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Coverage-AwareConnectivityRestorationinMobileSensorNetworks.

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Coverage-Aware Connectivity Restoration in Mobile Sensor Networks

Neelofer Tamboli and Mohamed Younis Department of Computer Science and Electrical Engineering

University of Maryland, Baltimore County (neelo1, younis@umbc.edu)

Abstract— Mobile sensor networks rely heavily on inter-sensor connectivity for collection of data. Nodes in these networks monitor different regions of an area of interest and collectively present a global overview of some monitored activities or phenomena. Since failure of a sensor leads to loss of connectivity it may cause a partitioning of the network. A number of approaches have been recently proposed that pursue node relocation in order to restore connectivity. However, these approaches tend to ignore the possible loss of coverage in some areas, either due to the failure itself or due to the connectivity-limited focus of the recovery. This paper opts to address the connectivity and coverage concerns in an integrated manner. A novel Coverage Conscious Connectivity Restoration (C3R) algorithm is presented. C3R involves one or multiple neighbors of the failed node to recover from the failure. Each neighbor temporarily relocates to substitute the failed node, one at a time, and then returns back to its original location. This leads to intermittent connectivity and monitoring of all the originally covered spots. C3R is validated through simulation. The simulation results confirm the effectiveness of the approach.

I. INTRODUCTION Interest in the applications of wireless sensor networks (WSNs) has been on the rise in recent years [1]. For some of these applications, such as space exploration, coastal and border protection, combat field reconnaissance and search and rescue, it is envisioned that a set of mobile sensor nodes will be employed to collaboratively monitor an area of interest and track certain events. Upon their deployment, nodes are expected to form a network and coordinate their action when participating in the execution of a task. In many setups, such as a disaster management application, nodes need to collaborate with each other in order to effectively search for survivors, assess damage and identify safe escape paths.

Inter-node connectivity is very crucial not only to the effectiveness of the application, but also for network connectivity. Some nodes may also play a role in maintaining flow of information from the sensors to in-situ and remote users. In the worst case, due to a node failure, the network may get partitioned into multiple disjoint blocks and stop functioning. Thus, the network connectivity should be recovered so that subsequent negative effects on the application could be avoided.

Rapid restoration of connectivity is desirable. In risky areas, e.g., combat zones, the recovery should be a self-healing process involving the existing sensor nodes. Also, given the autonomous and unsupervised operation of WSN, tolerating the failure should be performed in a distributed manner. Node repositioning is an effective means for repairing partitioned

networks [2][3]. However, prior work has focused on restoring the connectivity without considering the negative impact of repositioning nodes on coverage which may leave a portion of the network area unmonitored by any sensor.

In this paper a Coverage Conscious Connectivity Restoration (C3R) algorithm is proposed. Unlike other approaches that readjust the network topology by repositioning nodes, C3R strives to keep most of the network topology intact and localize the scope of the recovery. Basically, the failure of a node, “F”, is tolerated by temporarily replacing F with its neighbors, one at a time. These neighbors take turns in moving to the position of F. Upon detecting the failure, neighbors coordinate to establish a schedule for each of them to reposition to F. After serving for some time, each neighbor goes back to its original position, allowing for another neighbor of F to come forward and so on. In other words, the nodes strike a balance between temporal and spatial coverage in order to restore the connectivity. C3R is a distributed algorithm and requires very limited messaging overhead in conducting the recovery. C3R is validated in a simulated environment. The validation results demonstrate the effectiveness of C3R.

The paper is organized as follows. The next section gives an overview of the considered system model. Section III highlights the distinction of C3R from previous work. The details of the C3R algorithm are provided in Section IV and the validation results are reported in Section V. Finally, Section VI concludes the paper.

II. SYSTEM MODEL AND PROBLEM STATEMENT C3R is applicable to a network of mobile sensor nodes. The mobile nodes can be a part of a flat network topology or form a second tier in a hierarchical network architecture, e.g., a set of actors or aggregation and forwarding units. Sensors are randomly deployed in an area of interest. It is assumed that during a network bootstrap nodes discover each other and form a connected network [2]. C3R assumes that a mobile node can determine its location relative to its neighbors [4]. Each node maintains a list of its neighbors. Although the algorithm presented in Section IV assumes that a node is only aware of its 1-hop neighbors, the approach can leverage the availability of 2-hop neighbor list, as discussed later.

This paper focuses on maintaining network connectivity when a node fails while also sustaining the pre-failure coverage. Consider for example the network topology depicted in Figure 1. Nodes n1, n2, n3, n10, and n11 are neighbors of n9. The failure of n9 would detach n10 and n11 and their neighbors

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Figure 1: Connected network of mobile nodes

from the rest of the network and leave a hole in coverage since no other node’s sensing range overlaps with that of n9. C3R overcomes this problem by temporarily replacing the failed node with one or multiple of its neighbors. These neighboring nodes switch back and forth so that the network topology and the coverage are maintained.

III. RELATED WORK Node relocation is a well-studied problem [5]. Some approaches allow movement only between initial deployment and application startup (post-deployment). For example, Wang, Cao, and La Porta [6] and [8] proposed algorithms to spread the sensors and maximize coverage. Unlike C3R, both [6] and [7] focus on avoiding holes in coverage rather than maintaining connectivity. C2AP [6] and COCOLA [5] propose post-deployment relocation of nodes to maximize connectivity and coverage while optimizing other network parameters. However, [5] and [6] do not deal with impact of a node failure.

To recover from a node failure, Wang et al. [10] have proposed cascaded movement of nearby nodes towards failed node. Like C3R, Basu and J. Redi [11] do not assume availability of spare nodes. Unlike C3R, they perform block movement of a subset of the nodes. The approaches most related to C3R are DARA [2] and RIM [3]. Unlike C3R, DARA and RIM ignore the coverage issue and involve cascaded relocation.

IV. COVERAGE AWARE CONNECTIVITY RESTORATION As mentioned, failure of a node F may cause the network to partition into disjoint segments and may introduce a hole in coverage. If another non-spare node A replaces F, then the coverage hole shifts from one location to another and a major change in the network topology becomes unavoidable. In C3R, the relocated node A does not settle at the position of F, instead it moves back and forth between where F was and its original location. Thus, C3R provides temporal coverage and intermittent connectivity in order to localize the scope of recovery and avoid a full loss of coverage in some areas.

A. Detailed C3R approach If there are redundant nodes in the network, replacing the failed node F with a spare one is the best and most effective solution in terms of network connectivity and coverage. However, in the absence of spares graceful degradation is unavoidable. As indicated above, the sensing and data routing role that F used to play in the network will be provided by its neighbors. Those neighbors are still expected to perform their own duties and the tasks related to the tolerance of the failure

are considered an additional load on these neighbors. The following describes the detailed steps of C3R basic. 1. Pre-failure operation: The only pre-failure knowledge that C3R requires for each node is to have is a list of 1-hop neighbors. This list is formed post deployment with each node broadcasting a HELLO message to introduce itself to its neighbors. Each node also tabulates the position and ID of all its neighbors. Relative positions suffice for C3R. This list is updated dynamically with nodes periodically sending HEARTBEAT messages to their neighbors. When a node, A, does not receive a response to its HEARTBEAT message from a neighboring node, F, it assumes that F has failed. 2. Neighbors’ synchronization: Upon detecting the failure of its neighbor F, node A initiates a recovery process unless it is already participating in the recovery from another failed node. C3R applies recovery procedure whether failed node F is a cut-vertex or not, since cut-vertex detection is non-trivial and C3R aims to maintain coverage with connectivity. If only cut-vertices are considered to be recovered, then it would cause coverage holes.

For the recovery, node A first needs to know whether F has neighbors that would participate in the recovery. The neighbors of F will be referred to hereafter as concerned nodes. Given that the individual nodes only maintain a list of their 1-hop neighbors, node A will not be aware of all concerned nodes which may be at the same time planning for recovery from the failure of F. Note that the absence of F may have broken all communication paths between these concerned nodes. In order for all concerned nodes to connect and coordinate, they would have to move at least to a distance of rc/2 from F, where rc is the communication range of a node [3]. This scenario would require some synchronization though since the concerned nodes may not detect and react to the failure of F at the same time. Alternatively, all the concerned nodes could move to the position of F and the first of them to reach F, say node C, connects all of them. While this avoids the need for synchronization, it may lengthen the traveled distance overhead. Nonetheless, C3R favors this approach given the high level of coordination involved and given that it is practically hard to come up with a reasonable waiting time to guarantee a rendezvous for all concerned nodes. Node C will be referred to thereafter as the recovery coordinator. 3. Crafting a recovery plan: Before moving towards F, node A informs its neighbors of the temporary relocation to avoid being perceived by them as faulty. Neighbors that route data through A would either find new routes in the meantime or buffer the data until A returns. In addition, each of the concerned nodes calculates the degree of overlapped coverage, which is the percentage of the total area covered by this node that also lies within the sensing range of at least one of its neighbors. Assuming a disk coverage model, overlapping area among more than three circles can be found using the formulae given in [13].

Any of the concerned nodes can be the recovery coordinator. When any concerned node C, is the first node to

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2009 proceedings

Figure 3: An example for the operation of C3R

reach the position of F, it becomes the coordinator. Other concerned nodes stop moving further and inform C about their original degree of overlapped coverage, their energy level and their original proximity to F. The responsibility of a coordinator is to identify the subset of the concerned nodes that are the most qualified to handle the recovery process. By involving more than one node, C3R balances the load and minimizes the temporal disruption of connectivity and coverage. The levels of involvement of different concerned nodes may vary. Nodes with a high overlapping coverage, a small distance to failed node and a rich energy supply are ranked higher and are considered as best candidates to perform the recovery. On the other hand, nodes that are ranked low may be spared from participation. For example, if a node has little energy left it is preferred to keep it functional for the

longest time at its original position. Based on the ranked list, the coordinator forms a recovery

schedule, defining for each concerned node the time and duration to relocate at the position of F. The detailed ranking formulation and scheduling algorithm are not included due to space constraints. 4. Executing the recovery plan: The recovery plan crafted by the coordinator defines for each concerned node what to do. Nodes, that are spared, go back to their original position and resume their duties. The role of a participating node A is to go to the position of F and spend some time there and then come back to its original position. While at the position of F, node A links all neighbors of F to ensure coverage and network connectivity. Before it is time for A to go back, the next node on the schedule starts moving towards F, and so on. When A returns home, it notifies its neighbors and resumes routing of their buffered data packets. After all scheduled nodes finish one round, A relocates again to F and repeats the process. Thus the participating nodes are periodically moving back and forth until additional nodes are added to the network if the nodes do not have sufficient energy to double-task.

If A’s onboard energy falls below a certain threshold, it informs the node currently relocated to F that it has to seize its participation in the recovery. This active coordinator devises and broadcasts a new schedule to the concerned nodes. The new schedule commences immediately. Finally, it is worth noting that each active coordinator synchronizes the clock of all concerned nodes to avoid race conditions.

B. Pseudocode and Illustrative Example Figure 2 shows pseudo-code for C3R. Each node will keep checking for failure or recovery related events in lines 1-18. When node A detects the failure of F, it updates its routing table and checks its onboard energy reserve to decide on whether it will be able to help in the recovery or not (line 1-4). If node A can participate, it calculates the degree of overlapped coverage and informs its neighbors for data management (lines 5-7). When it relocates back (lines 37-40) it informs those neighbors again and resumes original tasks. Lines 9-17 are for a node that is notified about the departure or the return of a neighbor. Basically, this node either finds an

1. IF(node, A, detects a neighbor node, F, has failed) 2. Update routing table 3. Check level of onboard energy supply 4. IF(sufficient energy) 5. Calculate the degree of overlapped coverage 6. Send “TemporaryRelocation” message to neighbors 7. RelocateTemporarilyTo(F) 8. END IF 9. ELSE IF(node, A, receives “TemporaryRelocation”) 10. Find new route 11. IF(new route not found) 12. Buffer Data 13. END IF 14. ELSE IF(node, A, receives “RelocatedBack”) 15. IF(buffering data) 16. Transmit data 17. END IF 18. END IF

RelocateTemporarilyTo(F) 19. Step towards F 20. IF(reached F) 21. Broadcast “Will coordinate recovery” message 22. Collect relevant information from all concerned nodes 23. Form a ranked list and a recovery schedule 24. Broadcast recovery schedule 25. IF(not the first node on schedule) 26. RelocateBackToSelf() 27. END IF 28. ELSE IF(received “Will coordinate recovery”) 29. Stop moving 30. Receive the recovery schedule 31. IF(not the first node on schedule) 32. RelocateBackToSelf() 33. END IF 34. ELSE IF 35. RelocateTemporarilyTo(F) 36. END IF

RelocateBackToSelf() 37. Relocate back to self 38. Restart routing of buffered data 39. Broadcast “Came Back” message to neighbors 40. Schedule “Relocation” time given in schedule

Figure 2: High level pseudo code for the C3R algorithm

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2009 proceedings

Figure 4(a): Reduction in field coverage vs. Sensing range for node population of 150.

Figure 4(b): Reduction in field coverage for node population of 50 and 100

Figure 4(c): Reduction in field coverage with varying sensing range

alternate route or buffers the data until its neighbor returns. Figure 3 illustrates the recovery process of C3R using the

network configuration shown in Figure 1. When n9 fails, its neighbors lose connectivity and move inward to draft a failure recovery plan. Node n11 acts as the recovery coordinator as shown in Figure 3(b). Nodes collect the schedule from n11 and move back to their positions, except n2 which is the first node scheduled for recovery since it has high overlapping coverage as shown in Figure 3(c). Figure 3(d)-(g) show how neighbors of n9 move back and forth successively for recovering the network from failure of n9.

V. EXPERIMENTAL EVALUATION This section describes the simulation experiments to validate the effectiveness of the C3R approach.

A. Experiment Setup and Performance Metrics The performance is assessed using the following metrics: • The reduction in coverage relative to its pre-failure level.

This metric assesses the effectiveness of C3R relative to coverage unaware connectivity restoration techniques.

• The total distance moved by all nodes involved in the recovery, which gauges efficiency and energy consumption.

• The number of nodes moved in the process, which serves as a measure of the scope of the recovery effort.

• The number of messages exchanged among nodes. This is a measure of recovery process overhead.

The following parameters are used to vary the WSN configuration in the experiments:

The sensing range (rs) affects the node coverage. Short ranges make C3R a favorable recovery scheme.

The communication range (rc) influences network connectivity. Also, although a large value of rc increases the number of neighbors and splits the recovery overhead, those performing the recovery would travel longer distances.

The number of nodes in the network would affect the node density given a fixed size deployment area. Low node density limits the coverage and deems C3R invaluable.

B. Baseline Approaches The performance of C3R is compared to RIM and to the Nearest Neighbor (NN) algorithm [3]. RIM is a distributed

algorithm for recovery through inward motion. When a node Sf fails, its neighbors move inward toward its position so they can connect with each other. The relocation procedure is recursively applied to handle any node that gets disconnected due to the movement of one of their neighbors (e.g., those which have already moved towards the faulty node).

The NN algorithm, like RIM, pursues greedy heuristics. When a node Sf fails, NN moves its closest neighbor, SNN, to where Sf is located, repairing the severed connectivity around Sf. The neighbors of SNN react to its departure, as the closest among them moves where SNN used to be, and so on. NN terminates when no more neighbors are to be moved. While RIM uses 1-hop neighbor list, the NN algorithm requires that every node is aware of its 2-hop neighbors so that the nearest neighbor will be known before the failure of Sf. Neither RIM nor NN is concerned about loss of network coverage..

C. Simulation results The simulation experiments involve randomly generated WSN topologies with varying number of nodes and communication ranges. The number of nodes has been set to 50, 75, 100, 125, 150, and 200 in a network field with dimensions of 1000m X 1000m. Since RIM and NN do not accommodate different sensing and communication ranges, the values of rs and rc have been kept equal for all experiments that involve these approaches. rs and rc have been set to 25m, 50m, 75m, 100m, 125m, and 150m. In each experiment the node that transmits the largest number of packets is set to fail. The results of the individual experiments are averaged over 10 trials. All results are subjected to 90% confidence interval analysis and stays within 10% of the sample mean.

The key difference between C3R and the baseline approaches is that C3R is dynamic with nodes relocating back and forth. In RIM and NN, once relocated, nodes do not move further until another node fails. Hence parameters like total distance travelled by all nodes will keep increasing for C3R and the field coverage in C3R keeps changing every instant. For consistency, results of C3R have been collected for only one round of relocation. This can act as a guideline for scheduling the deployment of additional nodes to recover the system permanently. For C3R, the after-recovery field coverage was averaged from three instances per relocating node: before the node moves, when the node is halfway

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2009 proceedings

Figure 7: Total number of packetsexchanged vs. rc.

Figure 6: Total nodes moved vs. rc

(for 150-nodes).

Figure 5: Total distance travelled by allnodes vs. rc (for 150 nodes).

towards Sf, and when the node reaches Sf. Reduction in field coverage: Figure 4(a)-(b) show how a connectivity-centric restoration impacts coverage, measured in terms of the reduction in field coverage relative to a pre-failure level. Overall C3R could limit the loss in coverage to only 3%. When nodes are evenly distributed with minimal overlapping coverage, the field coverage in C3R reduces by a similar amount as in RIM. Figure 4(c) shows that as the degree of overlapped coverage of nodes increases, the field coverage after applying C3R is maintained while RIM gives a higher reduction in the field coverage. In networks with less node coverage, i.e., shorter sensing range, C3R moves the coverage hole from one point to another and when multiple adjacent nodes fails, then the number of concerned nodes is not sufficient to restore the coverage hole generated. Distance moved: Figure 5 shows the total distance that nodes had to travel during the recovery. In C3R, the maximum distance for a node to move equals the communication range of nodes. Hence as this range increases, the total distance travelled by a node increases. For high node density, no relocation may be necessary since nodes are already covered and well connected with more than one node. Hence the total distance travelled does not increase when the communication range goes really high. For RIM and NN, the total distance travelled by all nodes will keep increasing as the nodes will always keep relocating when a node fails. C3R performs better than RIM and NN because it localizes the failure recovery and introduces little changes in the network topology. Number of moved nodes: Figure 6 shows the total number of nodes moved in all three algorithms during failure recovery. The performance of C3R and RIM is similar to that shown in Figure 5 for the total distance travelled by all nodes. This is because more nodes travel causing the total distance to grow. Here RIM relocates more nodes than NN. However C3R involves the least number of node movements in any setting. Number of messages exchanged: Figure 7 shows the total number of packets exchanged while restoring connectivity. Each broadcast is counted as one message. Since fewer nodes need to synchronize during the failure recovery in C3R, the messaging overhead with C3R is the minimum, while NN exchanges the most number of packets.

VI. CONCLUSION A failure of a node can cause a mobile sensor network to partition and thus disrupt the application. Unlike most prior

work that exploits node relocation to restore connectivity, C3R addresses the concern about field coverage. Basically, a lack of coverage-awareness in the connectivity restoration process may leave some areas unmonitored by any sensors. To overcome this problem, C3R avoid permanent repositioning of nodes. Basically the neighbors of the failed node F coordinate among themselves and agree on their role in the recovery. Each neighbor will move to the position of F to restore connectivity and coverage in that area and then go back to its original position after serving for some time. The neighbors of the failed node take turns. C3R not only strikes a balance between connectivity and coverage, but also balances the load among the neighbors of the failed node. C3R is a distributed and localized algorithm that imposes little messaging overhead and can thus scale for large networks. The simulation results have confirmed the effectiveness of C3R. Acknowledgement: This work is supported by the National Science Foundation, contract # 0000002270.

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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2009 proceedings