An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor networks

Sridhar, M.; Pankajavalli, P. B.

doi:10.1007/s10586-020-03122-1

Cited by 14 publications

(4 citation statements)

References 17 publications

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“…Within this approach, every node generates individual local order k Voronoi diagrams and assists others in detecting gaps in their diagrams. While this approach employs a distributed architecture, it doesn't tackle the connectivity problem like the previous two techniques [31].…”

Section: A Traditional Methodesmentioning

confidence: 99%

Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area

Neggaz,

Seyyedabbasi,

Hussien

et al. 2024

IEEE Access

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Node localization is a non-deterministic polynomial time (NP-hard) problem in Wireless Sensor Networks (WSN). It involves determining the geographical position of each node in the network. For many applications in WSNs, such as environmental monitoring, security monitoring, health monitoring, and agriculture, precise location of nodes is crucial. As a result of this study, we propose a novel and efficient way to solve this problem without any regard to the environment, as well as without predetermined conditions. This proposed method is based on new proposed Nutcracker Optimization Algorithm (NOA). By utilizing this algorithm, it is possible to maximize coverage rates, decrease node numbers, and maintain connectivity. Several algorithms were used in this study, such as Grey Wolf Optimization (GWO), Kepler Optimization Algorithms (KOA), Harris Hawks Optimizer (HHO), Radient-Based Optimizer (GBO) and Gazelle Optimization Algorithm (GOA). The node localization was first tested in Istanbul, Turkey, where it was determined to be a suitable study area. As a result of the metaheuristic-based approach and distributed architecture, the study is scalable to large-scale networks. Among these metaheuristic algorithms, NOA, KOA, and GWO have achieved significant performance in terms of coverage rates (CR), achieving coverage rates of 96.15%, 87.76%, and 93.49%, respectively. In terms of their ability to solve sensor node localization problems, these algorithms have proven to be effective.INDEX TERMS Node localization, meta-heuristics, Coverage rate, NOA, WSN However, MWSNs also present challenges, such as mo-

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Section: A Traditional Methodesmentioning

confidence: 99%

Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area

Neggaz,

Seyyedabbasi,

Hussien

et al. 2024

IEEE Access

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“…The authors in [19] presented a novel approach using Voronoi diagrams and Delaunay Triangles for energyefficient path selection to forward the data. For optimal path selection, a source node identifies the destination node's Voronoi cell and its own, then selects the next hop from the common Delaunay Triangle.…”

Section: Literature Reviewmentioning

confidence: 99%

“…If there are no neighbors, it will use the broadcast mechanism to transmit the initialization packet (lines 4-5). If the source node has neighbors, it will first calculate the angle between the source node and the destination node (lines 7-12), then it will find the closest angle to the destination among the neighbor nodes' angles (lines [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Now, when a node receives the initialization packet, it will check whether it is the destination node (lines 28-29).…”

Section: B Route Discovery Algorithmmentioning

confidence: 99%

Location Centric Energy Harvesting Aware Routing Protocol for IoT in Smart Cities

Zeb,

Ghani,

Gohar

et al. 2023

IEEE Access

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The advent of battery technology revolutionized power storage for various purposes, including uninterrupted operations of IoT-based Wireless Sensor Networks (WSNs). However, limited energy sources pose a challenge for sensor nodes. To overcome this, researchers introduced Energy Harvesting (EH) in IoT-based WSNs, enabling them to overcome energy limitations and recharge IoT devices. The shift towards energy harvesting-aware routing is necessary to achieve a near-perpetual network environment. In light of the energy consumption of IoT-based WSN nodes, a simple energy-efficient routing technique is proposed to consume less energy. The protocol incorporates efficient link selection based on the closest angle to the destination node and is divided into two main parts: distributed neighbor discovery and routing processes. By incorporating EH techniques, the proposed model aims to improve network longevity. Experimental results demonstrate successful routing and promising outcomes in terms of energy and utilization techniques. INDEX TERMS Internet of things, smart cities, energy harvesting, routing protocol, energy efficiency I. INTRODUCTION

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“…The TESDA minimizes the overhead and the reduced transmission overhead leads to the collision. The node failure and energy hole issues are rectified using the Optimized Distributed Voronoi-based Collaboration (ODVOC) [13,14]. The consumption of energy is high and the network lifetime is low in ODVOC protocol.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Data Aggregation Scheme with Optimal Hop Selection Using Optimized Distributed Voronoi-Based Cooperation with Energy-Aware Dual-Path Geographic Routing Protocol

Sridhar

Pankajavalli

2023

Wireless Pers Commun

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In Wireless Sensor Network (WSN), the consumption of energy is high due to the data transmission in the sensing region. The Optimized Distributed Voronoi-based Collaboration (ODVOC) optimizes data delivery and it suffers from huge energy consumption. To overcome this drawback, a data aggregation scheme is introduced to expand the lifetime of the network and to minimize the energy utilization. Data aggregation is a significant approach that preserves the unwanted usage of energy during data transmission. In this paper, the Optimized Distributed Voronoi-based Cooperation scheme with Energy-aware Dual-path Geographic Routing (DAOHS-ODVOC-EDGR) is proposed which incorporates the polydisperse aggregation scheme and it aggregates the indispensable data in the intermediary nodes. The effective data aggregation is achieved by the estimation of the waiting time of the data at every intermediary node and cost function that is used for electing the next-hop node. In the proposed protocol, the buffer value of every node is divided to retain diversified kinds of flow for effective and fair delivery of data. The rate of transmission at the source and intermediary node is altered during the congestion. The proposed DAOHS-ODVOC-EDGR protocol is tested via simulation scheme. The result of the simulation exposes that DAOHS-ODVOC-EDGR outperforms the TESDA and DVOC protocol in terms of network lifetime, energy efficiency, and hop count delay.

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An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor networks

Cited by 14 publications

References 17 publications

Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area

Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area

Location Centric Energy Harvesting Aware Routing Protocol for IoT in Smart Cities

Adaptive Data Aggregation Scheme with Optimal Hop Selection Using Optimized Distributed Voronoi-Based Cooperation with Energy-Aware Dual-Path Geographic Routing Protocol

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