finally, in section 7 we summarize our conclusions and discuss future research plans.

Related Work

The design of energy-efficient networking protocols has long been the focus of scientific research. In [START_REF] Jones | A Survey of Energy Efficient Network Protocols for Wireless Networks[END_REF] Jones et al. provide a comprehensive survey of energy-efficient protocols across the network stack and summarize the design principles for achieving energy efficiency. At the lower network layers, substantial effort in the energy efficiency research involves the WLAN standard 802.11 [START_REF]Local and metropolitan area networks -Specific requirements, Part[END_REF]. The 802.11 protocol provides a power-save mode of operation (PSM) that buffers incoming data at the access point, allowing the mobile devices to temporarily switch their wireless interfaces to the sleep state. The energy conservation potential of 802.11 is limited by the relatively small buffer space at the access point and the lack of visibility at higher network layers, leading many researchers into examining alternative methods based on the same core principle.

In [START_REF] Chandra | Application-specific network management for energy-aware streaming of popular multimedia formats[END_REF] Chandra and Vahdat highlight the limitations of the 802.11 PSM and propose an application-specific traffic shaping proxy for multimedia streaming that can be implemented either on the server (source of the stream) or at the access point. In [START_REF] Adams | Adaptive-Buffer Power Save Mechanism for Mobile Multimedia Streaming[END_REF], Adams and Muntean propose an Adaptive-Buffer Power Save Mechanism (AB-PSM), again for multimedia streaming, which hides data from the base station and allows for longer idle intervals. Energysaving strategies when streaming from a single server to multiple clients are explored by Acquaviva et al. in [START_REF] Acquaviva | Design and Simulation of Power-Aware Scheduling Strategies of Streaming Data in Wireless LANs[END_REF]. Zhu and Cao in [START_REF] Zhu | A Power-Aware and QoS-Aware Service Model on Wireless Networks[END_REF] expand on the proxy idea by introducing a scheduler service at the base station and a proxy at the mobile terminal. Finally, in [START_REF] Tsaoussidis | TCP-Probing: Towards an Error Control Schema with Energy and Throughput Performance Gains[END_REF] the authors exploit windows of opportunities for optimal transmissions, while in [START_REF] Mamatas | Effort/Gains Dynamics in Heterogeneous Networks[END_REF] transmission behavior is adjusted according to network characteristics. Most of the described solutions target streaming applications and involve the installation of specialized components, such as proxies, schedulers and local services in order to operate. The network per se does not participate in the effort for energy efficiency.

Our contribution lies in the use of the DTN computer networking architecture for improving the energy efficiency of mobile devices. DTN was designed to cope with long propagation delays and lack of continuous end-to-end connectivity. Despite its original conception as space-communications architecture, DTN has been proposed for applications in various settings, including wired or wired-cum-wireless networks on the edges of the wellconnected Internet. The DakNet [START_REF] Pentland | DakNet: Rethinking Connectivity in Developing Nations[END_REF] network employs DTN to provide Internet connectivity by physically transferring data using appropriately equipped vehicles. Significant attention from the research community has also been drawn by DTN applications related to vehicular communications [START_REF] Morris | CarNet: a scalable ad hoc wireless network system[END_REF][14] and pocket-switched networks [START_REF] Hui | Pocket-Switched Networking: Challenges, Feasibility and Implementation Issues[END_REF], which aim at providing Internet connectivity to commuters and mobile users respectively. More recently, DTN has been combined with resource pooling techniques that share private broadband connections in order to provide Less-than-Best-Effort, free Internet services to all users [START_REF] Lenas | Enabling free Internet access at the edges of broadband connections: a hybrid packet scheduling approach[END_REF].

In our proposal we rely on the inherent capabilities of DTN for shaping internetwork traffic so that mobile receivers may suspend their WNICs with minimal cost on throughput. In our previous work [START_REF] Vardalis | Exploiting the potential of DTN for energy-efficient internetworking[END_REF] we have extended the DTN bundle protocol [START_REF] Scott | Bundle Protocol Specification[END_REF] with a cross-layer rendezvous mechanism employed between the base station and the receiving DTN nodes. The performance of our solution was evaluated for a single active file transfer. In this work we extend our study by quantifying the energy efficiency improvements achieved for the three main types of Internet traffic (i.e. file transfer, media streaming and web browsing) and assessing the impact of our solution on the user experience for each such traffic type. Furthermore, we propose alternative rendezvous mechanisms in case multiple mobile receivers are simultaneously active and evaluate the performance of these alternatives. In order to better grasp the potential and the limitations of our approach, we also formulate the operation of the rendezvous mechanism in mathematical terms.

Energy-Efficient Internetworking Overlay

Our energy-efficient DTN overlay is deployed on a traditional internetworked wired-cumwireless topology with a last-hop WLAN connection, aiming to improve energy efficiency for mobile devices. The proposed overlay exploits the inherent DTN capability for shaping internetwork traffic in a manner that allows mobile devices to balance their energy expenditure. A minimum deployment of the proposed energy-efficient DTN overlay involves employing DTN on three nodes: Source, Base Station (BS) and Mobile Receiver (MR). In our previous work [START_REF] Vardalis | Exploiting the potential of DTN for energy-efficient internetworking[END_REF], transmission scheduling from the BS to each MR was carried out independently of the transmissions to other MRs with active incoming flows. In this work, we extend the BS functionality so that transmission scheduling considers the incoming traffic for all active MRs, further improving the energy efficiency of the receivers.

Buffering Energy-Saving Potential

In order to assess the potential of the energy-efficient overlay as well as identify the practical limitations of the proposed solution, we developed a mathematical formulation describing the energy-saving potential of the buffering mechanism. Due to space scarcity we include an abbreviated description of the formulation; a detailed derivation of the formulation can be found here [START_REF] Vardalis | Design and Implementation of Algorithms Exploiting Properties of the Delay-Disruption Tolerant Networks[END_REF]. The formulation is based on the following simplifying assumptions:

The incoming and outgoing data rates at the BS are constant, wireless traffic is one-way from BS to MR, and the data to be transferred are of a certain predefined amount.

The diagram of Fig. 1 depicts an example scenario where the output rate (OutRate) at the BS (i.e. the wireless channel) is double the incoming rate (InRate) at the BS (i.e. the Internet connection) and the transfer size (Size) is three times the buffering size (Buffer). The WNIC is assumed to be initially active and immediately switched to the sleep state, allowing for the buffer at the BS to fill (i.e. for a time period of Buffer/InRate). The WNIC must commence the switch to the active state at a time equal to the transition time between the idle and active states (TransTime) prior to the buffer flush, so that it will be fully operational when the BS starts transmitting. Based on the above example, the main measures in a general case can be summarized as follows:

 The total transfer duration is:

𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛 = 𝑆𝑖𝑧𝑒 𝐼𝑛𝑅𝑎𝑡𝑒 + 𝐵𝑢𝑓𝑓𝑒𝑟 𝑂𝑢𝑡𝑅𝑎𝑡𝑒
 The receiving time for the WNIC of the MR is:

𝑅𝑒𝑐𝑣𝑇𝑖𝑚𝑒 = 𝑆𝑖𝑧𝑒 𝑂𝑢𝑡𝑅𝑎𝑡𝑒
 The actual sleep time considering the WNIC transition time is:

𝑆𝑙𝑒𝑒𝑝𝑇𝑖𝑚𝑒 = 𝑆𝑖𝑧𝑒 𝐼𝑛𝑅𝑎𝑡𝑒 - 𝑆𝑖𝑧𝑒 -𝐵𝑢𝑓𝑓𝑒𝑟 𝑂𝑢𝑡𝑅𝑎𝑡𝑒 -2 * 𝑆𝑖𝑧𝑒 * 𝑇𝑟𝑎𝑛𝑠𝑇𝑖𝑚𝑒 𝐵𝑢𝑓𝑓𝑒𝑟
 Finally, the energy spent during the transfer at the MR can be calculated as:

𝐸𝑛𝑒𝑟𝑔𝑦 = 2 * 𝑆𝑖𝑧𝑒 * 𝑇𝑟𝑎𝑛𝑠𝑇𝑖𝑚𝑒 𝐵𝑢𝑓𝑓𝑒𝑟 * 𝑇𝑟𝑎𝑛𝑠𝑃𝑜𝑤𝑒𝑟 + ( 𝑆𝑖𝑧𝑒 𝐼𝑛𝑅𝑎𝑡𝑒 - 𝑆𝑖𝑧𝑒 -𝐵𝑢𝑓𝑓𝑒𝑟 𝑂𝑢𝑡𝑅𝑎𝑡𝑒 -2 * 𝑆𝑖𝑧𝑒 * 𝑇𝑟𝑎𝑛𝑠𝑇𝑖𝑚𝑒 𝐵𝑢𝑓𝑓𝑒𝑟 ) * 𝑆𝑙𝑒𝑒𝑝𝑃𝑜𝑤𝑒𝑟 + 𝑆𝑖𝑧𝑒 𝑂𝑢𝑡𝑅𝑎𝑡𝑒 * 𝑅𝑒𝑐𝑣𝑃𝑜𝑤𝑒𝑟
The above formulation leads to a number of observations that highlight the tradeoff between energy-conservation and data delivery latency, as dictated by the amount of buffered data. For larger amounts of buffered data, the energy savings are higher, but the delivery latency is higher as well. Furthermore, the amount of data that need to be buffered for the scheme to work is dictated by the specifications of the WNIC (i.e. state transition time) and the amount of excess rate of the outgoing vs. the incoming data flows at the BS. Large transition times and small outgoing-incoming data rate differences require large buffering values in order to exploit the energy-saving potential. In case of multiple active mobile receivers, the perceived OutRate for each flow becomes smaller, leading to increased energy expenditure.

The Rendezvous Mechanism

Statistical Internet traffic is shaped into tactic data delivery by the integration of our rendezvous mechanism into the DTN overlay. During idle periods the BS refrains from routing incoming bundles to their final destination and simply stores them locally. As first described in [START_REF] Vardalis | Exploiting the potential of DTN for energy-efficient internetworking[END_REF], the next rendezvous time is calculated as follows:

 𝐵𝑃 = 𝑅𝐵 𝑇𝐵𝑂 : Ratio of the received bytes over the desired buffer occupancy (where BP = Buffer Portion, RB = Received Bytes and TBO = Target Buffer Occupancy). Once the NextRV has been calculated, the BS fragments all bundles that were partially received during the last rendezvous interval and sends the fragmented bundles to the MR. The last bundle of the burst contains the NextRV value in its DTN header. When the MR receives a bundle with a next rendezvous value, it checks if the time to the rendezvous is sufficient for switching to the sleep state and then back to the active state in time to receive the next data bulk and, if so, it suspends the WNIC.

 𝑆𝐵𝑃 = 𝐵𝑃 -𝐵𝑃-
In this work we extend the rendezvous mechanism for the case where multiple receivers are present. We experiment with alternative rendezvous strategies at the BS, seeking to improve the energy efficiency of mobile receivers by limiting overlaps among transmissions to different receivers. The analysis in the previous section suggests that by limiting transmission overlaps, each individual flush would be expedited allowing the receiver to remain active for shorter periods of time. In our study we consider three alternative rendezvous mechanisms: an isolated mechanism, which sets the rendezvous with each receiver individually based on the previously described algorithm, a combined mechanism, which takes into account scheduled rendezvous' of other receivers, and a time-based mechanism, which schedules rendezvous in a purely time-based fashion.

The combined mechanism is a variation of the isolated mechanism. Detection of overlaps among transmissions to different receivers is achieved by estimating the average flushing duration, based on the wireless link nominal bandwidth and the TBO (i.e. 𝐹𝑙𝑢𝑠ℎ𝑖𝑛𝑔𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛 = 𝑂𝑢𝑡𝑅𝑎𝑡𝑒 𝑇𝐵𝑂 ). At rendezvous time, each buffer's NextRV is calculated according to the original mechanism as described in the beginning of this section. Based on the flushing duration estimation, the BS detects possible overlaps with scheduled transmissions to other receivers and tries to reposition the rendezvous earlier or later in time so that overlaps are avoided. The rendezvous may be repositioned earlier than the first scheduled transmission, between two scheduled transmissions or later than the last scheduled transmission. The final choice is made so that the time shifting of the rendezvous is minimized (Fig. 2).

In the time-based mechanism, the rendezvous scheduling is based purely on time and the buffer flushes alternate in equal time intervals. Thus, overlaps are readily avoided since the flushes are scheduled adequately far apart. In this version of the mechanism, the intervals are predetermined based on the average rendezvous time for the combined algorithm for each TBO value. In future versions, however, the mechanism could be modified so that it dynamically adjusts to changing network conditions.

Experimental Methodology

The simulation experiments were carried out using our DTN simulation model, comprising a set of classes implemented in the ns-2 network simulator framework [START_REF] Vardalis | DTN agent for ns-2, Space Internetworking Center[END_REF]. The model supports both TCP and UDP underlying protocols through a DTN agent, which acts as a storeand-forward module between the application and the transport layers. Simulation experiments were conducted in two main directions: Firstly, we employed a single wireless receiving device and experimented with different application protocols; secondly, we employed multiple wireless receivers and experimented with different transmission scheduling strategies at the BS, focusing on file transfers.

In all experimental scenarios, the BS is connected with the MRs on an 802.11 WLAN, with a data rate of 11 Mbps and a basic rate of 1 Mbps (the rate to be used for protocol-specific operations). The TCP packet size is 1460 and the maximum window size 100 packets, while the UDP packet size is explicitly specified in each set of experiments. The energy expenditure for the WNIC of a mobile device is tracked through the inherent energy model of ns-2. The parameters necessary for the energy expenditure calculations are set as follows [START_REF] Shih | Wake on Wireless: An Event Driven, Energy Saving Strategy for Battery Operated Devices[END_REF]: transmit power = 1.400 W, receive power = 0.950 W, idle power = 0.805 W, sleep power = 0.060 W and transition time = 10ms [START_REF] Jamieson | Implementation of a Power-Saving Protocol for Ad Hoc Wireless Networks[END_REF].

The simulation topology used for our experiments is depicted in Fig. 3. Nodes with the DTN suffix host a DTN agent instance, whereas Rel-IP relays IP traffic. The bandwidth and delay values for the topology links are kept constant for all the experiments at the following values: Src -Rel-IP: 1.5Mbps bandwidth for single data transfers and 1 Mbps for multiple simultaneous data transfers, 100ms delay, Rel-IP -BS: 1.5Mbps bandwidth, 100ms delay.

Fig. 3. Simulation Topology

In the first set of experiments we employ the DTN agent in order to evaluate the energy conservation vs. delivery delay tradeoff from a user experience perspective, across the three main types of Internet applications: large file transfers (FTP), media streaming (CBR), and web browsing (HTTP). Data transfers on the overlay follow the Src-DTN→Rel-IP→BS-DTN→MR-DTN path over connections between adjacent DTN nodes. In the end-to-end scenarios a connection is setup directly between a source and a mobile receiver. The traffic characteristics for each application are: FTP -A file size of 10 MB, CBR -A flow sending 500 B every 5 ms (800 Kbps) for 1 minute, and HTTP -A flow continuously sending files of 20 KB for 2 minutes and 40 seconds, where each file is sent as soon as the previous file has reached the destination. HTTP is simulated using a simple application model that assumes no request pipelining [START_REF] Fielding | Hypertext Transfer Protocol --HTTP/1.1[END_REF] and a constant small file size.

In the second set of simulations, multiple simultaneous FTP flows are active, each transferring a file of 10 MB between a DTN source and a DTN-enabled mobile receiver. Namely, the file transfers take place along the network paths: Src-DTN1→Rel-IP→BS-DTN→MR-DTN1, Src-DTN2→Rel-IP→BS-DTN→MR-DTN2, etc. Simulation results are reported for 2, 3 and 4 simultaneous file transfers and for all three rendezvous strategies described in section 3.2 (isolated, combined, time-based). The rendezvous in the time-based case for each TBO are set based on the average measured rendezvous interval for the isolated case for that TBO.

Single Receiver Multiple Application protocols

The chart in Fig. 4 depicts the energy consumption and the delay for the completion of a 10 MB FTP data transfer for various TBO values, ranging from 10 to 80 KB. The end-to-end case is denoted as E2E and plotted as the first point on the energy and delay lines. The additional delay imposed by the data buffering at the BS reaches a maximum of only 0.4 seconds or 0.7% of the transfer duration for a 80 KB TBO. The energy line on the same chart shows that the consumption drops from 55.5 J in the E2E case to a minimum of 36.2 J for the 80 KB TBO case, yielding a reduction of approximately 34%. These results confirm our previous results [START_REF] Vardalis | Exploiting the potential of DTN for energy-efficient internetworking[END_REF] Table 1 provides information on the number of sleep intervals and the total sleep time and transition time for all TBO values. For the smallest TBO of 10 KB the overall idle time (sum of the sleep and transition time durations) is only 7 seconds. Due to the small TBO, the interval until the next rendezvous time is generally too short and does not allow enough time for transitioning to and from the sleep state. For TBO values of 20 KB and larger, the overall idle time is consistently higher than 25 seconds. Maximum sleep time is achieved for the TBO of 80 KB, which also yields the lowest energy consumption as shown in the energy chart. value is usually much smaller than the application ADU (i.e. the transferred file) and so the additional delay is negligible. Fig. 5 includes the energy consumption and the average delay of delivered datagrams for a CBR flow producing a 500-Byte datagram every 5 ms (800 Kbps) for 1 minute. It is apparent that even for the smallest tested TBO value of 10 KB the energy saving is substantial. Specifically, the energy consumption drops from 54.14 J in the E2E case to 36.15 in the 10 KB TBO case, achieving conservation of 33%. The corresponding average delay values for these experiments are 0.2 sec for the E2E and 0.27 sec for the 10 KB cases respectively; an increase in the order of 26%. In the 30 KB case the energy expenditure drops to 30.32 J (44% reduction), while the average delay is doubled with respect to the E2E case (0.41 sec). The energy conservation reaches a maximum of 47% for the 80 KB TBO case, when the delay is 3.7 times that of the E2E case.

TBO Sleep Count

The information displayed in Table 2 shows how the overall idle time (sum of sleep and transition times) remains almost identical (35-36 sec) for all tested TBO values. The additional energy conservation achieved for higher values of the TBO results from the decrease in the overall number of sleep intervals and the consequent reduction of the time spent in the energyconsuming transition state.

The simulation results show that the energy-efficient DTN overlay creates significant energy conservation potential for CBR streaming flows at the expense of a delay increase at the datagram level. However, the additional delay could be compensated in a straightforward way by a small increase in the buffering amount of the client buffering mechanism that is part of most streaming applications. For the 30 KB TBO, where the consumed energy is almost halved, the additional delay was measured at 200 ms, duration that is hardly noticeable by the end-user. Therefore our proposed solution can be applied for most streaming applications with limited adverse effect on the end-user experience. Fig. 6. HTTP with no competing traffic energy and delay vs. the TBO.

The chart contained in Fig. 6 depicts the energy consumption and the ADU delay for a 160second web browsing session vs. the same TBO values as the FTP and CBR examples. What becomes immediately obvious from the energy-delay chart is that the potential for energy saving is tremendous as the energy consumption drops from 134.1 J in the E2E case to 36.18 J in the 20 KB TBO case, a reduction of 73%. The average ADU delivery latency is, respectively, increased from 0.27 sec in the E2E case to 0.47 sec in the 20 KB TBO case. The large energy saving potential is attributed to the long idle periods between transmissions of successive ADUs that may be readily turned into sleep periods when the rendezvous mechanism is employed. For TBO values larger than 20 KB, the energy consumption only slightly improves with a significant deterioration of the introduced delay. The key value of 20 KB for the TBO coincides with the file size of 20 KB selected for this set of experiments.

Contrary to file transfers and streaming applications studied in the previous subsections, the web browsing user experience is sensitive to the application responsiveness. Furthermore, the additional delay of 200-300 ms introduced by the buffering at the overlay will be actually multiplied by the number of elements included in a web page, resulting in a, potentially unacceptable overall delay. However, modern browsers simultaneously open multiple TCP connections so that the web page elements can be more quickly downloaded reducing the overall delay. All these connections in the DTN overlay are handled collectively so that the TBO refers to the total data amount belonging to all active flows, destined to the same node, mitigating the total delay. In any case, this type of proposed buffering in web browsing applications may deteriorate user experience and, thus, it should be employed with prudence. A possibility would be to only enable this feature with the user's consent, in cases the battery level of the device is running low. This way the device operation could be extended at the expense of an inferior web browsing user experience.

Multiple Receivers File Transfers

The charts in this section depict the average energy consumption during an FTP transfer for all active mobile receivers for TBO values ranging from 10 to 80 KB, including the end-toend (E2E) case. Fig. 7 plots the energy consumption for the three rendezvous strategies when two mobile receivers are simultaneously active on the wireless network. For TBO values under 30 KB, the isolated and time-based mechanisms require the same amount of energy in order to complete the transfer, while the combined mechanism achieves an improvement of approximately 10%. For higher TBO values the combined mechanism generally stays between the lines of the isolated and the time-based mechanisms, achieving an average improvement of roughly 5 Joule compared to the isolated mechanism. For these values of the TBO, the timebased mechanism consistently achieves an improvement of over 15% with respect to the isolated case. The results are similar, but somewhat more pronounced, for the case with three mobile receivers as may be seen on Fig. 8. Here, the effect of the mechanisms that limit the overlaps of the buffer flushes to the mobile receivers (i.e. the time-based and combined cases) are even more substantial, improving the energy efficiency of the receivers up to 25% for TBO values between 30 and 60 KB. Moreover, the performance of the combined mechanism largely coincides with that of the time-based mechanism, showing that the flush fitting algorithm effectively limits contention on the wireless LAN. Fig. 9 depicts the average energy expenditure in the four-receiver case. Again, the timebased mechanism achieves significant energy conservation as compared to the isolated mechanism for TBOs of 30 -60 KB. On average, the energy efficiency improves by 30% when the time-based mechanism is in place. On the other hand, the combined mechanism does not exhibit a consistent behavior, although it generally achieves better energy efficiency than the isolated mechanism. The important conclusion that is drawn from the simulation results presented in this section is that, indeed, minimizing overlap of the buffer flushes on the wireless LAN significantly improves the energy efficiency of the mobile receivers. This becomes evident by the performance of the time-based mechanism in all 2, 3 and 4 mobile receivers cases. The combined mechanism also improves energy efficiency over the isolated mechanism to a good extent, despite its relatively inconsistent performance. It is evident that, under certain circumstances, the buffer flushing overlaps are not fully eliminated. In order to visualize the effect of an overlap limiting vs. the isolated rendezvous mechanism we plotted the actual goodput achieved for each buffer flush in the isolated and time-based cases for 3 mobile receivers and a TBO of 40 KB. The results of Fig. 10 and Fig. 11 show how, for the most part, the goodput of the isolated mechanism fluctuates within a wide range, whereas the goodput of the time-based mechanism is contained within a narrow band slightly below the 2.5 Mbps maximum value. In the isolated case, multiple buffer flushes may be simultaneously active, resulting in lower goodput and, thus, longer duration for each flush, forcing the WNIC to stay active for longer periods of time at each rendezvous. On the contrary, rapid buffer flushes of the time-based mechanism achieve better energy efficiency of the receiver by minimizing the time spent in the idle state.

Conclusions and Future Work

Our simulation experiments are in-line with the mathematical formulation and older results in that file transfers are only marginally affected by the DTN overlay operation. Therefore, the proposed energy-efficient solution does not affect the end-user experience and can be unconditionally applied to file transfer applications. With respect to streaming flows, significant energy conservation can be achieved, introducing, however, substantial delays at the datagram level. Since the buffering amount is, generally, larger than the stream datagram, delays are introduced due to the data buffering at the base station. Nevertheless, most streaming applications employ buffering on the client side in order to cope with data rate fluctuations, so the effect of the rendezvous buffering on the end-user experience should be fairly limited, and could be compensated by a slight increase in the client buffering amount. In contrast to file transfers and streaming applications, the user experience during web browsing depends highly on the application responsiveness. Our proposed buffering scheme in the DTN overlay may deteriorate user experience in web browsing applications and, thus, it should be employed with prudence, possibly soliciting the user's consent when the battery level of the device is running low.

When multiple mobile receivers are present, our simulations confirm the implications of the mathematical description that a smart scheduling mechanism that limits overlaps of the buffer flushes further improves energy efficiency as compared to an isolated scheduling approach. Our combined mechanism improves on the isolated approach, but it does not achieve the consistent improvements of the time-based mechanism, suggesting that further development of the mechanism is necessary. It is part of our imminent plans to refine the combined mechanism, mainly in the direction of increasing its flexibility and adaptiveness to changing network condition. Currently, buffer flushing overlap avoidance depends mainly on calculations according to the nominal duration of each flush. We have strong evidence that energy efficiency could be significantly increased if the calculations dynamically adjust based on the load of the WLAN as well as the expected data amount available at rendezvous time.

Fig. 1 .

 1 Fig. 1. Example scenario of a 3*Buffer Size transfer when OutRate equals 2*InRate at the BS.

1 2 :

 2 Smoothed BP value for faster convergence (where SBP = Smoothed Buffer Portion).  𝑁𝑒𝑥𝑡𝑅𝑉 = 𝑅𝐼 𝑆𝐵𝑃 : The interval until the next rendezvous. If 0 bytes were received during the previous interval, the NextRV is set to twice the duration of the previous interval (where NextRV = Next Rendezvous Time and RI = Reception Interval).

Fig. 2 .

 2 Fig. 2. Τwo cases of flush overlap avoidance using the Combined mechanism

Fig. 4 .

 4 Fig. 4. FTP with no competing traffic energy and delay vs. the TBO.

Fig. 5 .

 5 Fig. 5. CBR with no competing traffic energy and delay vs. the TBO.

Fig. 7 .

 7 Fig. 7. Energy consumption for 2 Mobile Receivers.

Fig. 8 .

 8 Fig. 8. Energy Consumption for 3 Mobile Receivers.

Fig. 9 .

 9 Fig. 9. Energy Consumption for 4 Mobile Receivers.

Fig. 10 .

 10 Fig. 10. Buffer Flush Output Rate for the Isolated Mechanism with 3 Mobile Receivers and 40 KB TBO.

Fig. 11 .

 11 Fig. 11. Buffer Flush Output Rate for the Time-Based Mechanism with 3 Mobile Receivers and 40 KB TBO.

Table 1 .

 1 that, for relatively large file transfers, substantial energy conservation can be achieved at a negligible performance cost. FTP with no competing traffic, sleep and transition information.

	TBO	Sleep	Sleep	Trans
		Count	Time	Time
	E2E	0	0	0
	10	232	2.39	4.62
	20	521	14.93 10.36
	30	348	19.93	6.9
	40	262	22.47 5.18
	50	212	23.04 4.16
	60	176	22.74 3.48
	80	134	26.42 2.62

* The research leading to these results has received funding from the European Union's (EU) Horizon 2020 research and innovation programme under grant agreement No 645124 (Action full title: Universal, mobile-centric and opportunistic communications architecture, Action Acronym: UMOBILE). This paper reflects only the authors' views and the Community


Mikhail Komarov
email: mkomarov@hse.ru

Dmitri Moltchanov
email: dmitri.moltchanov@tut.fi

System design and analysis of UAV-assisted BLE wireless sensor systems

Keywords: sensor networks, BLE, UAV, system design, performance

Inefficiency of wireless sensor networks (WSN) in terms of the network lifetime is one of the major reasons preventing their widespread use. To alleviate this problem different data collection approaches have been proposed. One of the promising techniques is to use unmanned aerial vehicle (UAV). In spite of several papers advocating this approach, there have been no system designs and associated performance evaluation proposed to date. In this paper, we address this issue by proposing a new WSN design, where UAV serves as a sink while Bluetooth low energy (BLE) is used as a communication technology. We analyze the proposed design in terms of the network lifetime and area coverage comparing it with routed WSNs. Our results reveal that the lifetime of the proposed design is approximately two orders of magnitude longer than that of the routed WSNs. Using the tools of integral geometry we show that the density of nodes to cover a certain area is approximately two times more for routed WSNs compared to our design.

Introduction

A wireless sensor network is defined as a collection of devices capable of sensing environmental parameters, connected in a network and collectively delivering the collected data to a certain location. As a result of more than a decade of investigations, the challenges and requirements of modern WSNs are well understood [START_REF] Akyildiz | Wireless sensor and actor networks: Research challenges[END_REF][START_REF] Vinel | A simple analytical model for the periodic broadcasting in vehicular ad-hoc networks[END_REF]. There are already a number of complete solutions available on the market. At the same time, in spite of significant market push from the vendors WSNs are still not widely deployed. One of the major reasons is inefficiency of such systems in terms of the network lifetime.

Uneven energy consumption is one of the reasons for short WSN lifetimes. The root cause of this phenomenon is networking of nodes. In practical deployments there are only few locations, where sinks can be positioned. In this case there is always a set of nodes that are more involved in packets routing and forwarding. Since the lifetime of a network is defined as the time till there is no path to the sink we see that uneven energy consumption places severe constraints on lifetime. Over the last decade researchers addressed this problem identifying a number of feasible solutions such as multi-path routing, clusterization, data aggregation, in-network data processing, etc. However, none of those are general enough to be applicable to any arbitrary deployment [START_REF] Andreev | Activemode power optimization in ofdma-based wireless networks[END_REF]. On top of this, the routed principle of WSNs adds to this problem. Indeed, networking mechanisms such as neighbor discovery, connectivity and topology maintenance, routing and packet forwarding require substantial amount of energy [START_REF] Horneber | A survey on testbeds and experimentation environments for wireless sensor networks[END_REF][START_REF] Andreev | Energy efficient communications for future broadband cellular networks[END_REF].

One way to avoid unequal energy consumption is to get rid of networking. Mobile sinks may allow to achieve this goal. To avoid human involvement the collection of data must be completely automatic. The obvious choice would be to use unmanned aerial vehicles (UAV), particularly, quadrocopters, also known as drones. UAVs are a new technology at the "rapid improvement" phase of the S-curve of technology development cycle. As improvements are made, drones are becoming more agile, autonomous, power efficient and safe. The idea of using UAVs for data collection in WSNs is not new. Particularly, the authors in [START_REF] Valente | Anair-ground wireless sensor network for crop monitoring[END_REF] advocated the use of UAV to interconnect sparse WSN clusters. The use of UAV as a mobile WSN node for emergency applications has been suggested in [START_REF] Leng | Using a UAV to effectively prolong wireless sensor network lifetime with wireless power transfer[END_REF]. The authors in [START_REF] Tuna | Unmanned aerial vehicleaided wireless sensor network deployment system for post-disaster monitoring[END_REF] proposed to use UAV for charging and deploying WSN nodes. Nevertheless, to date, no detailed investigations of such solutions and/or their comparison with conventional routed WSN designs have been performed.

We propose a new UAV-assisted solution for data collection in WSNs. We optimize performance of single hop communications between a sensor node and UAV in terms of optimal altitude and and flying speed. We also compare lifetimes, coverage and required density of nodes of our solution with those of routed WSN designs. Using the tools of integral geometry and random graphs theory we show that (i) the lifetime of our design is 10-18 times longer compared to routed WSNs operating using the same BLE technology, (ii) the required density of nodes for environment monitoring is approximately two times smaller even when sensing range coincides with the communications range. These two properties make UAV-assisted WSN design an attractive option for many applications including smart agriculture, forest fire monitoring, etc.

The rest of the article is organized as follows. Section 2 provides state-ofthe-art in UAV and short-range wireless communications backing up the choice of drones and BLE for our design. System parameters are optimized in Section 3. We analytically compare performance of routed WSN solution and the proposed one in terms of network lifetime, coverage and connectivity in Section 4. Conclusions are drawn in the last section.

Technological choices

Communication technology

There are several options to consider as a candidate wireless technology for single hop wireless sensor systems. The comparison of these technologies is shown in This allowed to increase the communications range of the BLE technology while using the same amount of emitted power. Further, significant changes has been introduced to the communication protocol limiting almost all basic parameters. Targeting state applications the maximum packet size was reduced to 27 bytes while the acknowledgement packet size was reduced from 100 bytes to 12 bytes. The authentication in BLE is performed for each packet favoring single packet transactions. Encryption is performed using AES-128. Of special importance is the reduction in the connection establishment time allowing to perform it in just 3ms and completing the transfer of one packet in less than a second. Recalling compatibility with Bluetooth, these features make BLE an excellent choice for power-constrained single-hop wireless sensor systems.

UAV technology

Over the last decade the UAV technology has advanced beyond the domain of military sponsored projects. Civilian applications are becoming more and more common. A whole new class of drones has evolved over the last decade -the Micro Air Vehicles (MAV). It encompasses a whole range of miniature, flying vehicles that have been around for a while now. Recently, multi-rotor UAVs, also referred to as drones, have attracted significant attention. These devices use three or more vertically aligned engines and movement is achieved by creating a difference in thrust on motors on the opposite sides of the frame resulting the UAV tilting and creating sideways acceleration. The thrust regulation needs to be very precise and cannot be done without a microcontroller. To perform basic operations, the microcontroller needs sensors including gyroscope and accelerometer. The first drones were built around Arduino platform, so they already had a surplus of processing power and are compatible with many more sensors than just the essentials. Achieving autonomous flight was just a matter of adding additional sensors and programming the software to use them. This trend still continues today and, as the sensors grow in numbers, so does the processing power needed to make effective use of them. As of today, widely available drones use dedicated Flight Management Units (FMUs) that come with all the basic sensors integrated on one board and a further support for the most common sensor buses like CAN or I2C. A commercial UAV include systems for precise navigation, collision protection, home landing and programmable flying route. With maximum flying speed of 15m/s (54km/h) and time of 30 -90m an UAV can fly up to 79.2km in a single run. Assuming that an UAV carries a BLE device with communication range of 50 -100m, flies in zig-zag way to scan sensors on monitored area, the coverage area of a single flight is 5.5 -16.0km 2 .

System design overview

The system design is illustrated in Fig. 1. Instead of keeping sink node stationary and gathering data by multi-hop routing, we make it mobile and apply BLE technology to further reduce power consumption of nodes. There are multiple advantageous features of using UAVs for data collection. First, flexibility of movement allows to freely optimize trajectories collecting data from the whole network or only from a part of it. Secondly, the movement speed is high allowing to cover the large areas of interest. Taking into account the recent progress in UAVs, the flight navigation could be made completely automatic using GPS. In this mode, an UAV can be configured to follow a predetermined route to collect sensors' data then come back to a ground station to transfer data and recharge battery. A group of UAVs can be used in combination with a ground station or adjacent stations to collect sensors' data in one sensing area or in different ones.

The proposed WSN system is based on single-hop data transmission approach where a UAV carries a sink node, BLE master, and fly over the monitored area to collect data from deployed sensor nodes, BLE slaves. Sensor and sink nodes communicate with each other using BLE. Sink node continuously scan for BLE slave nodes to establish connections and exchange data. After data collection, UAV comes back to ground station. The system does not require time synchronization between UAV flying time and BLE slave wake up pattern.

3 System design

Device discovery time

To exchange data BLE device have to discover its neighbors. It starts with entering the advertising state and then proceeding with connection state. In advertising state the sensor node sends advertising packets over three designated channels (37, 38, and 39). The scanner scans these channels continuously. The role of the scanner is performed by the UAV. When scanner discovers an advertiser, it sends a connection request packet to establish connection. Once replied, both devices enter the connection mode. While connected, advertiser becomes slave and scanner is designated as master.

The advertising interval, T A , consists of a static interval T A,0 and a random part ρ, where ρ is uniformly distributed between 0 and 10ms, T A,0 is chosen in between 20ms and 10s. There are two time intervals defined for the scanner, scanning interval T S , chosen in between 20ms and 10s, and the scanning event time d s chosen in between 0 and T S . The channel interval corresponds to the time to send 38 bytes of advertizing packet and equals to 446µs. The interval between sending advertizing packets within a single adverstizing event is d ch = 150µs.

Let T dis be the discovery time. Analyzing Fig. 2 we see that the discovery is successfull if d A < T dis < T A + d A implying that the upper bound on the discovery time is 10.24 + 0.01 + 0.000446 ≈ 10.25s while the minimum is exactly the time to transmit 38 bytes of data, 446µs.

Connection time

Having received an advertising packet, the scanner sends a connection request packet after waiting for distributed interframe spacing time ( packet contains two parameters, transmitWindowOffset, d two , and transmitWindow, d tw . According to our assumptions all the data can be transmitted in just one packet. Fig. 3 illustrates the connection phase between master and slave. Denoting the connection time by T con we see that

T con = d IF S + 1.25ms + d two + d tw , (1)
where d IF S = 150µs, 0 < d two < T C , 1.25ms < d tw < 10ms, T C is the connection interval which could be tuned between 7.5ms and 4s. Thus, we see that connection time is bounded by

d IF S + 1.25ms ≈ 1.4ms < T con < 4.0114s. ≈ d two + 1.25s + d tw . (2)
In the proposed system, an UAV scans for advertising packets continuously while the BLE module of a sensor node enter advertizing state after each wake up. The bounds on the time to transfer data, T = T dis + T con , is 446µs + 1.4ms ≈ 2ms < T < 14.262s = 10.250446s + 4.0114s.

(

) 3
implying that remaining in the coverage of a sensor node for at least 14s will guarantee exchange of data.

Optimal UAV altitude and speed

Consider a sensor node positioned on the ground and UAV flying over. Assume that the radio of a sensor node is omnidirectional forming a half-sphere around the node's position as shown in Fig. 4. The typical speed of UAV varies in the range of 30-45km/h, 10-15m/s. BLE range, R, is assumed to be upper bounded by 100m. According to the previous section the connection time is upper bounded by approximately 14s. We assume that when UAV crosses the half-sphere shown in Fig. 4 it goes through the center of the cutting plane to increase its chances to establish connection and collect the data from the sensor. Analyzing Fig. 4, we see that the maximum altitude of UAV is given by

h = R 2 -(vT T /2) 2 , (4)
where T T = T dis + T con is the transaction time, v is the speed of UAV. Keeping UAV flying speed low, UAV may have enough time to exchange data with sensor node but the coverage range of UAV decreases. To increase UAV velocity, we need to decrease the altitude. The dependencies between UAV altitude, speed and connection time is shown in Fig. 5.

Transmission power requirements

The commercial BLE chipsets (CC2541, CC2540, [START_REF] Kamath | Measuring bluetooth low energy power consumption[END_REF]) are produced with programmable output power that could be adjusted over a wide range. Relying on the estimated UAV flying parameters the task is to determine the minimum transmission power needed to establish connection and exchange the data [START_REF] Moltchanov | Simple, accurate and computationally efficient wireless channel modeling algorithm[END_REF]. Since the proposed system is applied for monitoring flat area with line-ofsight between UAV and sensor node, the free space path loss propagation model can be applied. Using Friis equation in the form

P Rx = P T x + G T + G R -20 log 10 (d) -20 log 10 (f ) + 147.55, ( 5
)
we derive P T x , where P Rx is the minimum power at the receiver set to -90dBm, P T x is the transmission power we are looking for, G T = G R = 6dBi are antenna gains at the transmitter and receiver, respectively, d is transmission range in kilometers and f = 2.4GHz is the operating frequency. Fig. 6 shows the relation between the required transmission power and transaction time when UAV altitude varies between 10m and 20m. The transmission power in the proposed WSN design is extremely low ranging from -224.946dBm to -207.702dBm. 4 Performance analysis and comparison

Network lifetime

In the proposed design all the nodes spend equal amount of power. Consider the power required to perform a single transaction. The power consumption of the radio states of advertising and connection events is provided in [START_REF] Kamath | Measuring bluetooth low energy power consumption[END_REF]. Based on these data, the power consumption for advertising and connection events are

P A = U i I i t i i t i = 32.759, P C = U i I i t i i t i = 24.762, (6)
leading to the overall consumption per transaction P = 57.521mW, that is, 19.173mA in 6.92ms. Assuming that sensor nodes typically use a coin-cell CR2032 battery with capacity 225mAh, a single node can work continuously during 20.605 hours. If we set the device to wake up periodically in each 10s, it can last for approximately 2020 days or around 5.53 years. Consider now a lifetime of a routed WSN. Nodes are assumed to be connected forming a network and all the data are routed to the sinks. Thus, sink nodes will be the first to run out of power. The lifetime of sink nodes depends on the number of nodes in a network, M , the number of sink nodes, n, and the data collection interval, d t . The lifetime of a routed WSN can be approximated as

T f = T c T d d t = T C N d t t C M . (7)
Assume that the routed WSN use BLE with the data exchange event consuming 24.762mW . The lifetime of a routed WSN is shown in Fig. 7(a). Observe that the maximum lifetime of a routed WSN with the same communication technology (n = 25, M = 100, d t = 10s) is approximately 2552 hours (106 days) which is 18 times smaller than 48482 hours (2020 days) for the proposed design.

Network coverage

Consider the number of nodes needed to cover a monitored area in the proposed system. The analysis relies upon the results of [START_REF] Lazos | Stochastic coverage in heterogeneous sensor networks[END_REF] and is based on integral geometry approach involving, particularly, the notion of kinematic density.

Let A 0 (F 0 , L 0 ) be the monitored area with perimeter L 0 and area F 0 . Assume N sensors are distributed according to K(A 0 ) distribution over A 0 . Each sensor has a sensing area A i (F i , L i ), i = 1, 2, . . . , N , where L i and F i are the perimeter and area, respectively. We are interested in the following metrics: (i) the fraction of area of A 0 that is not covered, and (ii) the probability that a randomly selected point of A 0 is covered by at least k, k ≥ 1 sensors. Assuming that A 0 and A i are convex, the fraction of area A 0 that is not covered by any sensors when N sensors are randomly and uniformly deployed is given by [START_REF] Lazos | Stochastic coverage in heterogeneous sensor networks[END_REF] F r(S = 0) = The probabability that a point is covered by at least k, k ≥ 1 sensors is [7]

N i=1 2πF 0 + L 0 L i 2π(F 0 + F i ) + L 0 L i . (8)
P r{S ≥ k} = 1 - k-1 h=0 C N h i ( N j=1 (2πF Ti,j ) N -h z=1 Θ(i, z)) N r=1 (2π(F 0 + F r ) + L 0 L r ) , (9)
where, Θ(i, z) = 2πF 0 + L 0 L Gi,z , T i,j is a matrix whose each row i is a kpermutation of [1, 2 . . . , N ], G i,z is a matrix whose each row i contains the elements of [1, 2, . . . , N ] that do not appear in the ith row of T i,z . The notation C N h denotes binomial coefficient.

The fraction of uncovered area as a function of the number of uniformly deployed nodes is shown in Fig. 7(b). The sensing radius of nodes are assumed to be 100m while the monitoring area radii are 0.5, 1, 1, 5, 2km. The fraction of uncovered area decreases exponentially when the number of nodes increases. Using these data one can estimate the number of nodes required to cover a certain area such that only a given small fraction on area is unmonitored.

Fig. 8 illustrates the probability that an arbitrary point of an area is covered by at least k sensors as a function of the number of nodes for different area radii. The redundancy added attempting to cover the largest possible fraction is significant. For example, recalling that for area radius of 1km 365 nodes cover 95% of area, we see that this amount of nodes results in non-negligible probability of having two and more nodes covering an arbitrary point of an area. This is a usual price to pay for simplicity of stochastic deployment.

Connectivity

In routed WSN nodes have to be connected. This may require denser deployment than that needed to ensure a coverage only. Here, we evaluate the nodes density required to ensure k connectivity and compare it to that dictated by the coverage.

We approach the connectivity problem using the results of random graph theory. In his seminal paper [START_REF] Penrose | The longest edge of the random minimal spanning tree[END_REF], M. Penrose addressed the question of the distribution of longest edge in a random geometric graph and k-connectivity of these graphs, respectively. Assuming uniform distribution of nodes over the monitored area and knowing the communications range of a node the former result immediately gives the probability that the network is disconnected while the latter one provides quantities such as the probability of exactly or at least k paths between two randomly chosen nodes.

Let R be the radius of a circular area, d be the communication range of a sensor, n be number of nodes and k be the number of neighbors of arbitrary node. The probability that an arbitrary node has at least k neighbors is [START_REF] Penrose | The longest edge of the random minimal spanning tree[END_REF] Q

n,k (d) = 2π C 0 rf (r)(1 - k-1 i=0 C n-1 i p(r, d) i (1 -p(r, d) n-1-i ))dr, (10)
where f (r) is the density of node location, see [START_REF] Lassila | Connectivity properties of random waypoint mobility model for ad hoc networks[END_REF].

The probability that a network with n nodes is k-connected is [10]

C n,k (d) ≈ [Q n,k (d)] n . (11)
Fig. 9 shows the probability that the network is connected (1-connected) as a function of the number of deployed nodes for different radius of an area in both linear and log scales. A routed WSN requires much more nodes than the proposed design and the reason is the need for connectivity of the system. Recall that for the proposed WSN design, for an area radius of 1km and sensing radius of 100m, the number of nodes ensuring 95% coverage is 365. For routed WSN with nodes having communication range of 100m, to keep the network connected with 0.95 probability, we need to deploy more than 1000 of nodes. Deploying 365 nodes randomly, the probability of connected network is 6.4E -7.

Conclusions

We proposed and evaluated a new UAV-assisted WSN design. It is based on BLE wireless technology for single-hop communications and UAVs for collection of data. The proposed system allows to enforce even energy consumption in WSNs, improves the network lifetime and is still completely automatic in terms of data collection. Based on the modern BLE and UAV specifications, we developed a single-hop sensor-UAV interaction model estimating the optimal altitude and flying speed of UAV for the worst case BLE transaction time as well as the energy required to establish reliable communications.

We analyzed the performance of the proposed design in terms of the lifetime and coverage properties. We also compared these metrics to those of conventional routed WSN design revealing that (i) the proposed system provides 10 to 18 times longer lifetimes compared to conventional WSN and (ii) the density of nodes required to ensure coverage is approximately two times smaller compared to routed WSNs even when sensing region of nodes coincides with their communications range. These properties make the proposal an attractive option for monitoring environmental parameters in large open areas.

Fig. 1 .

 1 Fig. 1. Proposed WSN data collection mechanism.

Fig. 4 .

 4 Fig. 4. An illustration of the UAV flying over a BLE sensor node.

Fig. 5 .

 5 Fig. 5. UAV altitude and transaction time for different UAV speeds.

Fig. 6 .

 6 Fig. 6. The required transmission power for different transaction times.

Fig. 7 .

 7 Fig. 7. Performance comparison of the poposed and conventional designs.

Fig. 8 .

 8 Fig. 8. Probability of an arbitrary point covered by at least k sensors.

Fig. 9 .

 9 Fig. 9. Probability of 1-connectivity.

Table 1 .

 1 Comparison of low power communications technologies.

	Metric	PPBµW/bit Range, m Rate, Mbps Delay, ms PPC, mA
	Bluetooth 0.05	30	2.1	20	100
	BLE	0.153	50	0.3	3-6	12.5
	ANT+	0.71	30	0.002	<1	17
	ZigBee	185	100	0.1	20	40
	Wi-Fi	0.005	150	6	1.5	50
	Nike+	2.48	10	0.0003	1000	12.3
	Table 1, where PPC stands for peak power consumption, PPB refers to power-
	per-bit. In this paper we advocate the use of recently standardized BLE. BLE is
	an evolution of Bluetooth for state transmission systems, i.e. sensor data. Table
	2 summarizes the most important improvements introduced in BLE compared
	to Bluetooth. Modifications affected the critical features including physical layer
	modulation, communications protocols, application interface and security. One
	important improvement is the use of Gaussian Frequency Shift Keying with
	larger modulation index, 0.45-0.55 instead of 0.25 -0.35 in Bluetooth. Recall,
	that the modulation index in GFSK affects the wideness of the signal spectrum.

Table 2 .

 2 Comparison of Bluetooth and BLE.

	Parameter	Bluetooth	BLE
	Channels	79	40
	Modulation/index GFKP/0.35	GFSK/0.55
	Tolerable pathloss 90dB	95dB
	Range	30m	50m
	ACK length	100B	12B
	Max packet size	1021B	27B
	CRC length	2B	3B
	Encryption	Safer+	AES-128
	Authentication	Once	Every packet
	Connection states Inquiry/Page/Connected Advertizing
	Connection time	20ms	3ms

  DIFS, d IF S). This Time diagram of advertising/scanning phase in BLE.

	Advertizing packet	d A		Advertizing event
				T A		d ch
		Ch. 37	Ch. 38	Ch. 38	Ch. 37	Ch. 38	Ch. 38	Ch. 37	Ch. 38	Ch. 38
	Advertizer
										t
	Scanning Ch. 37				Scanning Ch. 38			Scanning Ch. 39
	d S	T S					d E			d L
	Scanner									t
	CR Fig. 2. Transmit window M->S S->M ADV ADV Slave T A d IFS d two +1.25s d tw	M->S d	S->M	t
					d tp		T C
	ADV	ADV		CR	M->S	S->M	M->S	S->M
	Master									t

IFS Fig. 3. Time diagram of connection phase in BLE.