Mobile sensor nodes collaboration to optimize routing process based mobility model

Guezouli, Lyamine; Barka, Kamel; Bouam, Souheila; Bouhta, Djamila; Aouti, Souha

doi:10.1109/wincom.2017.8238161

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…In the marine environment, the simulation results based on the mobile model are reliable [47]. The entity movement model includes the following aspects: Random walk;Random waypoint mobile model;Random direction model;Gauss Markov model.…”

Section: Network Modelmentioning

confidence: 99%

Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Zhang

2019

Sensors

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Monitoring of marine polluted areas is an emergency task, where efficiency and low-power consumption are challenging for the recovery of marine monitoring equipment. Wireless sensor networks (WSNs) offer the potential for low-energy recovery of marine observation beacons. Reducing and balancing network energy consumption are major problems for this solution. This paper presents an energy-saving clustering algorithm for wireless sensor networks based on k-means algorithm and fuzzy logic system (KFNS). The algorithm is divided into three phases according to the different demands of each recovery phase. In the monitoring phase, a distributed method is used to select boundary nodes to reduce network energy consumption. The cluster routing phase solves the extreme imbalance of energy of nodes for clustering. In the recovery phase, the inter-node weights are obtained based on the fuzzy membership function. The Dijkstra algorithm is used to obtain the minimum weight path from the node to the base station, and the optimal recovery order of the nodes is obtained by using depth-first search (DFS). We compare the proposed algorithm with existing representative methods. Experimental results show that the algorithm has a longer life cycle and a more efficient recovery strategy.

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Section: Network Modelmentioning

confidence: 99%

Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Zhang

2019

Sensors

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“…Lyamineet et al [2] designed a new routing method RNC-RWP (Routing based Nodes Collaboration under Random WayPoint) which is able to route information in a mobile environment. The node of the RNC-RWP model is free to move in the environment (according to the model RWP).…”

Section: Routing Based Nodes Collaboration Under Random Waypointmentioning

confidence: 99%

An analysis of mobility management for mobile sensor nodes in MWSN

Zhang

Liang

2018

MATEC Web Conf.

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Mobile wireless sensor networks (MWSN) are composed of a large number of mobile sensor nodes, which are used to collect data. MWSN have been widely applied in a variety of harsh environments, so mobile sensor nodes are often at risk of damage. How to manage mobile sensor nodes is an important issue. In this paper, we analyse different mobility management schemes proposed in some typical research literature and classify these schemes by using three features: the collaboration between mobile sensor nodes, routing and deployment in MWSN. We specify advantages and disadvantages of the proposed schemes, compare different key parameters of MSWN respectively, including energy efficiency, data delay, cost and lifetime, etc. Finally, we discuss existing problems of MWSN management and give some helpful suggestions in this area.

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Mobile sensor nodes collaboration to optimize routing process based mobility model

Cited by 2 publications

References 17 publications

Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

An analysis of mobility management for mobile sensor nodes in MWSN

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