Maximizing Network Topology Lifetime Using Mobile Node Rotation

El-Moukaddem, Fatme; Torng, Eric; Xing, Guoliang

doi:10.1109/tpds.2014.2329851

Cited by 19 publications

(14 citation statements)

References 42 publications

(22 reference statements)

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“…However, the constraints in practical scenario including deployment of mobile sink, randomness in activities, and cost of mobility of sink reduce the realistic applicability of the approach. The problem of energy hole has been explored using controlled mobility of sensors with topology configuration in maximization of lifetime using mobile node rotation (LM‐MR) . It is based on the real life penguin huddling behavior, where penguins take turns on cold extremities with below 0° to increase the temperature of huddle above 20°.…”

Section: Related Worksupporting

confidence: 81%

“…In the mobility‐based green computing, some designated mobile sensors intelligently change the network topology by repositioning their locations for balancing energy consumption among sensors. The intelligent repositioning is significantly challenging considering the 3 critical constraints of the network and sensor specifications . The constraints include uneven power depletion, static nature of majority of normal sensors, and strong correlation between sensor position, and coverage and connectivity.…”

Section: Introductionmentioning

confidence: 99%

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Towards green computing in wireless sensor networks: Controlled mobility–aided balanced tree approach

Khatri

Kumar

Kaiwartya

et al. 2017

Int J Communication

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Network lifetime maximization has received continuous attention as green computing in wireless sensor networks. Recently, controlled mobility-based green computing has witnessed significant attention from academia and industrial research labs. It is due to the growing number of sensor-based services in mobility friendly nonhostile environments in our daily life. The intelligent mobility-aided repositioning of sensors is significantly challenging considering the critical constraints including irregular power depletion, static normal sensors, the correlation between sensor position, and coverage and connectivity. In this context, this paper proposes a network lifetime maximization framework based on balanced tree node switching. Specifically, a balanced tree-based network model for wireless sensor networks is designed focusing on energy consumption of sensor nodes in tree-based networks. The problem of lifetime maximization in tree-based network is identified considering energy loss rate, path load, and balancing factor. Two node-shifting algorithms are developed, namely, energy-based shifting and load-based shifting for balancing tree-based network in terms of energy. Analytical and simulation experimentbased comparative performance evaluation attests the benefit of the proposed framework as compared to the state-of-the-art techniques considering a number of energy-oriented metrics for wireless networks.Focusing on efficient and balanced energy consumption for uniform energy depletion among sensors, 2,4 various green computing techniques have been suggested. It includes duty cycle management, data aggregation, and controlled mobility-based lifetime maximization for green computing in WSNs. 5-9 One of the major constraints in duty cycle based techniques is the overhead involved in constructing broadcasting sets for assigning duty to the complete network. 10-14 It creates major network overhead and reduces actual data transmission capability of the network. Most of the data aggregation techniques are based on clustering approach, where networks are divided into manageable clusters to balance energy consumption in different areas of the networks. One of the major problem of the clusteringbased green computing is the frequent require of cluster head selection and updating. 11,[15][16][17][18][19] Recently, controlled mobility-based green computing in WSNs has witnessed considerable attention because of the growing significance of sensor technology in mobility-enabled nonhostile environment. In the mobility-based green computing, some designated mobile sensors intelligently change the network topology by repositioning their locations for balancing energy consumption among sensors. The intelligent repositioning is significantly challenging considering the 3 critical constraints of the network and sensor specifications. [20][21][22][23] The constraints include uneven power depletion, static nature of majority of normal sensors, and strong correlation between sensor position, and coverage and connectivity. In literature o...

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Section: Related Worksupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Towards green computing in wireless sensor networks: Controlled mobility–aided balanced tree approach

Khatri

Kumar

Kaiwartya

et al. 2017

Int J Communication

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“…A new technique called mobile node rotation was introduced in [4] for low-cost mobile sensor nodes to address the power consumption and increased the WSN lifetime. Though the network lifetime was improved, the reliability was not increased.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survival Study on Data Gathering and Target Tracking Techniques for Energy Efficient Routing in WSN

Nisha¹,

Velmurugan²,

Raja³

et al. 2020

jcr

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Wireless Sensor Network comprises thousands of small sensor nodes in a physical environment for monitoring and reporting the events. Wireless sensor networks (WSNs) are forecasted for gathering the data like physical or environmental properties from geographical area. Data gathering is based on the wireless communications between the sensor node and the sink node. Target tracking and multiple data collection are significant application in wireless sensor network. In Wireless Sensor Network, sensor nodes are used to sense the target object with minimum energy and collect data from different locations. However in existing techniques during routing, data gathering and target tracking, the energy consumption was not reduced and network lifetime was not improved.Our key objective is to reduce the energy consumption and data gathering efficiency during the data gathering and target tracking process in WSN. In this work, a survey is carried out with existing techniques for attaining higher energy efficient routing and maximal data gathering efficiency with minimal target tracking time in WSN.

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“…A novel weight-based tree-construction method is introduced. El-Moukaddem F [3] described the key challenges facing wireless sensor networks is extending network lifetime due to sensor nodes having limited power supplies. Chiara Petrioli [4], considered an ALBA-R protocol for converge casting in wireless sensor networks.…”

Section: Related Work a Clustering Schemes And Mobile Data Collmentioning

confidence: 99%

Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks

Kalaivani¹

2016

ijsrtm

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The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks.

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Maximizing Network Topology Lifetime Using Mobile Node Rotation

Cited by 19 publications

References 42 publications

Towards green computing in wireless sensor networks: Controlled mobility–aided balanced tree approach

Towards green computing in wireless sensor networks: Controlled mobility–aided balanced tree approach

A Survival Study on Data Gathering and Target Tracking Techniques for Energy Efficient Routing in WSN

Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks

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