Energy-Efficient Intra-Cluster Routing Algorithm to Enhance the Coverage Time of Wireless Sensor Networks

Gharaei, Niayesh; Bakar, Kamalrulnizam Abu; Hashim, Siti Zaiton Mohd; Pourasl, Ali Hosseingholi

doi:10.1109/jsen.2019.2901735

Cited by 27 publications

(16 citation statements)

References 30 publications

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“…In [9], the authors 4 propose an energy efficient routing protocol based on particle swarm optimization and genetic algorithm to prevent energy dissipation of sensor nodes by calculating the load-balanced table of clusters, the optimal set of data gathering points and an optimal path for the mobile sink. In [10], an energy-efficient intra-cluster routing algorithm is proposed to balance energy consumption among member nodes by optimizing the sojourn locations of mobile sink in each cluster.…”

Section: Related Workmentioning

confidence: 99%

“…Because a sink can randomly move within the sensing area to gather the detecting data, the data transmission distances of the sensor nodes could be reduced. The sink mobility provides more convenience data collection and reduces the energy consumption for the sensor nodes [6]- [10]. WSNs with mobile sink are expected to be used in a high dangerous region, including battlefield, disaster area, and environmental pollution area, among others as well as to collect the sensing data.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

Chang

Jeng

Sheu

et al. 2020

Preprint

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Wireless sensor networks with mobile sink provide a mobile device to move into the sensing area for collecting the sensing data. It increases the flexibility and convenience of data gathering in such systems. Taking into account the energy consumption of mobile sink, the moving distance of mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for the mobile sink in large-scale wireless sensor networks systems. According to the greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel efficient data collection path planning scheme with an inner center approach is proposed to reduce the moving distance and to prolong the lifetime of mobile sink in wireless sensor networks. The relationship among moving path, moving distance, and number of sensor nodes are analyzed and discussed. Considering the communication range limitation of sensor nodes and the sensing area with obstacles, the proposed scheme makes an adaptive decision for creating the moving path of mobile sink. Simulation results demonstrate that the reasonable moving path planning can be achieved and the moving distance can be reduced for a mobile sink in wireless sensor networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

Chang

Jeng

Sheu

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In [9], the authors proposed an energy-efficient routing protocol based on particle swarm optimization and a genetic algorithm to prevent energy dissipation between sensor nodes; by calculating the load-balanced table of clusters, the optimal set of data gathering points and an optimal path for the mobile sink can be obtained. In [10], an energy-efficient intracluster routing algorithm was proposed to balance the energy consumption among member nodes by optimizing the sojourn locations of the mobile sink in each cluster.…”

Section: Related Workmentioning

confidence: 99%

“…Because a sink can randomly move within the sensing area to gather the detection data, the data transmission distances of the sensor nodes can be reduced. The mobility of the sink enables convenient data collection and reduces the energy consumption by the sensor nodes [6]- [10]. WSNs with mobile sinks are expected to be used to collect sensing data in highly dangerous regions, including battlefields, disaster areas, and environmental pollution areas.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sinks

Chang

Jeng

Sheu

et al. 2020

Preprint

View full text Add to dashboard Cite

Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks.

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“…Compound Event-Driven Deployment Algorithm Based on the Collaborative Neural Network Strategy. 1: Initialization: An initial confidence coefficient c 1 is selected, and the number of initial iterations s = 1 is set; 2: For the confidence coefficient c s , using Neural Network(15), the optimal solution α s of the single-objective Sub-problem (7) and the Lagrange multiplier vector β s are obtained. If α s is the Pareto optimal solution for the multi-constrained compound event-driven deployment problem, the algorithm terminates and outputs; otherwise, the process skips to step 3; 3: After obtaining α s and β s in step 2, Neural Network (16) is used to solve Formula(13) to obtain a new optimal confidence coefficient; 4: The number of iterations is set and the process skips to step 2.…”

mentioning

confidence: 99%

Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

Zhuang

et al. 2020

Sensors

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Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.

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Energy-Efficient Intra-Cluster Routing Algorithm to Enhance the Coverage Time of Wireless Sensor Networks

Cited by 27 publications

References 30 publications

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sink

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sinks

Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

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