A survey on fault tolerance techniques in Wireless Sensor Networks

Kakamanshadi, Gholamreza; Gupta, Savita; Singh, Sukhwinder

doi:10.1109/icgciot.2015.7380451

Cited by 51 publications

(29 citation statements)

References 31 publications

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“…Moreover, since crashes of nodes, failures of communication links, and missing data are unavoidable in wireless networks, fault-tolerance becomes a key-issue. Among the causes of these constant failures are environmental factors, damaged communications links, data collision and overloaded nodes [15]. Thus, the problems of efficient data transfer, storage and backup become major topics of interest in IoT [26].…”

Section: The Importance Of Backup Placement In Wireless Networkmentioning

confidence: 99%

Fast Distributed Backup Placement in Sparse and Dense Networks

Barenboim¹,

Oren²

2020

Symposium on Algorithmic Principles of Computer Systems

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We consider the Backup Placement problem in networks in the CON GEST distributed setting. Given a network graph G = (V, E), the goal of each vertex v ∈ V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized. The backup placement problem was introduced by Halldorsson, Kohler, Patt-Shamir, and Rawitz [12], who obtained an O(log n/ log log n) approximation with randomized polylogarithmic time. Their algorithm remained the state-of-the-art for general graphs, as well as specific graph topologies. In this paper we obtain significantly improved algorithms for various graph topologies. Specifically, we show that O(1)-approximation to optimal backup placement can be computed deterministically in O(1) rounds in graphs that model wireless networks, certain social networks, claw-free graphs, and more generally, in any graph with neighborhood independence bounded by a constant. At the other end, we consider sparse graphs, such as trees, forests, planar graphs and graphs of constant arboricity, and obtain a constant approximation to optimal backup placement in O(log n) deterministic rounds.Clearly, our constant-time algorithms for graphs with constant neighborhood independence are asymptotically optimal. Moreover, we show that our algorithms for sparse graphs are not far from optimal as well, by proving several lower bounds. Specifically, optimal backup placement of unoriented trees requires Ω(log n) time, and approximate backup placement with a polylogarithmic approximation factor requires Ω( log n/ log log n) time. Our results extend the knowledge regarding the question of "what can be computed locally?" [20], and reveal surprising gaps between complexities of distributed symmetry breaking problems.

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Section: The Importance Of Backup Placement In Wireless Networkmentioning

confidence: 99%

Fast Distributed Backup Placement in Sparse and Dense Networks

Barenboim¹,

Oren²

2020

Symposium on Algorithmic Principles of Computer Systems

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“…This application field usually consists in the deployment of low power sensors with limited transmission range for data collection and task monitoring. Various mechanisms have been proposed based on replication (e.g., hardware, paths), data aggregation, and the adaptation of the network topology in order to tolerate failures [51]. Even if fault-tolerant WSN has received much attention, this application domain exhibits a lower heterogeneity than the Fog-IoT ecosystem and has other characteristics (e.g., energy awareness) that should be addressed for failure management.…”

Section: Related Workmentioning

confidence: 99%

F3ARIoT: A framework for autonomic resilience of IoT applications in the Fog

Ozeer

Letondeur²,

Salaün³

et al. 2020

Internet of Things

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Fog Computing is especially appealing to the Internet of Things (IoT) because it provides computing, storage, and communication resources at the edge of the network, near the physical world (PW). Thus, IoT located in the PW can have interesting properties such as low latencies, real-time operations, and data privacy. The Fog, however, is unstable because it is constituted of billions of devices in a dynamic environment. Moreover, the Fog is cyber-physical and devices are thus subjected to external PW conditions which increase the occurrence of failures. When failures occur in such an environment, the resulting consequences on the PW can be hazardous and costly. This paper presents F 3 ARIoT, a framework for autonomic resilience of IoT applications in the Fog. This framework recovers from failures as well as maintains consistency and safety with respect to the PW during the recovery procedure. F 3 ARIoT was implemented and evaluated on a smart home application. A performance evaluation showed that it has a negligible overhead and recovers from failures in a very short delay with respect to end-users.

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“…Sensor nodes in wireless sensor networks are prone to failure because of hardware and software failures, instability of communication link, battery depletion, dislocation, etc. Therefore, there is a needfor an efficient fault tolerance mechanism to manage or identify the fault and take appropriate action while it occurs [3]. A collector node has restricted capability in sensing and collecting meaningful environment information within its range.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Informer Homed Routing Protocol for Wireless Sensor Network

Kakamanshadi¹,

Gupta²,

Singh³

2019

IJCNC

Self Cite

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A wireless sensor network consists of severalsensor nodes. Sensor nodes collaborate to collect meaningful environmental information and send them to the base station. During these processes, nodes are prone to failure, due to the energy depletion, hardware or software failure, etc. Therefore, fault tolerance and energy efficiency are two important objectives for reliable packet delivery. To address these objectives a novel method called fuzzy informer homed routing protocol is introduced. The proposed method tries to distribute the workload between every sensor node. A fuzzy logic approach is used to handle uncertainties in cluster head communication range estimation. The simulation results show that the proposed method can significantly reduce energy consumption as compared with IHR and DHR protocols. Furthermore, results revealed that it performs better than IHR and DHR protocols in terms of first node dead and half of the nodes alive, throughput and total remaining energy. It is concluded that the proposed protocol is a stable and energy efficient fault tolerance algorithm for wireless sensor networks.

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A survey on fault tolerance techniques in Wireless Sensor Networks

Cited by 51 publications

References 31 publications

Fast Distributed Backup Placement in Sparse and Dense Networks

Fast Distributed Backup Placement in Sparse and Dense Networks

F3ARIoT: A framework for autonomic resilience of IoT applications in the Fog

Fuzzy Informer Homed Routing Protocol for Wireless Sensor Network

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