Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

Zhao, Chuanxin; Wu, Changzhi; Wang, Xiangyu; Ling, Bingo Wing‐Kuen; Teo, Kok Lay; Lee, Jae-Myung; Jung, Kwang-Hyo

doi:10.1016/j.apm.2017.05.001

Cited by 34 publications

(13 citation statements)

References 21 publications

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“…All processes of communication involve the efforts of the sensor nodes and their battery power. Therefore, it is clear from this fact that the overall lifetime of the sensor nodes depends upon the battery of the sensor node (Zhao et al, 2017). The sensor nodes are capable to generate the data from their vicinity or surroundings.…”

Section: Introductionmentioning

confidence: 99%

Distance Based Energy Optimization through Improved Fitness Function of Genetic Algorithm in Wireless Sensor Network

Panhwar¹,

Deng²,

Khuhro³

et al. 2024

SIC

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For the last few decades, Wireless Sensor Networks (WSNs) has been drawing important considerations due to having application-specific characteristics. These WSNs are usually deployed in one of the following two manners: deterministic or random (ad hoc). In the ad hoc manner, the deployment is mostly subjected to a significant number of limitations such as limited bandwidth, routing failure, storage and computational constraints. The overall performance of the WSNs is determined by a robust routing scheme. Nevertheless, WSNs include prominent application parameters for routing such as energy usage and network longevity. Therefore, the routing scheme is the key element for the longevity and usability of WSNs. In the conventional WSNs, the routing design can be opted for the network longevity optimization, while, assuming all the other objectives to be the limitations are imposed on the optimization problem Genetic Algorithm (GA) performs the small-scale computation and large-scale computation as well. Performance of GA is robust in both small scale and large scale computations. The original GA is assumed with some modifications. In this paper, a GA based optimization in the stationary WSNs with the deployment of multiple sinks is proposed. It is assumed that the sensor nodes route the data towards the nearest sink through the multiple hops communication strategy. In our simulations results: routing is following the multiple hops to the sink by the optimized routing. Moreover, we've enhanced the Network lifespan. The proposed technique saved both the route distance through optimization and energy by routing the data through optimized neighbor sensor nodes.

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Section: Introductionmentioning

confidence: 99%

Distance Based Energy Optimization through Improved Fitness Function of Genetic Algorithm in Wireless Sensor Network

Panhwar¹,

Deng²,

Khuhro³

et al. 2024

SIC

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“…In recent years, researchers have carried out extensive studies on metaheuristic algorithms such as harmony search (HS) [8,9], artificial bee colony (ABC) [10,11], cuckoo search (CS) [12], imperialist competitive algorithm (ICA) [13], teaching-learning-based optimization (TLBO) [14], backtracking search optimization algorithm (BSA) [15], firefly algorithm (FA) [16], Yin-Yang-pair optimization (YYPO) [17], and squirrel search algorithm (SSA) [18]. Besides, many metaheuristic algorithms have been enhanced to solve real-world optimization problems such as a decomposition-based multiobjective firefly algorithm developed for RFID network planning [19] and a novel diffusion particle swarm optimization proposed for optimizing sink placement [20]. Based on the "no free lunch" theorem (NFL) [21,22], there is no optimization algorithm that works well on all optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Search and Rescue Operations

Shabani

Asgarian

Gharebaghi

et al. 2019

Mathematical Problems in Engineering

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In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

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“…A practical solution to address the challenge of node failure is node replication, in the sense that a network architecture is designed to provide the opportunity to accommodate multiple sinks and controllers to achieve higher reliability. Additionally, it has been shown that deploying multiple sinks in a sensor network ensures better network performance . Likewise, in an IIoT system, where the network brain is placed on a controller, the controller failure will abandon the network with no proper control commands, which will result in severe performance degradation.…”

Section: Introductionmentioning

confidence: 99%

An efficient placement of sinks and SDN controller nodes for optimizing the design cost of industrial IoT systems

et al. 2018

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Recently, a growing trend has emerged toward using Internet of Things (IoT) in the context of industrial systems, which is referred to as industrial IoT. To deal with the time-critical requirements of industrial applications, it is necessary to consider reliability and timeliness during the design of an industrial IoT system. Through the separation of the control plane and the data plane, software-defined networking provides control units (controllers) coexisting with sink nodes, efficiently coping with network dynamics during run-time. It is of paramount importance to select a proper number of these devices (i.e., software-defined networking controllers and sink nodes) and locate them wisely in a network to reduce deployment cost. In this paper, we optimize the type and location of sinks and controllers in the network, subject to reliability and timeliness as the prominent performance requirements in time-critical IoT systems through ensuring that each sensor node is covered by a certain number of sinks and controllers. We propose PACSA-MSCP, an algorithm hybridizing a parallel version of the max-min ant system with simulated annealing for multiple-sink/controller placement. We evaluate the proposed algorithm through extensive experiments. The performance is compared against several well-known methods, and it is shown that our approach outperforms those methods by lowering the total deployment cost by up to 19%. Moreover, the deviation from the optimal solution achieved by CPLEX is shown to be less than 2.7%.

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Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

Cited by 34 publications

References 21 publications

Distance Based Energy Optimization through Improved Fitness Function of Genetic Algorithm in Wireless Sensor Network

Distance Based Energy Optimization through Improved Fitness Function of Genetic Algorithm in Wireless Sensor Network

A New Optimization Algorithm Based on Search and Rescue Operations

An efficient placement of sinks and SDN controller nodes for optimizing the design cost of industrial IoT systems

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