IMPROVING ENERGY CONSUMPTION IN LARGE SCALE WIRELESS SENSOR NETWORKS WITH MULTIPLE MOBILE SINKS DEPLOYMENT

2008 ◽  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Wireless Sensor ◽  
Mobile Sinks

scholarly journals Use of multiple mobile sinks in wireless sensor networks for large-scale areas

2020 ◽  
Vol 10 (4) ◽  
pp. 175-180
Author(s):  
Hasanain A.H. Al-Behadili ◽  
Saddam K.A. AlWane ◽  
Yasir I.A. Al-Yasir ◽  
Naser Ojaroudi Parchin ◽  
Peter Olley ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Wireless Sensor ◽  
Mobile Sinks

scholarly journals Designing Constrained Trajectory Based on Maximizing Energy Reduction in Large-Scale Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Xu ◽  
Chuan Ping Wang ◽  
Hua Dai ◽  
Da Qiang Zhang ◽  
Jing Jie Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Mobile Sink ◽  
Energy Reduction ◽  
Wireless Sensor ◽  
Movement Trajectory ◽  
Computation Efficiency

TheMobile Sinkbased data collection in wireless sensor network can reduce energy consumption efficiently and has been a new data collection paradigm. In this paper, we focus on exploring polynomial algorithm to compute the constrained trajectory of theMobile Sinkfor data collection. We first present a universal system model for designing constrained trajectory in large-scale wireless sensor networks and formulate the problem as theMaximizing Energy Reduction for Constrained Trajectory(MERC) problem. We show that the MERC problem is NP-hard and design an approximation algorithm (CTMER), which follows the greedy approach to design the movement trajectory of theMobile Sinkby maximizing theeffective average energy reduction. Through both rigid theoretical analysis and extensive simulations, we demonstrate that our algorithm achieves high computation efficiency and is superior to otherMobile Sinkbased data collection methods in aspects of energy consumption and network lifetime.

An Energy-Efficient Data Dissemination Scheme in Large-Scale Wireless Sensor Networks

2013 ◽  
Vol 5 (3) ◽  
pp. 34-54
Author(s):  
Shiow-Fen Hwang ◽  
Han-Huei Lin ◽  
Chyi-Ren Dow
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Storage ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Dissemination ◽  
Wireless Sensor ◽  
Event Data ◽  
Efficient Data

In wireless sensor networks, due to limited energy, how to disseminate the event data in an energy-efficient way to allow sinks quickly querying and receiving the needed event data is a practical and important issue. Many studies about data dissemination have been proposed. However, most of them are not energy-efficient, especially in large-scale networks. Hence, in this paper the authors proposed an energy-efficient data dissemination scheme in large-scale wireless sensor networks. First, the authors design a data storage method which disseminates only a few amount event data by dividing the network into regions and levels, and thus reducing the energy consumption. Then, the authors develop an efficient sink query forwarding strategy by probability analysis so that a sink can query events easily according to its location to reduce the delay time of querying event data, as well as energy consumption. In addition, a simple and efficient maintenance mechanism is also provided. The simulation results show that the proposed scheme outperforms TTDD and LBDD in terms of the energy consumption and control overhead.

scholarly journals Biased trajectory dissemination of uncontrolled mobile sinks for event collection in wireless sensor networks

2016 ◽  
Vol 12 (12) ◽  
pp. 155014771668203 ◽  
Author(s):  
Guisong Yang ◽  
Lijun Wang ◽  
Linhua Jiang ◽  
Xingyu He
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Path Length ◽  
Cluster Head ◽  
Mobile Sink ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Mobile Sinks ◽  
The Social

Considering the social properties of mobile sinks, we propose a biased trajectory dissemination of uncontrolled mobile sinks for event collection in wireless sensor networks. In biased trajectory dissemination of uncontrolled mobile sink, we first divide the whole network into clusters which can be managed by cluster heads that are elected in turn for intra-cluster event collection and inter-cluster communication. Second, for a mobile sink, we further divide the clusters it visits into biased clusters and non-biased clusters according to its staying probability. The mobile sink will send its mobility message which shows its location as it moves into a new cluster. We then construct a biased loop which is composed of all biased clusters and some non-biased clusters to disseminate a mobile sink’s mobility message only to clusters on it when the mobile sink moves into a biased cluster. We also construct query path that connects any cluster head that is not on the biased loop to a cluster head on it. An event could be transmitted to the biased loop along the query path for further forwarding to the mobile sink. Numerous simulations show the superior performance of biased trajectory dissemination of uncontrolled mobile sink compared to the representative schemes in terms of average path length, delay, and network energy consumption.

Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors

2018 ◽  
Vol 12 (1) ◽  
pp. 616-626 ◽  
Author(s):  
Kenneth Li-Minn Ang ◽  
Jasmine Kah Phooi Seng ◽  
Adamu Murtala Zungeru
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Wireless Sensor

A Hybrid Energy-Efficient Itinerary Planning for Mobile Agents in Wireless Sensor Networks

2013 ◽  
Author(s):  
Yu-Cheng Chou
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Evolutionary Process ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computing Power ◽  
Itinerary Planning ◽  
Hybrid Energy

Wireless sensor networks (WSNs) are limited to resources including computing power, storage capacity, and especially energy supply. Thus, energy consumption of sensor nodes has become a dominant performance index for a WSN. In addition, data transmission between sensor nodes is a main energy consumer of WSNs. This paper presents a method called immune genetic algorithm based multiple-mobile-agent itinerary planning (IGA-M2IP) that addresses issues of energy consumption in large-scale WSNs. The IGA-M2IP preserves a GA’s advantages, and further improves a GA’s efficiency by restraining possible degenerative phenomena during the evolutionary process.

scholarly journals An Improved Niche Chaotic Genetic Algorithm for Low-Energy Clustering Problem in Large-Scale Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Min Tian ◽  
Jie Zhou ◽  
Xin Lv
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Energy ◽  
Wireless Sensor ◽  
Mixed Integer ◽  
Clustering Problem ◽  
Systems Simulation

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.

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