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Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo, ND 58105 USA {Xiaojiang.Du, Fengjing.Lin}@ ndsu.edu Abstract Wireless sensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. So far, the main research focus has been on routing, media access control, security, etc, while less emphasis was placed on network management aspect. Designing an application layer management protocol for sensor networks has several advantages. Sensor networks have many application areas, and the management protocol should utilize the features in different applications. Sensor nodes have limited energy supply from battery, thus energy efficiency is very important for sensor networks. In this paper, we propose an energy management protocol for target tracking sensor networks. The protocol exploits the features of target tracking applications to allow sensor nodes that are far away from targets go to sleep and save lots of energy while guarantee the accurate tracking of targets. We also propose a routing protocol that cooperates very well with the energy management protocol. Extensive simulation experiments show that the energy management protocol plus the routing protocol save significant amount of energy while at the same time achieve high accuracy tracking and high data delivery ratio. Keywords Sensor networks, network management, energy efficiency, target tracking 1. Introduction Recent advances in microprocessor and wireless communication technologies have enabled the deployment of large scale sensor networks where many low-power, low- cost small sensors are distributed over a vast field to obtain fine-grained, high- precision sensing data. These sensor nodes are typically powered by batteries and communicate through wireless channels, and are usually scattered densely and statically. Sensor networks can be used in many application areas such as military surveillance, environmental monitoring and target tracking. In sensor networks, a source is defined as a sensor node detecting a target and generating data to report the 0-7803-9087-3/05/$20.00 ©2005 IEEE

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Page 1: [IEEE 2005 9th IFIP/IEEE International Symposium on Integrated Network Management, 2005. IM 2005. - Nice, France (15-19 May 2005)] 2005 9th IFIP/IEEE International Symposium on Integrated

Efficient Energy Management Protocol for Target Tracking Sensor Networks

X. Du, F. Lin Department of Computer Science North Dakota State University Fargo, ND 58105 USA {Xiaojiang.Du, Fengjing.Lin}@ ndsu.edu

Abstract Wireless sensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments. So far, the main research focus has been on routing, media access control, security, etc, while less emphasis was placed on network management aspect. Designing an application layer management protocol for sensor networks has several advantages. Sensor networks have many application areas, and the management protocol should utilize the features in different applications. Sensor nodes have limited energy supply from battery, thus energy efficiency is very important for sensor networks. In this paper, we propose an energy management protocol for target tracking sensor networks. The protocol exploits the features of target tracking applications to allow sensor nodes that are far away from targets go to sleep and save lots of energy while guarantee the accurate tracking of targets. We also propose a routing protocol that cooperates very well with the energy management protocol. Extensive simulation experiments show that the energy management protocol plus the routing protocol save significant amount of energy while at the same time achieve high accuracy tracking and high data delivery ratio.

Keywords Sensor networks, network management, energy efficiency, target tracking

1. Introduction Recent advances in microprocessor and wireless communication technologies have enabled the deployment of large scale sensor networks where many low-power, low-cost small sensors are distributed over a vast field to obtain fine-grained, high-precision sensing data. These sensor nodes are typically powered by batteries and communicate through wireless channels, and are usually scattered densely and statically. Sensor networks can be used in many application areas such as military surveillance, environmental monitoring and target tracking. In sensor networks, a source is defined as a sensor node detecting a target and generating data to report the

0-7803-9087-3/05/$20.00 ©2005 IEEE

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conditions of the target; a sink is defined as an end user or a base station that collects data from the sources. For a large sensor network, multi-hop data forwarding is typically used to reach a distant destination.

In recent years, many researchers have been studying sensor networks. Most existing researches focus on issues in network layer, media access control layer and physical layer. Although many application areas for sensor networks are defined and proposed, potential application layer protocols for sensor networks remain a largely unexplored region. Designing an application layer management protocol has several advantages. Sensor networks have many different application areas, which have different features and properties.

In [5], the authors proposed Sensor Query and Tasking Language (SQTL) as an application protocol that provides a set of services. SQTL supports three types of events, defined by keywords receive, every, and expire. Receive defines events generated by a sensor node when the sensor node receives a message; every defines events occurring periodically due to timer timeout; and expire defines events occurring when a timer is expired. However, SQTL does not address an important issue in sensor networks – energy efficiency.

Sensor nodes usually are equipped with batteries with limited power. Moreover, it is impractical or infeasible to replenish energy via replacing batteries on these sensors in most applications. Thus energy efficiency is particularly important for sensor networks. In a multi-hop ad hoc sensor network, each node plays the dual role of data originator and data router. The malfunctioning of a few nodes can cause significant topological changes and might require rerouting of packets and reorganization of the network. Hence, power conservation and energy management take on additional importance. Several energy saving algorithms have been proposed for sensor networks. In [1], Ye et al. presented PEAS - a probing-based sensing coverage algorithm. In this work, after a sleeping node wakes up, it broadcasts a probing message within a certain range and waits for a reply. If no rely is received within a timeout, it will start to operate until it depletes its energy. However, this probing-based approach has no guarantee on sensing coverage and blind points can occur. Tian et al. [2] proposed an algorithm that provides complete coverage using the concept of “sponsored area”. Whenever a sensor node receives a packet from one of its working neighbors, it calculates its sponsored area (defined as the maximal sector covered by the neighbor). If the union of all the sponsored areas of the sensor node covers the whole disk covered by it, the sensor node turns itself off. In [3], Zhang and Hou showed that coverage with minimal overlap is achieved when three sensor nodes form an equilateral triangle, and they proposed a localized density control algorithm OGDC based on the result.

All these algorithms provide uniform (the same) coverage across the network. However, the above works did not consider the knowledge of application during the algorithm design. E.g., for monitoring task, every point in the field should be covered with certain degree. While for target tracking, only the area around the target should be covered with the required degree, and other area can have much lower coverage. In many applications, multiple sensors are needed to perform the sensing task, which means high coverage degree is required. Maintaining high coverage degree for the

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whole field would waste significant amount of energy. Thus, for some applications (like target tracking), it is more efficient to maintain high coverage only for areas around the interesting events.

In this paper, we present a novel energy management protocol for sensor networks – Sensor Network Energy Management (SNEM) protocol. SNEM is particularly designed for target tracking applications. The main difference between SNEM and other energy saving algorithms is that SNEM utilizes application knowledge and only provides high coverage for areas around the target, while other algorithms provide uniform coverage for the whole network.

2. Network Model In this Section, we introduce the network model used to design and analyze the energy management protocol. We assume the sensor field is a two-dimensional square region F with side length L, and totally n nodes are randomly and uniformly distributed in the field. It is well known that n nodes whose locations are mutually independent random variables, each with uniform distribution in F, are essentially a Poisson point process with density λ = n/ 2L if F is large [10]. We assume the deployed sensor nodes in region R form a (homogeneous) Poisson point process with density λ. There are several ways of defining a Poisson point process, one of which is stated below. First, for any subset A of the region R, the distribution of the number of nodes in the set is Poisson with mean λ||A||, where ||A|| is the area of A. Second, given that the number of nodes in such a set A is m, the node locations in A are m mutually independent random variables, each uniformly distributed over A.

We denote the sensor nodes as, and use iβ to denote the location of node i, for

1 i n≤ ≤ . Denote the sensing range of a sensor node as r. Then the covered area of a sensor node i is the disc of radius r and centered at

iβ , i.e.,

( ) { : , }i iC r x x r x Fβ= + ≤ ∈ . Thus, the sensing coverage of the network field can be

defined as a sequence of random discs, denoted as { : , ,1 }iC x x r x F i nβ≡ + ≤ ∈ ≤ ≤ . C is referred to as a coverage process by the set of n discs.

In SNEM, the entire sensor network region R is divided into several small, equal-size squares -- cells. All the cells form a grid in the network, as illustrated in Figure 1. The network in Figure 1 is divided into 9 cells. The grid structure is fixed even the target or sink may move around. If the sensing range of a sensor node is r, then the side length of each cell is set as a = r. Since the sensor node deployment is considered as a Poisson point process, the number of nodes in each cell is Poisson with mean

2aλ . Assume the sensor node transmission range is R. Most sensor networks have the double range property [24], i.e., / 2R r ≥ . With this property, in most cases sensors in two neighbor cells can directly communicate with each other. Note: / 2 2R a > guarantees the connection between sensors in two neighbor (including diagonal) cells.

We designed the SNEM protocol and the corresponding routing protocol based on the following assumptions: (1) We consider sensor nodes are static, and each sensor node is aware of its own location. There are several ways to provide sensor location information. The network can use location services such as [6], and [13] to

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estimate the locations of the individual nodes, and no GPS receiver is required at each node. Note that our SNEM protocol does not require very accurate node locations. (2) For most cells, there is at least one sensor node in each cell. Since sensor networks usually are dense and have a large number of nodes, this is true for most sensor networks.

Figure 1: The grid structure

3. The Sensor Network Energy Management Protocol In this Section, we present the Sensor Network Energy Management (SNEM) Protocol. In order to design protocols and algorithms that can save energy for sensor networks, first we need to understand where the energy is spent. The main task of a sensor node in a sensor field is to detect events, perform quick local data processing, and then transmit the data. Power consumption can hence be divided into three domains: sensing, communication, and data processing. SNEM mainly considers how to save energy in communication domain. Many studies and measurements have shown that node idle listening consumes 50–100% of the energy required for receiving. For example, Stemm and Katz measure that the idle:receive:send ratios are 1:1.05:1.4 [15], while the Digitan 2 Mbps Wireless LAN module (IEEE 802.11/2Mbps) specification shows idle:receive:send ratios is 1:2:2.5 [14]. In many sensor network applications, nodes are in idle mode for most of the time if nothing is sensed. This is a major source of energy waste. The idea of SNEM is to let nodes that do not perform sensing task go to sleep and thus save significant amount of energy. Sensor networks have many applications, and different energy saving schemes should be designed for different applications. SNEM is particularly designed for target tracking sensor networks.

Tracking is an important and widely-used application for wireless sensor networks. In military, sensor networks can be used to detect and track enemy troops and tanks. In civilian life, sensor networks can be used to track the movement of wild

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animals. We design an energy management scheme for target tracking sensor networks. This scheme lets most sensors go to sleep while only the sensors near the moving target keep active. The scheme saves energy for sensor nodes while at the same time keeps tracking the moving target and sends the target information to the sink. The sensor energy management scheme is presented in the following.

In target tracking applications, the interesting events happen infrequently with long intervals of inactivity. Most sensor nodes can go to sleep during the inactivity period, while only a small number of nodes stay alert to detect the presence of the target. Thus the network operations have two stages. The first is the watching stage during which no target is present in the field. The second is the tracking stage during which sensor nodes track the moving target. We will first discuss the energy saving scheme during tracking stage, and we will talk about the scheme during watching stage at the end of the Section.

As discussed in Section 1, a sensor network is divided into several cells. Each cell has a unique coordinate (x, y). The cells in a sensor network are classified into two types: Target Neighbor (TN) cells and relay cells. TN cells are the cells near the current location of (or including) the target T. As shown in Figure 2(a), the circle is the target, and all the neighbor cells (including the cell having the target) are TN cells. Other cells (that are not close to the target) are called relay cells. Nodes in TN cells need to perform sensing task – detecting target location, speed, target type, etc, and these sensor nodes are referred to as source. Nodes in relay cells only need to forward data from source to sink. When the target moves, the TN cells change. Figure 2(b) shows that the TN cells change as the target moves.

TN TN TN

TN

TN

TN TN TN

1,2 2,2 3,2 4,2 5,2

1,1 2,1 3,1 4,1 5,1

1,5 2,5 3,5 4,5 5,5

TN TN TN

TN

TN

TN TN TN

1,1 2,1 3,1 4,1 5,1

Fig 2(a): TN cells Fig 2(b): Moving target and changing TN cells

Sensor nodes have limited power supply from battery. Energy efficiency is very important for maximizing lifetime of sensor networks. To save energy, sensor nodes should go to sleep whenever possible. Consider the sensor nodes in the two types of cells - TN cells and relay cells. For a relay cell, only one sensor node needs to be awake to maintain the connectivity and forward data from source to sink, while other nodes can go to sleep and save energy. For a TN cell, in order to perform accurate sensing task, all the sensor nodes should be active. This is the main idea of our energy management protocol.

All nodes in TN cells are active and perform sensing, signal processing and data aggregation. Each sensor node stores two state variables: (1) Cell status – indicating

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the status of the cell (TN or relay); (2) Active time – the time to keep the node active. Only one node in a relay cell is active, while all other nodes in the relay cell are in sleep mode. Sleeping nodes periodically awake after every at second. at is a system parameter and it depends on the size of the cell – a , and the maximum speed of the target - tv . The maximum speed of a target usually is available. For example, the maximum speed of tanks or certain animals are known. Then at can be set as a/ tv , i.e., the minimum time for the target to cross one cell. The active node in a relay cell is referred to as Relay Point (RP). When the status of a cell changes from relay to TN, the RP will periodically broadcast a cell status update message to all nodes in the cell for every bt seconds. When a sleeping node awake, it will keep active for bt seconds. The broadcast will repeat for /a bt t times, where /a bt t is the least integer greater than /a bt t . This is to ensure that every node in the cell receives at least one cell status update message. If a cell status update message is received, the nodes will keep active and prepare for possible sensing tasks. Otherwise the node will go to sleep again for another at second.

If the target moves very fast, the at could be very small. Then sensors will have to wake up after only a short sleep. Since there is a fixed energy cost to active sensors from sleep mode, the sensor sleep time should not be too small. To deal with fast moving target, the following enhanced SNEM scheme is proposed. For fast moving target, the Target Neighbor cells include not only the direct neighbor cells, but also include the cells two step (or even several steps) away from the target. For example, all the cells shown in Figure 2(b) are TN cells when two-step neighbors are included. Then the sensor sleep time at can be set as 2a/ tv . This enhanced scheme can handle fast moving target.

Assume at the beginning of deployment, each sensor node has initial energy H. Several rotate points are selected to let different nodes serve as the RP in turn. For example, the rotate point can be the time when the RP’s energy is 2H/3, H/3, 0.01H (or a small value close to 0). When the current RP (say A) uses about 1/3 of its energy, A will include its remaining energy and a retiring indication in a broadcast message. When another node B is awake and finds out it has more remaining energy than A, it will send a take-over message to A. Then A can go to sleep and B becomes the new RP. The above approach allows sensor nodes rotate to serve as the RP, and balance the energy among nodes.

When the target moves, the TN cells change. We will use Figure 2 to illustrate the update of cell status when target moves from one cell to another. In Figure 2, the target moves from cell (2, 4) to cell (3, 3), and the TN cells change as shown in Figure 2(b). When the target moves cross the cell border and enters cell (3, 3), the node in cell (3, 3) (that first detects the target) broadcasts a cell status update message to nodes in the nearby cells of cell (3, 3), in the example are cell: (2, 2), (2, 3), (2, 4), (3, 2), (3, 3), (3, 4), (4, 2), (4, 3), & (4, 4). All nodes in these cells will change the cell status to TN (if the status was relay) and increase the active time by one period at . If

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the new TN cell was a relay cell, then when the sleeping nodes wake up, they will receive a cell status update message from the active node and know that the cell status has changed to TN, and the nodes will keep active and prepare possible sensing activities. Some of the previous TN cells, like (1, 5), (2, 5), (3, 5), (1, 4), (1, 3) will become relay cells when the target is out of range (e.g. moved to cell (3, 3)). Their active time decreases to zero after the previous period expires, and the nodes will change the cell status to relay. When the cell status becomes to relay, nodes will broadcast a Relay Point (RP) message to other nodes in the same cell with a delay

( ) /d rt t E tα= + , where E is the remaining energy of the node, and ( )tα is a system parameter, and it is a decreasing function of the local time t in the node. rt is a small random back off time (compared to ( ) /t Eα ). The node that first sends out a RP message becomes the RP node in the cell, when other nodes hear a RP message, they will not send RP messages again and just go to sleep.

The idea is to let one of the nodes with more remaining energy to become the RP node. The delay ( ) /d rt t E tα= + includes two parts. The first part ( ) /t Eα is to ensure that a node with more remaining energy in a cell serves as RP. Since sensor networks usually have a large number of nodes, there might be several nodes with similar remaining energy E. In order to avoid the concurrent transmissions of RP message, a small random back off time rt is added to the delay. ( )tα is chosen to be large enough so that the transmission of RP from different nodes will not overlap, but

( )tα should not be too large since this may cause large delay. And since the remaining energy of sensor nodes decreases with time, ( )tα is a decreasing function of time t. This avoids long delay when all the nodes do not have much energy left. Note that local time t does not need to be synchronized.

By adopting the above sensor node sleeping scheme, only nodes in Target Neighbor cells plus one node in each relay cell are active, while most nodes in relay cells are in sleep mode. Since there are only 9 TN cells at any time (for one target) and usually a sensor network has lots of cells, the proposed SNEM saves significant amount of energy for sensor nodes and dramatically increases sensor network lifetime. In addition, nodes in a relay cell alternatively serve as the active node RP based on the remaining energy, and this balances the energy consumption among different sensor nodes, which prevents some nodes die out too soon and partition the network.

When sensor nodes are in the watching stage during which no interesting events happen, only one active node is maintained in each cell, while all other nodes are in sleep mode. The sensor nodes in each cell rotate to serve as the active node. The network operation is divided into several rounds with T seconds for one round. Nodes can figure out the number of sensors in the cell based on location information. Assume there are m nodes in a cell, then each round is further divided into m equal slots, and each node is assigned with one slot. A node becomes active during its own time slot, while all other nodes go to sleep. All the sleeping nodes wake up at the end of a round and stay active for a short time period for possible messages. If the active node detects a target, it will broadcast cell status update messages two or three times

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during the node wake-up time. When the sleeping nodes wake up and receive the message, they will remain active and perform tracking task. Otherwise, only the designated node becomes active while all other nodes go to sleep again. Only lose synchronization is needed for nodes in the same cell. At the end of each round, the last active node broadcast a synchronization message including its local time, and all other nodes can synchronize their time with the local time.

4. The Routing Protocol Several routing protocols have been developed for sensor networks, such as Directed Diffusion [4], Leach [12] and Mesh [7]. However, these routing protocols do not work well with the Sensor Network Energy Management protocol. Since in SNEM, only one node in each relay cell is active, and most routing protocols do not work well when there is only one active node in a cell. For example, Mesh deploys multiple nodes to form a mesh and forward data packets, while Directed Diffusion uses several nodes to distribute interests. A routing protocol that jointly considers the node sleeping scheme in the application layer and data dissemination in network layer is needed. We propose the following routing protocol that integrates SNEM and data dissemination together. First we introduce the concept of routing cells.

Based on the location of source and sink, a serial of cells that need to participate in routing are determined. These cells are in the direction from source to destination, and are called routing cells. Consider the network in Figure 1, assume the source is node A and destination is B, a straight line L is drawn between the geographic centers of cell C0 and cell C2. And the cells with which line L intercepts are the routing cells – C1 in this example. The routing protocol is presented below. 1. When a source node R wants to send data to a sink S, first R determines the

routing cells, based on the location of R and S. A line L is drawn between the geographic cell centers of node R and S. The line L will intercept with several cells, and these cells are denoted as C0, C1, C2,…, Ck starting from the cell with source node R. R records the routing cells in a cell_list field, which is stored in the header of the data packet. The header contains the following fields: session_id, source_id, sink_id and cell_list. session_id plus source_id uniquely determines a data transmission session.

2. Then the data packet is sent from source node R to cell C1. Contention-based mechanism is used in MAC layer, e.g., CSMA/CA or IEEE 802.11. First, a RTS (Request To Send) is broadcast to neighbor nodes, and there is a next_cell field in the RTS packet. The next_cell refers to the next cell to receive the data packet. For the RTS from node R, the next_cell is C1. Based on the next_cell field, only the nodes in cell C1 will response to this RTS packet. If cell C1 is a relay cell, then there is only one active node (assume R1) in C1. Node R1 will reply a CTS (Clear To Send) packet to node R and become the relay node. If the cell C1 is a TN cell, then one of the available nodes (not performing other tasks like sensing at that time) will become the relay node. Nodes in TN cell C1 send a CTS packet to R with a delay of ( ) /d rt t E tα= + , where dt has similar meaning as in Section 3 but may have different parameters. Assume node R1 is the first node

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that sends CTS back to node R. Other nodes in cell C1 will not send CTS when they overhear the transmission of the CTS. Then R sends the data packet to node R1. And R1 becomes the relay node in cell C1.

3. Node R1 sets the next_cell field as C2 (based on the cell_list field in the packet) and sends the data packet to a node R2 in cell C2. To guarantee the delivery, each relay node is responsible for confirming that its successor has successfully received the packet. This may be implemented by the transmitter monitoring the packet just sent out to next node and overhearing if that node has passed it on within a time period. Of course, if link level acknowledgement is supported by the MAC layer protocol (for instance, 802.11 has such function), the above passive acknowledgement scheme is unnecessary [9]. The transmitted data packet has to be kept in the buffer before its receipt has been confirmed. The acknowledgement scheme reduces the impact of channel error.

4. If R1 does not get any acknowledgement within a time period, R1 will re-transmit the data packet to a node in cell C2 once, and if fails again, R1 will use a backup path, which is discussed in the follows.

5. This process continuous until the data packet reaches the sink. In the following, we discuss route maintenance. When there is no sensor node

available in a routing cell, or the route becomes broken when the only active node in a relay cell failed, a backup path will be used. We will use an example to illustrate the route maintenance. In Figure 1, when the node in cell C1 is not available, node in cell C0 will use a backup path to send packets to the sink. The backup path is the shortest path from current location to the destination that detours the unavailable cell. For example, in Figure 1 cell C3 and C4 are used as the backup path to send packet to the sink.

The above routing protocol is referred to as Cell Relay (CR) routing, since the main idea is to let a serial of cells to relay packets from source to sink. The CR routing protocol is designed for static sinks. However, it can be extended to handle mobile sinks.

5. Performance Evaluation We evaluate the effectiveness and efficiency of SNEM and CR through experiments. Two metrics are used to evaluate the performance of SNEM plus CR: delivery ratio and average energy consumption per data delivery. The delivery ratio is defined as the ratio of the number of data packets successfully received by a sink node to the total number of data packets sent by source nodes. The average energy consumption is the ratio of the total energy dissipation to the total number of delivered data packets. We compare the performance of SNEM plus CR with two scenarios: (1) A sensor network energy conserving protocol - PEAS [1] plus CR routing; (2) A popular sensor network routing protocol – Directed Diffusion [4]. In PEAS, each node periodically transmits probe messages, and replies to any received probe messages from the neighbor nodes. A node can go to sleep if it receives replies for its probes, then it wakes up in random time and transmits probe messages. PEAS relies on the local probe scheme to maintain a set of working nodes while turn off redundant ones.

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In the following discussion, for simplicity we will use SNEM and PEAS to denote SNEM+CR and PEAS+CR respectively.

We implemented the SNEM protocol and the CR routing protocol in QualNet [11]. For comparison, PEAS and Directed Diffusion were also implemented in QualNet. The underlying MAC is 802.11 DCF. In the simulation, a sensor node has a fixed energy supply, and it becomes unavailable when its energy runs out.

The default simulation testbed has 4 sinks and 900 sensor nodes randomly distributed in a 300m x 300m area. Each simulation run lasts for 600 seconds, and each result is averaged over five random network topologies. A source generates one data packet per second. Each data packet is 64 bytes. A target moves across the network. The mobility of target follows the standard random waypoint model. During the simulation, the target is always present in the network. The sinks are static. The transmission range of each sensor node is R=40m, and the sensing range is r=20m. The side length of a cell is set as a = r = 20m, so there are totally 15x15 = 225 cells in the network, and on average there are 4 nodes in each cell. We studied the effect of different cell size on routing performance in our previous work [8]. Because of the page limit, we will not discuss the detail in this paper. One of the results is that R/2 is a good value for a that tradeoffs the routing performance and the number of cells. For the following simulation tests, unless we are varying a particular parameter in a test, the default settings are: The probability of node failure is 0.1; the average path length between source and sink is 10 hops, the maximum speed of target is 20 m/s. We choose function ( )t T tα = − for both SNEM and CR, where T = 800 (sec).

5.1 Performance under Different Node Density First we compare the delivery ratio and energy consumption under different sensor node density. For the fixed 300m x 300m routing area, the number of sensor nodes is changed from 450 to 1350 with an increment of 150. The delivery ratios under different routing schemes are plotted in Figure 3, where DD denotes Directed Diffusion. Figure 3 shows that the delivery ratio of SNEM is higher than both PEAS and Directed Diffusion. SNEM+CR chooses the shortest path between source and sink based on their locations, and this increases delivery ratio because it has less chances of being affected by node failures. In PEAS, nodes go to sleep and randomly wake up. This may cause certain area without any active nodes and packets can be lost. So the delivery ratio of PEAS is the lowest among the three. In the late stage of the simulation, sensor nodes may die out because they run out energy. In Directed Diffusion, all node remain active and consume significant amount of energy, in addition the route discovery introduces large routing overhead and consume lots of energy. In the late stage of simulations, Directed Diffusion causes more sensor nodes to die out than SNEM, thus its delivery ratio is lower than SNEM. The delivery ratios under all the three routing protocols increase as the node density increases. Since when node density is high, there are more nodes available for data forwarding, and this increases the delivery ratio.

The total energy consumptions of the three protocols under different node density are reported in Figure 4. The energy consumption of SNEM is much lower than Directed Diffusion and PEAS. Since in SNEM, only one node in each relay cell is

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active while all other nodes are asleep. And the number of TN cells is small (9) compared to the total number of cells (225), which means most of the nodes are in sleep mode. Since idle sensor nodes consume considerable amount of energy, SNEM saves lots of energy for sensor nodes. Figure 4 also shows that the energy consumption of Directed Diffusion increases much faster than PEAS and SNEM, and it becomes very large when node density is high. This is because in Directed Diffusion much more nodes are involved in route discovery and consume much more energy when node density is high. Figure 4 shows when node density is low, the energy consumption of PEAS is close to SNEM, and the difference becomes larger when node density increases. In SNEM there is only one active node in most (relay) cells, there are more nodes asleep in each cell when node density is high, thus SNEM saves more energy than PEAS when node density is high. The energy consumption of SNEM increases a little bit when node density is high, and it is mainly because there are more sensor nodes in TN cells.

Fig 3: Delivery ratio vs node density

Fig 4: Energy consumption vs node density

5.2 Different Source-Sink Distances Figure 5 reports the delivery ratio for different source-sink distances. All the delivery ratios decrease when source-sink distance increases. Because the longer the route, the larger chance of broken path (Recall the node failure probability is 0.1). For any source-sink distance, the delivery ratio of SNEM is the highest among the three schemes. When nodes failed in SNEM, a backup path will be used to forward packets. Thus the delivery ratio under SNEM is high even when the source-sink distance is large. PEAS has lower delivery ratio than both SNEM and Directed Diffusion, since some area may not be covered by any sensor nodes due to node random sleep scheme.

The energy consumptions for different source-sink distances are shown in Figure 6. The total energy consumed by the three schemes increases as distance increases. However, the increase of Directed Diffusion is much faster than PEAS and SNEM. As the source-sink distance increases, much more nodes are involved in route discovery of Directed Diffusion, thus much more energy is consumed. In SNEM, the number of nodes that forward data is about the same as the number of hops, so the energy consumption increases slowly. And since only one sensor node is active in most cells, the energy consumption under SNEM is much lower than the other two schemes.

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Fig 5: Delivery ratio vs path length

Fig 6: Energy consumption vs path length

Fig 7: Average delay vs path length

Fig 8: Delivery ratio vs node failure prob.

5.3 The Delay Performance The average delays of data packet under the three schemes are plotted in Figure 7. The delays of different schemes are similar. Delay under SNEM is smaller than the other two schemes, since the route under CR is usually the shortest path between a source and a sink. Also, CR does not have route discovery phase while Directed Diffusion has. PEAS encounters larger delay because of the node random sleep scheme. Sometimes the packet can not be forward to the next sensor node until it wakes up.

5.4 Resilience to Sensor Node Failures

Figure 8 shows the delivery ratio for different node failure probability nP . The delivery ratios of all schemes decrease as node failure probability increases. However, the decrease in SNEM is slower than both Directed Diffusion and PEAS. In SNEM, if the active sensor in a relay cell fails, other node in the cell will become active and start forwarding data. In addition, if there is no node available in a relay cell, the upstream cell will find a backup path the forward packets to the sink. Thus, SNEM has higher delivery ratio than both Directed Diffusion and PEAS when node failure happens. In PEAS, a packet may be dropped if an active node fails, thus the delivery ratio in PEAS is lower than SNEM and Directed Diffusion.

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The energy consumptions under different node failure probability are plotted in Figure 9. The energy consumption under PEAS decreases as nP increases, since there are fewer nodes forwarding packets when failure probability is high. While the energy consumptions for SNEM and Directed Diffusion increase as nP increases. In SNEM, a backup route will be used when a relay node fails, and usually a backup route is longer than a direct route thus causes more energy consumption. In Directed Diffusion, when a relay node fails, a new route needs to be discovered and this increases energy consumption. Figure 9 shows that SNEM scheme consumes much less energy than both Directed Diffusion and PEAS for different node failure probability.

Fig 9: Energy consumption

Fig 10: Target tracking quality

5.5 Tracking Performance The energy management scheme that turns off redundant sensor nodes should not affect the tracking performance, i.e., the target should be detected and tracked for most of the time. In order to study the tracking performance of SNEM, we use the percentage of traveled distance that is detected by active sensor nodes as the measurement of tracking quality. The test results are presented in Figure 10. The target moves according to random way point model, and the x-axis is the maximum target speed (m/s), which varies from 5 m/s to 25 m/s. The y-axis is the percentage of traveled distance that is detected by sensor nodes. Figure 10 shows that Directed Diffusion has the highest tracking percentage, since all nodes in Directed Diffusion are active. The tracking percentage of SNEM is very close to Directed Diffusion. In PEAS, nodes go to sleep and randomly wake up, so it is possible that some area is not covered by any active node. Thus the tracking quality of PEAS is lower than Directed Diffusion and SNEM. In summary, our simulation results show that SNEM has low energy consumption, high delivery ratio and high tracking quality compared to PEAS and Directed Diffusion under different scenarios.

6. Conclusions In this paper, we presented a novel energy management protocol for target tracking sensor networks – Sensor Network Energy Management protocol. SNEM exploits the

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features of target tracking applications and sensor networks, and it allows sensor nodes that are far away from targets go to sleep and save lots of energy while guarantee the accurate tracking and timely delivery. We also designed a Cell Relay routing protocol that integrates very well with SNEM. Extensive simulation experiments show that our SNEM scheme performs much better than another energy saving scheme – PEAS and a popular routing protocol – Directed Diffusion. SNEM saves significant amount of energy and achieves high quality tracking and high delivery ratio.

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