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Autonomous Recovery from Multi-node Failure in Wireless Sensor Network

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Abstract

Wireless Sensor Networks (WSNs) often serve mission-critical applications in inhospitable environments such as battlefield and territorial borders. Inter-node communication is essential for WSNs to effectively fulfill their tasks. In hostile setups, the WSN may be subject to damage that breaks the network connectivity and disrupts the application. The network must be able to recover from the failure and restore connectivity so that the designated tasks can be carried out. Given the unattended operation of the network, the recovery should be performed autonomously. In this paper we present a distributed algorithm for Autonomous Repair (AuR) of damaged WSN topologies in the event of multiple node failures. AuR models connectivity between neighboring nodes as electrostatic interaction between charges based on Coulomb's law. The recovery process is initiated locally at the neighbors of failed nodes by moving in the direction of loss to reconnect with other nodes. The performance of AuR is validated through simulation.
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... UAV network is a relatively new research field, and it is a very special kind of network with many technical challenges [3], [8]. Although UAV is often used as a mobile node to restore the connectivity of WSNs [9] and there have been some studies on the damage-resilient problem in WSNs and other fields [6], [7], [10]- [18], there are still no reports on the problem of how to recover severely damaged USNETs. ...
... The first can only deal with the problem of network partitioning caused by the damage of single node [10]- [15]. The second can tolerate simultaneous damage of multiple nodes [6], [7], [16], [17]. The first subcategory methods restore connectivity by moving surviving node to the location of the damaged cutvertex node. ...
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... According to this technique, a nearby node replaces a failed node, which is then replaced by another node, and this process continues until finding a redundant node. C 3 R (Coverage Conscious Connectivity Restoration) [18], RIM (Recovery through Inward Motion) [19], and AUR (Autonomous Repair) [20] are techniques that have a similar focus as our work. C 3 R addresses connectivity restoration in case of one or more failed nodes. ...
... In [20], the authors proposed an approach called Autonomous Repair (AuR). It uses a concept similar to electrostatic interaction based on Coulomb's law between modeling connectivity charges among neighboring nodes. ...
... Each point in the graph is calculated by running simulations with random seeds ten times. The results for the proposed algorithm are compared with baseline algorithms RIM [18], C3R [19], GSR [22], and AUR [20]. The following sections explain the results obtained by doing extensive simulations. ...
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... Some previous works, such as [6]- [8], have attempted to recover the network connectivity by moving some healthy nodes to specific positions or directions. For instance, Autonomous Repair (AuR) is an algorithm for WSN which try to restore connectivity based on the idea of Coulomb's law between charges. ...
... The neighbor nodes of the failed SNs initiate the restoration procedure. They move toward the damaged area and then toward the center of the WSN's working area [8]. In [9], two algorithms have been designed for UASN connectivity restoration whose nodes model is similar to ours. ...
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... e second algorithm discovers out those locations, which have minimum hop count between the event area and the base station. A method Wireless Communications and Mobile Computing for cut-vertex or critical-node determination is done in [26]. ...
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... Moreover, many of the UED algorithms [11], [12], [14], [16], [17] were designed regarding to the failure of only one UAV in one-off UEDs, which is relatively basic and simple. Other UED algorithms [13], [15], [18] were developed for the failure of multiple UAVs in oneoff UEDs, but exclusively focused on the scenarios where a small number of UAVs were destructed. In fact, a general UED 1 can destruct any number of UAVs at random time steps, which is more common in practice but more difficult to handle. ...
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