Reliable Data Collection from Mobile Users with High Data Rates in Wireless Sensor Networks

Reliable Data Collection from Mobile Users with High Data Rates in Wireless Sensor Networks Lin Guo and Qi Han Department of Electrical Engineering and Computer Science Colorado School of Mines Email:{lguo,qhan}@mines.edu Abstract-Reliably collecting data from mobile sensor nodes is essential with the increasing popularity of integrating mobile nodes into stationary sensor network infrastructure for various scientific and social applications. Existing sensor data collection protocols demonstrate high reliability in stationary sensor networks, but when applied to mobile nodes, the packet acceptance ratio drops significantly. This is because the topology changes due to mobility are not well detected by routing protocols. We present Location-Aware Relay Association Protocol (LRP), a relay association protocol that is independent of any routing protocols and adopts distance measurement techniques to detect topology changes. Instead of reinventing yet another routing protocol, LRP complements it by estimating the distances between a mobile node and its current parent node in the infrastructure network. LRP notifies the routing protocol to trigger route refreshes when a node moves out of the effective communication range of its parent. By continuously adapting to changing topologies in a fast and efficient manner, LRP improves the network reliability and throughput when nodes are moving. In test bed experiments, LRP achieves higher network throughput and lower control overhead than other comparable protocol in data collection applications with high data rate under various mobile environments. Keywords-Reliable data collection; Mobile wireless sensor network; High data rate; Relay association protocol I. INTRODUCTION Wireless sensor networks (WSNs) have been used in a wide range of applications such as structural health monitoring in remote or hostile environments. The use of low power sensors offers the benefit of continuously collecting data at unprecedented spacial and temporal scales. Many applications expect to reliably receive data at a very high data rate from sensors that might be mobile. For instance, voice and video data may be collected from sensors carried by first responders who move around at a disaster site to improve emergency response; patient-specific data may be monitored and collected from sensors worn by patients and these patients do not stay still for a long period of time [1]; structural health monitoring applications [2] collect data from sensors mounted on continuously rotating parts such as wind turbines or aircraft wings. In this work, we investigate how to provide reliable data collection from mobile sensors with high data rates. We specifically consider the scenario where mobile sensors co-exist with static sensors in the area of interest. This hybrid infrastructure allows for mobile nodes in the network to move to any location while still maintaining communication with stationary nodes. These stationary nodes (also called infrastructure nodes) provide necessary network coverage of the area. Mobile nodes are able to move around the coverage area based on application needs and use infrastructure nodes to transmit data to the base station. In other words, we assume each mobile node is always able to communicate with at least one infrastructure node. Therefore, the impact of mobility is limited to the first hop between a mobile node and the base station. The requirements of high data rate, mobility support, and reliable data collection pose significant challenges. First, the high data rate traffic consumes more bandwidth than low data rate traffic and pushes the network to the capacity limit. Second, the movement of sensors results in the change of communication links. The routing protocol should be adaptive to the topology change in order to identify a reliable and efficient path. Last, dynamic wireless links are known to present variable and unreliable link quality even in stationary networks and stable environmental conditions. The mobility of sensor nodes exacerbates the situation. In the literature, techniques have been developed to deal with high data rate, node mobility, and reliability separately. We argue that providing reliable data collection from mobile sensors with high data rates in WSNs requires a holistic approach that is more than a crude composition of existing techniques addressing each individual requirement. The most relevant work is Dynamic Relay Association Protocol (DRAP) [1]. It is used to collect data from sensors worn by patients. DRAP limits the mobility on first hop communication and shows better performance than CTP. In clinical health monitoring the typical data rate is 1 packet every second. DRAP detects invalid communication links with a reactive strategy. When the number of packet loss exceeds the predefined threshold, the current routing path is determined as invalid and then DRAP triggers the node to select a new node for routing. This reactive strategy works well in low data rate applications. However, it is not suitable for high data-rate applications. If an aggressive threshold 978-1-4673-1239-4/12$31.00 2012 IEEE

is used, DRAP results in unnecessary rerouting overhead and decreases the performance. On the other hand, with a conservative threshold, DRAP fails to detect invalid links in a timely manner, thereby leading to degradation of reliability. Considering that a large number of routing protocols have already been developed for WSNs, our approach is to design a relay association protocol for mobile nodes. Relay association basically connects a mobile node to an infrastructure node. The relay association protocol can work with any existing routing protocol and is responsible for effectively detecting invalid routes and then establishing a new route for mobile nodes. Our proposed relay association protocol achieves this by keeping track of the movement of mobile nodes. In contrast, a routing protocol runs on stationary nodes and is responsible for multi-hop communication in hybrid WSNs. The primary objective of our protocol is to improve reliability and throughput in mobile environments. Reliability is an important metric from applications' perspective. With the improvement of reliability and decreased communication overhead, our approach indirectly improves the network throughput, thereby satisfying the high bandwidth requirement of high data rate applications. II. LOCATION-AWARE RELAY ASSOCIATION To ensure fast responses to topology changes caused by mobile nodes, we develop a proactive location-aware relay association protocol (LRP). LRP does not assume any prior knowledge of the node movement history and does not attempt to predict the movement patterns of nodes either. The mobile stress studies [1] conducted for clinical monitoring applications indicate that a typical routing protocol such as CTP [3] designed for stationary WSNs fails to identify suitable parents in the routing tree for mobile nodes. To overcome this drawback, LRP uses relative distances between nodes to infer whether a node might have moved out of the effective communication range of its current parent in the routing tree. Therefore, the routing protocol can use this information to better cope with node mobility by broadcasting the routing beacons to the neighbors and updating the stale routing information. Specifically, instead of finding the accurate locations of mobile nodes, LRP estimates the relative distance from a mobile node to its parent stationary node. Using this relative distance, LRP infers whether the mobile node is still within the maximum transmission range (also called distance threshold dth) of the current parent. If the node moves away from its parent more than dth, LRP will trigger the routing protocol to refresh the route so that a new parent will be used for the mobile node. Otherwise, the topology is considered stable, so the routing protocol will determine the next hop node as usual. However, it is not reasonable to assume constant communication range since the quality of radio links is essentially time-varying and also different hardware platforms and power levels usually have different communication ranges. To this end, LRP employs an adaptation strategy for dynamically adjusting communication ranges based on current network conditions. In the following, we first discuss how relative distance between nodes is estimated and then present adaptive communication ranges used by LRP. A. RSS-based Distance Estimation To calculate the relative distance between a mobile node and its current parent infrastructure node, we adopt the RSS (Received Signal Strength) based statistical model. RSS-based measurement has been widely used in various localization approaches because it is a low-cost solution for many applications in WSNs. The RSS measurement is simple to implement in resource-constrained hardware platforms. It is also inexpensive to measure the RSS value in each receiver during normal data communication without extra communication and energy cost. TOA (Time of Arrival) based localization approach [4] requires sensitive timer and high time synchronization accuracy. AOA (Angle of Arrival) based localization approach [4] requires additional directional antenna in specific topology. Compared with TOA and AOA measurement, RSS measurement is more attractive due to its low complexity and cost. Typically, the received power in radio communications decays proportionally to d2, where d is the distance between a node pair [4]. The received signal strength is affected by multipath reflection and environment noise, and the error of RSS measurement can impact the accuracy of estimated distance. Assuming these effects are randomized, the received power at a given distance is modelled as a log-normal distributed variable as in Equation (1), where Po (do) [dbm] is the prior known reference power at the reference distance do, n p is the environment-specific path loss exponent. X u is Gaussian distributed random variable with standard deviation (Y. X u represents the random effect of a real environment [5]. d Pr(d)[dBm] = Po(do)[dBm]-lOnploglo( do ) +X u (1) Based on Gaussian probability density function, we can derive the maximum estimated distance between the transmitter and the receiver as follows [5]. d = do( ) (n1p) Po (do) In Equation (2), Pr and Po are measured in millliwatts instead of dbm. Distance estimation and route update are done as follows. Require: Reference Power Po, Reference Point do, Pass loss exponent n p, Initial Threshold Dinit 1: if receive beacon from parent then 2: Get RSSI value from incoming beacon 3: Calculate d using Equation (2) (2)

4: if d ;:::: dth then 5: trigger routing update. 6: end if 7: end if Distance estimation is complementary to the routing protocol. It does not need additional beacons running in the nodes. LRP calculates the relative distance between a mobile node and its current parent using original routing beacons. The routing protocol uses periodic beacons to detect the link quality and topology. B. Adaptation of Distance Threshold The accuracy of RSS-based distance estimation is limited by additive noise and shading effect. The empirical study of distance measurement approaches on MICA2 sensors shows that accuracy of radio-based RSS measurement is low (2-4 meters) even in good conditions [6]. Since distance measurement errors are inevitable in practice, the routing protocol using this technique must be robust and tolerant of imprecise location or distance information. It is hard to balance the efficiency and cost with a fixed distance threshold. A smaller distance threshold is agile to topology changes, but decreases routing stability and uses more energy. In contrast, a larger distance threshold reduces the routing cost but might lead to stale and ineffective routings. To this end, LRP dynamically adjusts the distance threshold, aiming to achieve both agility and low cost. In general, if a node establishes the same route as before after refreshing, LRP increases the threshold to suppress future route refreshes. If a node detects the burst of packet loss or the continuous degradation of link quality, LRP lowers the threshold to a smaller value to refresh the route. Growing the threshold: LRP suppresses route refreshes by monitoring any parent changes after reroute. The same parent implies that the previous link still maintains good connectivity but the node thought the link was broken due to mobility. The unmatched result reflects the effect of inaccurate distance estimation and dynamic channel condition. The same route selection will be fed back to the distance estimation module to increase the distance threshold Dth a larger value. Inspired by the Trickle algorithm for efficient code maintenance and propagation in WSNs [7], we design LRP to scale up Dth by a factor of Ndistance when the same parent is selected after route refreshing. When adapting Dth exponentially, LRP quickly suppresses unnecessary route refreshes and becomes more tolerant of location measurement errors in dynamic radio communication. Shrinking the threshold: LRP decreases the distance threshold when a burst of packet loss is observed in current link. Here, we define the size of packet loss as the parameter Wloss. The packet loss information is pulled from the routing layer. The receiving node counts the sequence of routing beacons from the neighbor. If the current link drops more than Wloss packets, LRP modifies the distance threshold to a smaller value. Similar to the adapting mechanism in growing the threshold, LRP multiples Dth by a factor of l/ndistance when shrinking the threshold. With the new smaller threshold, LRP is more sensitive to the location changes of mobile nodes and is more likely to trigger the routing protocol to update the routes. Figure 1 shows the process of adjusting distance threshold based on the feedback from the routing protocol. LRP uses both regular route beacons and the packet losses to adjust the distance threshold in two parallel components. When mobile nodes receive packets from the parent nodes, LRP measures the RSS value of the incoming packets to estimate the relative distance among the mobile node and its parent. If the estimated distance exceeds the threshold Dth, it triggers the routing protocol to refresh the route. To protect the route refreshing from interrupt, the refresh flag will be set to true when the routing protocol starts refreshing. When it finishes route update and selects new parent, the refresh flag will be set to false. During the route refresh which is indicated by refresh flag, new refreshing request will be ignored since the routing protocol is currently refreshing the route table. Simultaneously, LRP tracks the number of packet losses to determine when to shrink the threshold Dth. If the packet loss exceeds the loss window Wloss, it will decrease the threshold Dth to a smaller value. Figure I. When Mobile Node Receives Packet From Parent Adaptation of Distance Threshold III. PERFORMANCE EVALUATION The performance of LRP is evaluated against DRAP [1] under various real world mobile conditions using data collection applications with different data rates. DRAP is

chosen as the comparison candidate because previous work shows that DRAP outperforms well-known CTP [3] when collecting data from mobile nodes for clinic monitoring [8]. The MAC protocol used is standard Carrier Sense Medium Access (CSMA) implementation in TinyOS 2.1 [9]. In the experiments, we measure the receiving data rate ratio and number of beacons. The sending (or receiving) data rate is computed as the total number of sending (or receiving) packets divided by the duration of test. Receiving data rate ratio is the ratio of receiving data rate to the sending data rate. The receiving data rate ratio measures the network reliability under different traffic load. The number of beacons measures the network routing packets overhead which reflects the efficiency of routing strategy. A. Testbed Setup We deploy an infrastructure network with 11 TelosB nodes in a closed office environment illustrated in Figure 2. The sensors are distributed in four walls of the room. On each wall, the sensors are placed in a line with equal distance apart. The infrastructure nodes provide the communication coverage in the space. They are running regular CTP protocol and do not inject data traffic in the network. The base station is placed in one corner of the office to best explore the depth of network. sensors in one robot. One is running LRP at channel 15 and the other is running DRAP at channel 21. Corresponding to two mobile nodes running two protocols, we deployed two sets of infrastructure nodes which have the same network topology. One network uses channel 15 and the other uses channel 21. In the experiments we configured the power level of the mobile nodes to be 3 so that the average hop from a mobile node to the base station is two. Due to the lab space limitation, our infrastructure network was deployed as a small network with 11 nodes. The CTP implementation in TinyOS 2.1 [9] demonstrates a sensor node can easily store the information of all the 11 nodes in the memory. In this case, neither LRP nor DRAP can further improve the network reliability because the strategy of refreshing the routing table in relay association protocols does not help a mobile node to discover any new nodes. However, this is very different from the situation in a large network where the sensor nodes typically store a limited number of nodes in the memory compared to the total number of nodes in the network. To mimic larger network, we adjusted the neighbor table size of CTP to be 3 for mobile nodes. This means that the mobile nodes can store at most 3 neighbors in the memory. In this way, our experiments are more compatible to the situations in a large network. B. LRP Setup * 5.5m Figure 2. Testbed: The green square denotes the base station. Red circles denote the infrastructure node. Blue stars represent mobile nodes. The solid line are forwarding links in the routing tree while dashed lines are relay links at the current position. Mobile sensors are attached to IRobots which can be programmed to move around in the office. We used robots to carry sensors so that experiments can be repeated. The mobile nodes are running both relay protocols and CTP protocols. They inject data traffic with specified data rate and send the data towards the base station. The robot is programmed to move forward at a given speed. Once the robot runs into obstacles, it moves back and turns around at random angle to avoid the obstacles. Since the office is occupied with desks and chairs, the robots are eventually moving in random patterns. In the experiments, each run has a different moving path. It is unreasonable to compute the mean and standard deviations from multiple runs with different moving paths. Therefore, the experiment results presented are from an individual run. To make sure LRP and DRAP are run under the same movement condition for fair comparison, we placed two There are several configurable parameters in RSSI Equation 2 we need to determine when setting up LRP. Those parameters should be configured based on environment and application scenarios. 1) do,po,np (Constants in Equation (2)): do represents the reference distance which can be configured with any distance value. In our implementation, do is set as 1 meter for simplicity. Po represents the reference receiving power at the reference distance. Since do is set to 1 meter, the Po is determined as -39dBm correspondingly. np is the path loss exponent and depends on the sensor hardware and environment. To determine np for our hardware and environment, we implemented a small-scale experiment and measured the power data with a variety of distances listed in Table I. We then plotted a fitted curve with the power and distance data to calculate np. The experiment consists of one sender and one receiver. The sender is broadcasting packets, and the receiver records the receiving power level at the given distance. We vary the distance among the sender and receiver, and record the corresponding power readings at the receiver. Figure 3 shows the measurement and fitted curve. From our experimental data, np is calculated as -1.095. 2) Dinit (Predefined Distance Threshold): The appropriate value of Dinit helps to minimize the warm up time of the protocol. Otherwise, the protocol will take longer time to adjust the threshold to an appropriate value. In our

10 -;:-, 8-:.,, E ' -, Q) 6.1 U, C, :. 4.1 o ".,.,, -;...... -- I - Measured Distance - - RSS Measurement triggers excessive beacon messages to probe the topology change resulting in unnecessary overhead. o.sooo 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 Power(milliwatts) 0.0014 Figure 3. RSS Measurement model for different distances listed in Table I. The fitted curve plots Equation 2. Table I THE POWER MEASUREMENT WITH DIFFERENT DISTANCES Distance( m) Measured Power(d Bm) Measured Power(uW) implementation, we set initial distance Dinit to 30 according to the TelosB hardware manual [10]. 3) Ndistance (Distance Adjustment Amount): Ndistance relates to the velocity of the mobile nodes. The higher velocity the mobile nodes moves, the more amount we need to adapt in distance threshold. In our implementation, we configured distance adjustment factor Ndistance to 2 from practical experience under low-speed scenarios. 4) W10ss (Packet Loss Window): Wloss is related to the rate of the transmitted data rate. The higher the data rate is, the larger the packet loss window is. In our implementation, we chose packet loss window W10ss to be 10 which is same as the current implementation of CTP in TinyOS2.1 distribution [9]. C. Experimental Results Impact of Date Rate Effect. Figure 4 shows the performance results when we vary the data rate in the application. In this experiment, the sending data rate increases from 1 packet/second to 20 packets/second. Increasing data rate is necessary to observe the network performance under different traffic load. When the sending data rate increases, the receiving data rate ratio of DRAP drops from 95% to 80%. In contrast, the receiving data rate ratio of LRP remains over 99% for all data rates. These results demonstrate that LRP is more suitable for high data rate applications. Figure 4(b) shows the beacon traffic from 20-minute experiments when a mobile node moves at 500 millimeters/second. LRP has a much lower number of beacon packets than DRAP in all experiments: the beacon overhead in LRP is at least 50% less than the beacon overhead in DRAP. The high beacon overhead in DRAP implies that it overestimates the effect of movement in route selection and (a) Receiving Data Rate Ratio Figure 4. Impact of Data Rate (b) Number of Beacons Impact of Moving Speed. Figure 5 shows the result of one mobile node sending 20 packets every second while increasing the moving speed from 100 mm/second to 500 mm/second. LRP achieves higher receiving data rate and lower beacon overhead than DRAP in all cases. When the moving speed varies, the receiving data rate ratio of LRP is above 95% while the receiving data rate ratio of DRAP fluctuates between 80% and 90%. The beacon overhead of DRAP shows significant difference in all cases. In four experiments, the average number of beacon packets per node in DRAP is greater than 150 but the beacon cost drops below 40 in the last experiment. We believe that the differences in all experiments is because DRAP refreshes the routes simply based on the fixed number of unacknowledged packets. It is expected that one strategy can not be adapted to all cases and results in unstable beacon overhead in different cases. Figure 4(a) and Figure 5(a) reveal that within certain speed range, the network reliability and throughput mainly relates to the sending data rate. Figure 4 shows the general trend for both protocols is that the higher the sending data rate is, the lower the receiving data rate ratio is. This is because when the sender is sending packets in a short interval, the application needs the network to discover the topology changes and forward the data packets within short time intervals as well. The high data traffic load increases pressure on the network strategy to discover the topology change in a fast and efficient way. (a) Receiving Data Rate Ratio Figure 5.. I- -MAP "" I '" " " Impact of Moving Speed m, II,,, - Speed(mmfSl!<ond) (b) Number of Beacons

Impact of Number of Simultaneous Mobile Nodes. Figure 6 shows the result of increasing the number of simultaneous mobile nodes while keeping the data rate at 20 packets per second and the moving speed at 500 mm per second in each mobile node. As expected, the receiving data rate ratio increases in proportion to the number of mobile nodes. In this experiment, LRP achieves higher receiving data rate ratio and lower beacon cost in all cases than DRAP. Figure 6(a) also indicates that the average receiving data rate ratio per mobile node is decreasing when the number of mobile nodes is increasing. In LRP, the receiving data rate ratio is above 95% when there is one mobile node. But the average receiving data rate ratio drops to 90% when there are three mobile nodes. This implies that when more mobile users are injecting data traffic in the networks, more packet losses occur due to potential network congestions and network bandwidth limitation. sensor networks. We devise a Location-Aware Relay Association Protocol which adopts RSS-based localization to detect topology changes. We also present an adaptive strategy to adjust the maximum communication range in order to allow the relay association protocol to remain efficient and cost effective in the presence of changing topologies. The performance of LRP has been thoroughly evaluated using data collection applications with different data rates under a variety of moving speeds. Testbed results demonstrate that LRP offers higher network reliability with different data traffic loads and sends 50% fewer beacon packets than the existing protocol DRAP. ACKNOWLEDGEMENT This work was supported in part by NSF grant CNS- 0855060. REFERENCES [I] O. Chipara, C. Lu, T. C.Bailey, and G.-c. Roman, "Reliable clinical monitoring using wireless sensor networks:experiences in a step-down hospital unit." in ACM Conference on Embedded Networked Sensor Systems (SenSys), 2010. [2] J. P. Lynch and K. J. Loh, "A summary review of wireless sensors and sensor networks for structural health monitoring," Shock and Vibration Digest, vol. 38, pp. 91-128,2006. (a) Receiving Data Rate Ratio Figure 6. (b) Number of Beacons Impact of Nu mber of Si multaneous Mobile Users We further study the packet loss in the mobile experiments. In our experiments, there were few consecutive packet losses. This means the forwarding path in the network is reliable and the data packets are rarely dropped in intermediate nodes. The reason that receiving data rate decreases might be that the source nodes (i.e., mobile nodes) fail to relay all the packets to the infrastructure nodes. In LRP, the source nodes drop packets in two cases. In the first case, if the source node fails to discover a parent to forward packets, it will not transmit the data packets until the path is set up. In the second case, if the source node already selects its parent but it fails to forward packets even after maximum number of retransmissions. The packet losses at the source node imply that LRP cannot find a good link to forward the packets in a timely manner when the nodes are moving. The performance results show that LRP achieves higher receiving data rate and lower control overhead than DRAP in hybrid WSNs. It convinces us that our design in LRP provides promising strategy to well support sensor data collection from mobile users with high data rates. IV. CONCLUSIONS In this paper we present a design supporting data collection applications with high data rates in hybrid wireless [3] O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis, "Collection tree protocol," in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sen Sys),2009. [4] N. Patwari, J. N. Ash, S. Kyperountas, A. O. H. III, R. L. Moses, and N. S. Correal, "Locating the nodes: cooperative localization in wireless sensor networks," in IEEE Signal Processing Magazine, vol. 22, 2005, pp. 54-69. [5] G. Mao, B. Fidan, and B. D. Anderson, "Wireless sensor network localization techniques," Computer Networks, pp. 2529-2553, 2007. [6] c. wang and L. Xiao, "Sensor localization under limited measurement capabilities," IEEE Networks, vol. 21, pp. 16-23, 2007. [7] P. Levis, N. Patel, D. Culler, and S. Shenker, "Trickle: A selfregulating algorithm for code propagation and maintenance in wireless sensor networks," in Proceedings of the First USENIXIACM Symposium on Networked Systems Design and Implementation( NSDI), 2004. [8] O. Chipara, C. Brooks, S. Bhattacharya, C. Lu, R. Chamberlain, G. catalin Roman, and T. C. Bailey, "Reliable data collection from mobile users for real-time clinical monitoring," in Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, 2009, pp. 397-398. [9] TinyOS. (2011) Tinyos go ogle code project. [Online]. Available: http://code.google.comjp/tinyos-mainl [10] w. Technologies. (2011) Telosb data sheet. [Online]. Available: http://www.willow.co.ukltelosb_datasheet.pdf