Energy Efficient Model for Recovery from Multiple Nodes Failure in Wireless Sensor Networks

Jha, Vivekanand; Prakash, Nupur; Mohapatra, Amar Kumar

doi:10.1007/s11277-019-06479-8

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…The first stage tries to minimize the amount of delays, and in the next stage for the desired area, the fundamental diagram is created to show the traffic flow. Jha et al [43] proposed a model for connectivity restoration for the WSN which consists of two phases: (1) intra-…”

Section: Traffic Flow Controlmentioning

confidence: 99%

Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Fakhouri

Soltani

2021

IEEE Access

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Section: Traffic Flow Controlmentioning

confidence: 99%

Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Fakhouri

Soltani

2021

IEEE Access

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“…Contrary to the redeployment-based solution, some researchers investigated node mobility for network restoration. An approach for network partition healing through movable relay node deployment is presented by Jha et al 61 The proposed solution finds the minimal number of nodes through heuristics. Monte-Carlo Markov Chain (MCMC) solution to determine coverage of WSN is discussed in Chakraborty et al 41 MCMC is used to determine the probability that WSN with multi-state nodes can efficiently send its sensed data to the mobile sink within a specified time.…”

Section: Related Workmentioning

confidence: 99%

Toward connectivity of a disconnected cluster in partitioned wireless sensor network for time-critical data collection

Nasr

Khan

2020

International Journal of Distributed Sensor Networks

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Reliable network connectivity is one of the major design issues in the context of wireless sensor networks. These networks have diverse communication patterns due to non-uniform sensing activities at various locations in the environment being monitored. In such scenarios, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This gives rise to a situation in which alive nodes are trapped in a disconnected cluster, and they do not have enough radio range to communicate their data to the destination (i.e. a sink or a relay node connected to the main part of the network). This phenomenon may deprive a large number of alive nodes of sending their important time-critical data to the sink. In this article, we propose a virtual antenna–based cooperative beamforming approach for retrieving valuable data from these disconnected nodes. In the proposed approach, the sensor nodes in an isolated partition work together to form a directional beam. This directional beam significantly increases their overall communication range to reach out to a distant relay node which is connected to the main part of the network. The proposed approach of cooperative beamforming–based partition connectivity is more effective when a cluster with a favorably large number of nodes gets partitioned. Furthermore, a beamforming-based mechanism is proposed for a disconnected cluster to locate the nearest relay node which is still connected to the sink and to reconnect itself to the main part of the network via the most adjacent relay node for time-critical data transmission. The proposed mechanism is then evaluated through simulation results.

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“…The failures can also create a network partition where parts of the deployed network get entirely disconnected from the base station (sink) node. Thus network connectivity to avoid such partitioning can be assured by integrating node deployment mechanisms along with recovery mechanisms (Stojmenovic et al, 2011;Mahmood et al, 2018;Lee et al, 2015;Jha et al, 2019;Senturk et al, 2014;Ranga et al, 2016b;Lalouani et al, 2017;Bayrakdar, 2020d).…”

Section: Introductionmentioning

confidence: 99%

“…The utmost important fourth network recovery technique is through relay node deployment. It is the most popular and efficient mechanism where existing and additional nodes are deployed to re-establish connection among nodes (Jha et al, 2019;Joshi & Younis, 2016;Ranga et al, 2016b;Chouikhi et al, 2017). Finding an optimal relay count required to restore links is an optimization problem and requires complete information about the number of partitions and locations of nodes in them (Younis et al, 2014;Shriwastav & Ghose, 2018;Senturk et al, 2014;Chouikhi et al, 2017;Alfadhly et al, 2011;Cheng et al, 2017;Nikolov & Haas, 2016;Devi & Manickam, 2014;Zahid et al, 2018;Liu et al, 2018;Lee et al, 2016;Kumar & Amgoth, 2019;Lalouani et al, 2017;Ranga et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

Distributed partition detection and recovery using UAV in wireless sensor and actor networks

Zear¹,

Ranga

2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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Wireless Sensor and Actor Networks (WSANs) have been extensively employed in various domains ranging from elementary data collection to real-time control and monitoring for critical applications. Network connectivity is a vital robustness measure for overall network performance. Different network functions such as routing, scheduling, and QoS provisioning depends on network connectivity. The failure of articulation points in the network disassociates the network into disjoint segments. We proposed Distributed Partition Detection and Recovery using Unmanned Aerial Vehicle (UAV) (DPDRU) algorithm, as an optimal solution to recover the partitioned network. It consists of three steps: Initialization, Operational and Detection, and Recovery. In the Initialization phase sink node collects all the information about the network. In the Operational and Detection phase, network nodes communicate regularly by exchanging HEARTBEATS, detects failure if some nodes do not get a message from the neighbor node and send failure reports, and sink node identifies network partition. In the recovery phase, the sink node sends UAV at the positional coordinates of the failed node and examines network recovery after UAV reaches the desired location. Our approach primarily focuses on reducing message overhead by sending few update messages to sink node and energy consumption by engaging network nodes only for communication. The requirements of the recovery process (physical movement and communication) are fulfilled by UAV. The algorithm is tested according to the following parameters: Detection Time, Recovery Time, message overhead, and distance traveled by UAV. Simulation results validate the efficacy of the proposed algorithm based on these parameters to provide reliable results. The minimum and the maximum number of messages transmitted are 11 for 10 nodes and 24 for 100 nodes respectively. Hence these results demonstrate that the message overhead in our proposed solution is less as compared to other techniques when the number of nodes increases.

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Energy Efficient Model for Recovery from Multiple Nodes Failure in Wireless Sensor Networks

Cited by 9 publications

References 27 publications

Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Multi-Objective Robust Optimization for the Traffic Sensors Location Problem

Toward connectivity of a disconnected cluster in partitioned wireless sensor network for time-critical data collection

Distributed partition detection and recovery using UAV in wireless sensor and actor networks

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