On the Minimum k-Connectivity Repair in Wireless Sensor Networks

Almasaeid, Hisham M.; Kamal, Ahmed E.

doi:10.1109/icc.2009.5199257

Cited by 37 publications

(20 citation statements)

References 5 publications

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“…Li et al [13] proved that it is NP-hard to add the minimum number of nodes to improve the network connectivity. Almasaeid et al [3] proposed a method with objective of minimizing the number of additional nodes needed to repair the connectivity. Ibrahim et al [11] also discussed network repair problem using minimum number of additional relays.…”

Section: Introductionmentioning

confidence: 99%

On “Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks”

Wang

Mao

Tang

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In wireless sensor and actor networks (WSANs), a set of static sensor nodes and a set of (mobile) actor nodes form a network that performs distributed sensing and actuation tasks. In [1], Abbasi et al. presented DARA, a Distributed Actor Recovery Algorithm, which restores the connectivity of the inter-actor network by efficiently relocating some mobile actors when failure of an actor happens. To restore 1-and 2-connectivity requirements, two algorithms are developed in [1]. Their basic idea is to find the smallest set of actors that needs to be repositioned to restore the required level of connectivity, with the objective to minimize the movement overhead of relocation. Here, we show that the algorithms proposed in [1] will not work smoothly in all scenarios and we give counterexamples for some algorithms and theorems proposed in [1]. We then present a general actor relocation problem and propose methods that will work correctly for several subsets of the problems. Specifically, our method does result in an optimum movement strategy with minimum movement overhead for the problems studied in [1].

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Section: Introductionmentioning

confidence: 99%

On “Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks”

Wang

Mao

Tang

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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“…In [9], a number of additional relays are deployed to ensure that each sensor node in the initial design has k lengthbounded vertex-disjoint shortest paths to the sinks and uses Counting-Paths to minimize the number of deployed relays. Almasaeid et al [10] tried to exploit the overlap between communication ranges to minimize the number of additional nodes needed to repair k-connectivity.…”

Section: Establishing K-connected Topologiesmentioning

confidence: 99%

Multi-objective optimized connectivity restoring of disjoint segments using mobile data collectors in wireless sensor network

Kang

Hong²,

et al. 2017

J Wireless Com Network

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Wireless sensor networks (WSNs) have been used extensively in a range of applications, which realizes data acquisition, processing, transmission, and analysis in an interesting area. Harsh surroundings and their inherent vulnerability often mean that these networks suffer from simultaneous node failure possibly causing the network to become partitioned into multiple disjointed segments. This in turn can prevent the gathering of data from the sensors and subsequent transmission to the sink, causing the whole network to fail. In this paper, a strategy is presented for restoring multi-objective optimization connectivity of these segments using mobile data collectors (MDCs), by considering the segments as collections of sensor nodes and not as some representative node. Different from existing uses of MDCs for restoration, the delay in data collection and task balance is considered, and the network connectivity and data acquisition path optimization problem are transformed into an improved multi-travelling salesman problem (iMTSP). An improved multi-objective optimization genetic algorithm for solving the optimal collection data collector position and moving paths is proposed, which introduces virtual segments and hierarchical chromosome structure, improved population diversity, and custom coding and decoding. The simulation results show that the proposed method can effectively solve the iMTSP of the Pareto optimal solution and can provide a new strategy for connectivity-restoring technology in WSNs. Compared with NSGA-II, the diversity of the proposed gene algorithm represents a clear improvement.

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“…Deploying redundant nodes to achieve a level of connectivity In [1], [2], [3], [4], [5], the goal is to deploy redundant nodes with the intention of achieving k-connectivity. The main idea of these papers is to place redundant nodes at some calculated locations to create a kconnected graph.…”

Section: Related Workmentioning

confidence: 99%