Coverage-time optimized dynamic clustering of networked sensors for pervasive home networking

Kim, Joongheon; Lee, Wonjun; Eunkyo, K.; Kim, Doe-Wan; Kim, Hyeokman

doi:10.1109/tce.2007.381712

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…No convergence results to favourable configurations are given. In [28], a dynamic clustering scheme for WSN lifetime optimization is proposed, which requires periodically solving a non-linear programming problem to regulate the radius of each cluster.…”

Section: Related Work and Paper Contributions 21 Related Workmentioning

confidence: 99%

“…The mission area is partitioned according to a weighted Voronoi tessellation. The resulting clusters are unequal, as in [14], [27], [28], with the key differences that the proposed algorithm i) dynamically sets the weights without the need of solving optimization problems at each time-step, ii) in stationary environments, lets the partition converge to the weighted Voronoi tessellation which minimizes the mean squared error between the load of each CH nodes and the average load of the CH nodes.…”

Section: Paper Contributionsmentioning

confidence: 99%

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Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

Pietrabissa

Liberati

2017

International Journal of Control

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This paper present two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSN). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalized Voronoi Tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches.

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Section: Related Work and Paper Contributions 21 Related Workmentioning

confidence: 99%

Section: Paper Contributionsmentioning

confidence: 99%

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

Pietrabissa

Liberati

2017

International Journal of Control

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“…The area of each cluster is around 2 / opt . Like in [25] the expected distances are computed from a cluster member node to its CH and from a CH to the BS. Because of the uniform distribution of CHs in a × (m 2 ) sensor area, the expected squared region enclosed by each cluster with the CH positioned at ( CH , CH ) can be computed as given below:…”

Section: Squared Optimization Problemmentioning

confidence: 99%

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Kumar

2016

Journal of Sensors

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Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

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“…In [9], the authors considered the coverage of the area and the connectivity in an unreliable wireless sensor grid-network and derived the conditions for the sensing range to ensure that an area is fully covered. In [10], the authors proposed a novel energy-efficient coverage-time optimized dynamic clustering scheme that regulates cluster radii for balanced energy consumption among CHs to maximize coverage-time. In [11], the authors proposed a minimum-cost maximum-flow based schedule algorithm to determine a movement plan for the sensors in order to maximize the sensor network coverage and minimize the number of flips.…”

Section: Related Workmentioning

confidence: 99%

Coverage Capacity Optimization for Mobile Sensor Networks Based on Evolutionary Games

Liu

Yue

Shen

et al. 2014

International Journal of Distributed Sensor Networks

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The optimal and distributed provisioning of high coverage capacity in mobile sensor networks is known as a fundamental but hard problem. The situation is exacerbated in a mobile wireless setting due to the dynamic coverage of a mobile sensor network resulting from continuous movement of sensors. In this paper, we propose an optimization framework for maximizing the coverage capacity in mobile sensor networks that comprise both stationary sensors (SSs) and mobile sensors (MSs). Both the intracoverage capacity and the intercoverage capacity are jointly optimized by considering the control of the power and distance between MSs and SSs and the interference among MSs. We propose a new noncooperative control algorithm that iteratively solves intracoverage capacity optimization between MSs and SSs. We also further formulate intercoverage capacity as evolutionary coalition game and present a new cooperative interference control algorithm that iteratively solves intercoverage capacity optimization among MSs. We prove the existence of a solution for iteration control equation of the power and distance and the interference to maximize the coverage capacity. Finally, we assess the performance of the proposed algorithm and show that proposed control scheme can effectively improve the average coveragecapacity in mobile sensor networks.

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Coverage-time optimized dynamic clustering of networked sensors for pervasive home networking

Cited by 8 publications

References 19 publications

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Coverage Capacity Optimization for Mobile Sensor Networks Based on Evolutionary Games

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