Aggregation-Friendly Data Collection Protocol of Wireless Sensor Networks for PoI Monitoring

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1 Aggregation-Friendly Data Collection Protocol of Wireless Sensor Networks for PoI Monitoring Xiaobing Wu,ShenLi, Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University, China Department of Computer Science University of Illinois at Urbana-Champaign, USA Abstract Point of Interest (PoI) coverage problem has been discussed lately. Both practical strategies and theoretical analysis have been proposed. We however argue that coverage solution alone is not enough. In real applications, data from the covered area need to be collected and sent back to the sink. We notice that existing protocols are for general purpose use and not specially designed for PoI monitoring. Accordingly, we propose Smaller tree Collection Protocol () for data collection in this kind of applications. is a distributed approximation for building a minimum Steiner tree connecting all PoI nodes and the sink. aims to activate minimum relay nodes and supports data aggregation better than other collection protocol, e.g., Collection Tree Protocol (), Backpressure Collection Protocol (BCP) and opportunistic routing protocols. We also design low cost distributed mechanisms to handle PoI dynamics. Extensive simulations have been performed to evaluate the proposed protocol. Keywords: Wireless sensor network, PoI routing, collection protocol, distributed Steiner tree. I. INTRODUCTION Data collection is a fundamental functionality for wireless sensor networks. Collection Tree Protocol () [1] [2] is a practical approach which has been used for years and many applications [3] [4] [5] and testbeds [6] [7] have already evaluated its performance. Another approach, Backpressure Collection Protocol (BCP) [8] tries to view the problem from a different standpoint. It was also implemented on real sensor networks with good outcome. Opportunistic routing [9] takes advantage of the broadcasting nature of wireless media. Aggregation, which is another important technique for WSN monitoring applications, has been discussed for many years [13] [14] [15] [16]. The basic idea is to combine packets from different sources into one packet, in order to reduce redundancy and, eventually, save energy. Many researchers have done quite a few works on different aspects of aggregation, including fault tolerance and scalability [14], aggregation architecture [13], network lifetime maximization [16] and etc. Point of Interest (PoI) monitoring scenario needs both collection protocols and aggregation techniques. To the best of our knowledge, PoI coverage was first considered in [10]. In this scenario, instead of the whole area, only a few portion of locations need to be monitored. They use mobile sensors to conduct sweep coverage. Additionally, a single coverage scheme is not enough for monitoring applications. Data collection protocol is also necessary for PoI monitoring applications. Although [1] and BCP [8] have been used in many applications, we argue that they are not suitable for PoI data collection. Since they are designed for general purpose use and could activate too many relay nodes which are precious resources in the application. Data aggregation is not considered either in these two protocols. Therefore data collection protocols using less relay nodes and supporting aggregation are needed for PoI monitoring application. In this paper, we propose Smaller tree Collection Protocol () which aims to use minimum relay nodes and is able to fulfill the PoI monitoring tasks without incurring much unbalanced traffic in the network by means of aggregation. Our contributions are three folds. First, we propose a novel data collection protocol for PoI monitoring applications, which can deal with PoI dynamics with efficient strategies. Second, we theoretically prove that activates no more relay nodes than if the PoI emerges one by one. Third, we have performed extensive simulations to evaluate the proposed scheme. The remainder of this paper is organized as follows. In section II we provide a brief survey of related works. Section III presents our main idea. The detailed design is described in IV. In section V, we show our evaluation results. Finally, we conclude our work in section VI. II. RELATED WORKS Quite a few previous works have addressed the coverage problems in wireless sensor networks, including full coverage and barrier coverage. Liu et al. [10] proposed a new coverage scenario where only certain points of interest (PoI) need to be periodically monitored. Mobile sensors are used to achieve sweep coverage among all those PoIs. Ma et al. [11] propose to employ a steepest descent method to optimize the control parameters for scheduling a mobile sensor s coverage time among a set of PoIs. Their model is based on Markov chain wherein the sensor uses a transition probability to determine next destination. We do not consider mobile sensor in our work. Most existing collection protocols are for general purpose. Collection Tree Protocol () [1] is the most widely used one. They assume expected transmissions (ETX) as the cost /11/$ IEEE

2 metric and a tree topology is employed implicitly. works well in some specific applications [3] [4] [5]. But in PoI monitoring, only sparse spots generate data, a much smaller collection tree is adequate. Moeller et al. [8] consider the same problem in another way. In their Backpressure Collection Protocol, routing and forwarding decisions are made on a per-packet basis. BCP is more agile and can handle local congestions well. However, this agility makes aggregation, an important and useful functionality, hard to be applied. When, where and how to conduct aggregation becomes difficult in BCP. Different from the above two protocols, opportunistic routing [9] chooses a candidate subset rather than a determined node to forward packets. All the candidate are prioritized by distance. But for PoI monitoring cases, minimizing ETX is not the only objective. In our work, we propose Smaller tree Collection Protocol for PoI monitoring, which employs a much smaller collection tree in which aggregations can be easily conducted. Data aggregation in wireless sensor networks has been discussed for years. But it always acts as an auxiliary part. Kalpakis et al. [16] focus on how to maximize lifetime of a WSN. Given the location of both sensors and base station together with energy storage at each sensor, they designed a polynomial time algorithm to find an efficient manner which defined the data collection and transmission strategy. Li et al. [17] find that correlations between data generated by different sensors can help to reduce data gathering cost. They thus employ a compression tree and use conditional distribution to compress observed value. Joo et al. [18] analyze the performance of optimal scheduling with in-network aggregation in tree-topology networks while aiming at minimizing sum delay of sensed data. In [13], Zhao et al. describe an architecture for sensor network monitoring, which consists of three classes of software, namely continuously collects aggregates, scans and dumps. They also designed protocols to continuously compute network digests which help to implicitly build a energyefficient aggregation tree and showed the impact of packet loss on the aggregation performance. Gupta et al. [14] design a gossip strategy to calculate global aggregate functions over the votes of individual group members. Aggregation is better supported in our proposed protocol since the generated tree has more convergence points. III. OVERVIEW Different from traditional WSN routing protocols, our approach does not aim at serving each individual node with a minimum expected transmission (ETX) path. We consider a task-intensive scenario where the wireless sensor nodes are highly loaded such that the nodes themselves are precious resources. Accordingly, aims to build a collection tree with minimum active relay nodes. Meanwhile it does not increase the load on each sensor to a great extent with data aggregation. A. Assumptions We assume that the sensors in the field are evenly deployed and there are approximately the same number of sensors in each unit area. Points of interest (in the form of events that the system is interested in) can emerge and disappear in random. The sensors are able to detect events occurred near them. We call the sensor closest to one point of interest a PoI agent. The PoI agents use a routing path to relay data to the sink node. B. Approach to Build Data Collection Tree The PoI monitoring problem can be modeled as a Steiner tree problem. Let G = {V,E} represent the whole WSN, where V and E denote the set of nodes and links respectively. Each edge e(u, v) represents a neighboring pair u and v, where they can both hear each other. The subset U V consists of all PoI agents covering all PoIs. The minimum Steiner tree T is thus the data collection tree we try to build. However, it is well known that minimum Steiner tree problem is an NP-hard problem [12]. Most of the known Steiner tree approximation algorithms are too complicated and call for heavy calculation, which is not practical to be implemented into distributed sensor networks. Thus, we propose, which is an aggregation-friendly data collection protocol for point of interest applications of WSNs. The basic idea of is that we treat every active node in WSN as a virtual sink. We call a node active when it is awake and can be selected as a relay for data reception and transmission. This method allows a new PoI agent connects to the nearest virtual sink (an active node) which might be much closer than to the real sink. In this way, the number of active nodes are reduced because different agents might connect to the same virtual sink. Whenever a new PoI agent joins the collection tree, all the nodes on its path towards its virtual sink are activated. Then they can serve as virtual sinks for future PoI agents. Actually, as shown in Fig. 1, all the virtual sinks on our collection tree divide the whole network into a Voronoi diagram due to the fact that every PoI agent connects to the nearest active node. Accordingly, when a new PoI emerges, the new routing path for its agent is limited inside the Voronoi cell covering the PoI. Another problem has to be handled is the PoI dynamics. According to the assumption, PoIs may emerge and disappear. The PoI agent and related active relay nodes might turn to dormant due to the disappearance of a PoI, and they cannot be virtual sinks before be activated again. Thus, their Voronoi cells should be disabled and the nodes inside the original cells need new routing information. We denote the disabled Voronoi cell a hole. There is backup route option provided in design. In specific, we keep two routing information on each node from and respectively. Whenever the routing decision for is out-of-date, the node starts to use the next hop provided by. It is worth noticing that the routing decisions apply only to those node inside holes. When a data packet is forwarded into an active Voronoi cell, takes over routing decision again. As time goes by, new PoI

3 Fig. 1. Voronoi Diagram : all virtual sinks divide the WSN into different Voronoi cells. Fig. 2. Collection tree generated by. Circles are PoI agents and triangle is the sink. emergence and periodical beacon broadcasting by the virtual sinks can help to fill up holes. IV. PROTOCOL DESIGN Since the PoI can emerge and disappear in random, we need handle the PoI dynamics in the protocol design. The emergence and disappearance of the PoIs brings holes. Although nodes inside disabled areas have an alternative routing option, it undermines the global efficiency. We now elaborate the details of protocol in four parts: Network Initialization, Handling PoI Emergence, Dealing with PoI Disappearance, Beacon Broadcasting and Data Aggregation Support. A. Network Initialization Our approach requires each node maintain two different forwarding parents and two ETX items for and seperately. The sink node first broadcasts a control beacon which contains three data fields and two control bits. The three data fields store ETX (ETX ) information, ETX (ETX ) information and the ID of the sender. The control bits are virtual bit (V) and quit bit (Q). The Q bit is used when an active node turns into dormant. We present its usage in the PoI Disappearance section. The V bit denotes whether one beacon is initialized by a virtual sink. During network initialization, all packets are initialized by the real sink and virtual bit is set to 0. When receiving this beacon, the receiver compares the new ETX inside the beacon with its own. If the new ETX is at least 1 lower, then the receiver uses the sender ID and ETX to update its parent and ETX field. Then, this node informs neighbors by broadcasting its ETX and node ID. In this way, a static data collection tree is built and maintained in the network. The ETX is set to the same as ETX in this phase. A node needs to update expected transmission only when it hears a beacon whose V bit is 1 and carries a lower ETX, i.e., this beacon indicates a shorter path towards a virtual sink. Fig. 3. Collection trees generated by. Circles are PoI agents and triangle is the sink. B. Handling PoI Emergence In, the PoI agent connects itself to the nearest virtual sink when a new PoI appears. Fig. 2 and 3 present the collection trees generated by and respectively. It is obvious that a much smaller data collection tree is used in, compared to the tree used in. Therefore is more suitable for task-intensive scenarios where every node is highly loaded and data collection trees with small sizes are needed. When a node is activated after the path towards some virtual sink is built, it starts to broadcast a beacon whose ETX field is set to 0 and V bit set to 1. If the ETX in the beacon is smaller than receiver s ETX, the receiver will use this beacon to update its own information, increase the ETX field by 1 and forward this beacon to further nodes. Otherwise, if the ETX inside the beacon is greater, the receiver just simply drops it. So, turning on one node affects only sensors inside the same Voronoi cell. We also have the following theorem regarding to the number of relays used in data collection trees by and. Theorem 4.1: activates no more relay nodes than does when PoI emerges one by one. Proof: We prove this theorem by contradiction. Suppose connecting a new PoI agent to the sink activates less relay nodes than. Let G(V,E) denote the whole network where V and E represent the set of all nodes (b)

4 and set of all available links respectively. Let Path(u, v) denote a sequence of available links connecting u and v. Let Len(p) represent the number of links on the path p. Wehave Len(Path min (u, v)) = Min{Path(u, v)}. We define set of active nodes as ACT = {v v V v is active}. Suppose the agent of the new PoI is s and its ETX (s) = Min{Len(Path min (s, v)), v ACT }. Let AN C(s) be the set of ancestors of s on the collection tree. If s is connected to its nearest active ancestor, this action will turn on L anc nodes, where L anc = Min{Len(Path min (s, a)), a ACT ANC}. According to the assumption, L anc < ETX (s). However, ETX (s) =Min{Len(Path min (s, v)), v ACT } = Min{Min{Len(Path min (s, v)), v (ACT ANC)},Min{Len(Path min (s, v)), v ACT ANC}} = Min{Min{Len(Path min (s, v)), v (ACT ANC)},L anc } L anc <ETX (s). We get ETX (s) <ETX (s), which is impossible. Thus, the assumption does not hold, i.e., always uses the same or smaller number of relay nodes than to build a collection tree. C. Dealing with PoI Disappearance It is very clear that when a PoI disappears, the agent should disable its Voronoi cell. In order to achieve this, the agent simply broadcasts a control beacon whose Q bit is set to 1 (quit beacon) and node ID field is set to the agent s node ID. As to a receiver, if its own parent (the predecessor on the path) is the same as the node ID in the beacon, the receiver broadcasts another quit beacon with its own ID inside. In this way, ETX of all nodes in the Voronoi cell are disabled. When a PoI emerges in this cell again, the routing path is used. But different from the initial path, we only activate the segment inside the same Voronoi cell. For nodes outside, whenever their parents are still available, the routing path is used. Besides disabling PoI agent, related relay nodes need quit the data collection tree as well. For an illustration, as in Fig.??(d), if the upper-right PoI disappears, both its agent (the gray node) and all of the nearest 5 ancestors should quit. It is possible that some intermediate ancestor quit earlier and the downstream nodes cannot receive the quit beacon and does quit eventually. Here, we present a simple distributed algorithm to deal with this situation. The pseudo codes are given in Algorithm 1. The basic idea of Algorithm 1 is to quit the ancestors one by one until control beacon arrived at the nearest branch on data collection tree. The branch node is parent of more than one PoI agents. Thus, the path above this node should be reserved. D. Beacon Broadcasting The two sections above describe actions when a PoI agent joins or leaves the collection tree. However, since the PoI set changes dynamically, a long path might be used to connect a Algorithm 1: PoI Quit Input: Control beacon (P), Children list (C), Node ID (ID) C.cnt = C.cnt - 1; if C.cnt > 0 then C.remove(P.ID); Drop P; end else P.ID = ID; Broadcast P; end C.cnt the number of items in the children list. C.remove(v) remove v from C. P.ID return the sender ID inside the control beacon. PoI agent to the sink. As shown in Fig. 4, dashed squares and circles are previous agents and activated nodes respectively. They were in the same collection tree before. But, after their departure, current PoI agent still use previous path for data collection, which is longer than the path formed by gray circles. Another problem is that, each time when an active node leaves, an hole appears. Although the newly-activated sensors will help to fill it up, it depends on the location of the newly-emerged PoI agents. Periodic beacon broadcasting is used in to reshape the collection tree. This beacon is launched by the virtual sinks and the real sink and broadcasted among the whole network. On receiving the first beacon, every sensor sets parent into its parent and broadcasts to inform other nodes. When the sensor heard later beacons, it just simply drops it. As to PoI agents, they randomly pick a back-off interval. And when the timer fired, the very agents start to join the collection tree as described in the PoI emergence section. u Active Path in Use Sink v Previous PoI w Better Active Path Current PoI Fig. 4. Excessively Long Path. Pink nodes are previous PoI agents. As they quit from the Collection tree, the routing path for the current PoI agent becomes excessively long.

5 E. Data Aggregation Support For some aggregation functions, e.g., MAX, MIN, COUNT, SUM and etc., can significantly save the volume of packets. Because no matter how large a subtree is, the root node just receives one packet. In fact, all aggregates which has the property of decomposability [14] [15] can be supported in. Function f is decomposable by a function g if it can be formulated as: f(v 1,,v n )=g(f(v 1,,v k ),f(v k+1,,v n )) Collection tree size V. EVALUATION Simulations are performed under situation where information is not decomposable, which is the most unfavorable scenario for. We have examined the performance of in terms of collection tree size and packets volume under both static and dynamic environments. Here, being static means PoI emerges one by one and will not disappear. We have compared the results of with and BCP (when it is applicable). For all the simulations below, we employ the disk model and the communication radius is set to 70 meters. The density of WSN is 1/70 which means there is one sensor every 70 square meters on average. The nodes and PoIs are randomly deployed. As to BCP, since its initialization step generates many data packets, we use the ETX to initialize its floating queue [8]. It does not restrict BCP to obey a topology even though ETX implies intuition. Since data packets are continuously generated, the queue size changes overtime and it does not rely on the infrastructure. A. Collection Tree Size We consider a scenario where a collection tree with small size is prioritized. We first look at how the collection tree grows with the increasing network size. Since the WSN is randomly deployed, we conduct five experiments with the same parameter configuration for both and. As shown in Fig. 5(a), the number of PoI agents are set to 30 for all cases and we can see that collection tree grows much slower than does. For the case where the WSN consists of 100 nodes, almost use only PoI agents themselves to build collection tree. Fig. 5(b) shows the performance when network size is 500 and the number of PoI agents grows. The squared line is for reference purpose and indicates the number of PoI agents. We notice that no matter how number of PoIs increases, constantly uses about 20 extra sensors for data collection. This indicates that performs quite well in terms of collection tree size. B. Performance of dealing with PoI Dynamics Result of dynamic situations are shown in Fig. 6. We define an action PoI shift as an event that a previous PoI disappears and a new one emerges. Thus, PoI shift does not change total number of PoI agents. Simulation is conducted with 500 sensor nodes in total and 30 of them are PoI agents. We let and walk through 450 PoI shifts. For, the interval of periodical beaconing is set to 60 PoI shifts. From the result Collection tree size Number of active nodes Network size POI (a) Number of POIs Fig. 5. (b) Collection Tree Size Number of POI shifts Fig. 6. Dynamic Collection Tree size we can see that use much less active nodes than and the number of activated nodes fall down apparently after each beaconing. C. Overall Number of Packets in the Network Another advantage of is that it can save overall number of packets on the network. During experiment, we let our system runs for 10 minutes in each scenario and count all the packets sent in the network. For both simulations, 30 PoI agents are deployed randomly. Each PoI agent generates one packet every minute and we assume that all links are perfect, i.e., sensors do not need to retransmit any data packet or control beacon. In Fig. 7, it is obvious to see that BCP uses

6 Data packets BCP does not raise much traffic load on each individual node. Simulation results show that is not only feasible in both static and dynamic scenarios, but also outperforms and BCP in PoI applications when assigns higher priority to smaller collection tree in the network. Fig Network size Fig. 7. Data Packets Overall number of packets as network size grows BCP Number of POI shifts Overall number of packets in each interval under dynamic situation much more packets to collect information from all PoI agents. It can be explained in this way. As BCP has no determined topology, it is hard to employ any aggregation strategy, i.e., each packet can just carry data for one PoI agent. We can also see that the overall cost of is lower than, which verifies that supports data aggregation better. Fig. 8 shows the overall number of packets in, BCP and in each interval under dynamic situations. Each PoI agent generates one data packet every 10 minutes. And there is one PoI shift each hour. The lines display the number of packets between every two PoI shifts. BCP performs the worst as it is hard to conduct aggregation strategy. Although activates less relay node, its total packets is the same as, if not less than, s. VI. CONCLUSION We present the design and evaluation of, a data collection protocol for PoI monitoring. The key idea of is to connect a new PoI agent to the current collection tree rather than the real sink node in order to activate less relay nodes due to the fact that in PoI applications, only a small portion of sensors generate data. By employing aggregation techniques, ACKNOWLEDGMENTS The work is partly supported by China NSF grants ( , , ). REFERENCES [1] O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis, Collection tree protocol, ACM SenSys, November [2] R. Fonseca, O. Gnawali, K. Jamieson, S. Kim, P. Levis, and A. Qoo. TEP 123: The Collection Tree Protocol, Augest [3] M. Bathula, M. Ramezanali, I. Pradhan, H. Patel, J. Gotschall, and N. Sridhar. A sensor network system for measuring traffic in shor-term construction work zones. DCOSS 2009, Berlin, Heidelberg, [4] J. Ko, T. Gao, and A. Terzis. Empirical study of a medical sensor application in an urban emergency department. In BodyNets [5] S. Li, A. Zhan, X. Wu, and G. Chen, ERN: emergence rescue navigation with wireless sensor networks, IEEE ICPADS, Shenzhen, China, [6] B. N. Chun, P. Buonadonna, A. AuYoung, Chaki Ng, D. C. Parkes, J. Shneidman, A. C. Snoeren, and A. Vahdat, Mirage: a microeconomic resource allocation system for sensornet testbeds, The 2nd IEEE workshop on Embedded Networked Sensors, April-Ma, [7] G. W. Allen, P. Wieskowski, and M. Welsh, MoteLab: a wireless sensor network testbed, The 4th international symposium on Information processing in sensor networks, Los Angeles, California, April [8] S. Moeller, A. Sridharan, B. Krishnamachari, and O. Gnawali, Routing without routes: the backpressure collection protocol, ACM IPSN, April [9] S. Biswas and R. Morris, Opportunistic Routing in Multi-Hop Wireless Networks, ACM SIGCOMM, August [10] W. Cheng, M. Li, K. Liu, Y. Liu, X. Li, and X. Liao, Sweep coverage with mobile sensors, IEEE International Parallel and Distributed Processing Symposium, pages 1C9, March [11] C.Y.T.Ma,D.K.Y.Yau,N.K.Yip,N.S.V.Rao,andJ.Chen, Stochastic steepest-descent optimization of multiple-objective mobile sensor coverage, IEEE International Conference on Distributed Computing Systems, Genoa, Italy, June [12] D. S. Hochbaum, Approximation algorithms for NP-hard problems, PWS Publishing Company, Boston, MA, [13] J. Zhao, R. Govindan, and D. Estrin, Computing aggregates for monitoring wireless sensor networks, IEEE Sensor Network Protocols and Applications, [14] I. Gupta, R. van Renesse, and K. Birman, Scalable fault-tolerant aggregation in large process groups, International Conference on Dependable Systems and Networks, [15] S. Madden, M. Franklin, J. Hellerstein, and W. Hong, TAG: a tiny aggregation service for ad hoc sensor networks, USENIX symposium on Operating Systems Design and Implementation, [16] K. Kalpakis, K. Dasgupta, and P. Namjoshi, Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks, Computer Networks, 42(6): , August [17] J. Li, A. Deshpande, and S. Khuller, On computing compression trees for data collection in wireless sensor networks, IEEE INFOCOM, San Diego, USA, [18] C. Joo, J. G. Choi, and N. B. Shroff, Delay performance of scheduling with data aggregation in wireless sensor networks, IEEE INFOCOM, San Diego, USA, 2010.