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Journal ArticleDOI

An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks

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TLDR
WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes and reduces energy consumption and network lifetime by 44%, as compared with existing algorithms.
Abstract
Several studies have demonstrated the benefits of using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks (WSNs). However, these benefits are dependent on the path taken by the mobile sink, particularly in delay-sensitive applications, as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multihopping to the nearest RP. The fundamental problem then becomes computing a tour that visits all these RPs within a given delay bound. Identifying the optimal tour, however, is an NP-hard problem. To address this problem, a heuristic called weighted rendezvous planning (WRP) is proposed, whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP. WRP is validated via extensive computer simulation, and our results demonstrate that WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption by 22% and increases network lifetime by 44%, as compared with existing algorithms.

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Citations
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Journal ArticleDOI

A survey of network lifetime maximization techniques in wireless sensor networks

TL;DR: The family of NL maximization techniques is introduced, the portrayal of rich variety definitions of NL design objective used for WSNs, and some design guidelines with examples are provided to show the potential improvements of the different design criteria.
Journal ArticleDOI

Machine learning algorithms for wireless sensor networks: A survey

TL;DR: This survey presents various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018.
Journal ArticleDOI

Particle swarm optimization based clustering algorithm with mobile sink for WSNs

TL;DR: Extensive simulation results show that the energy consumption is much reduced, the network lifetime is prolonged, and the transmission delay is reduced in the proposed routing algorithm than some other popular routing algorithms.
Journal ArticleDOI

Priority-Based Data Gathering Framework in UAV-Assisted Wireless Sensor Networks

TL;DR: A priority-based frame selection scheme to suppress the number of redundant data transmissions between sensor nodes and the UAV and a novel routing protocol to reduce the transmission distances between senders and receivers is presented.
Journal ArticleDOI

A Tree-Cluster-Based Data-Gathering Algorithm for Industrial WSNs With a Mobile Sink

TL;DR: A tree-cluster-based data-gathering algorithm for WSNs with a mobile sink that can significantly balance the load of the whole network, reduce the energy consumption, alleviate the hotspot problem, and prolong the network lifetime is proposed.
References
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Proceedings ArticleDOI

Wireless sensor networks for habitat monitoring

TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Proceedings ArticleDOI

CarTel: a distributed mobile sensor computing system

TL;DR: CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year, and has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.
Journal ArticleDOI

Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems

TL;DR: The previous best approximation algorithm for the problem (due to Christofides) achieves a 3/2-aproximation in polynomial time.
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