Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)

Islam, Nazmul; Dey, Saurabh; Sampalli, Srinivas

doi:10.3390/s18124258

Cited by 8 publications

(6 citation statements)

References 45 publications

(106 reference statements)

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“…Unequal clustering (UC) is a clustering algorithm that acts as a direct solution to the hotspot and blind spot problem, as discussed in [69][70][71]. Unequal clustering is where the clusters near the BS are smaller and have lesser nodes than clusters far away from the BS, as visualized in Figure 6.…”

Section: Cluster Formationmentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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In recent years, wireless sensor networks (WSNs) have been growing rapidly because of their ability to sense data, communicate wirelessly, and compute data efficiently. These networks contain small and low-powered sensor nodes that organize and configure themselves to carry out their functions. Even though WSNs are cheap, easy to deploy, flexible, and efficient, there are some challenges in terms of energy efficiency and network lifetime. Clustering in WSNs is the most reliable solution for the challenges, in which nodes are grouped into few clusters, and a cluster head (CH) is selected for data aggregation and data transfer to the base station (BS). However, there are still many challenges such as energy hole and isolated node problems that exist because of inefficient CH selection and cluster formation methods. In this work, we comprehensively reviewed various nonmetaheuristic and metaheuristic methods for CH selection and cluster formation that are used in networks from various environmental settings, for a better understanding of how the aforementioned problems are tackled by some authors. Moreover, the methods’ parameter settings, advantages, limitations, and future directions are presented with a brief performance summary of the approaches.

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Section: Cluster Formationmentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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“…The results show an improvement of up to 15% at 10 s when compared with the underlying scheme. In addition, we have also compared RL-based distance and energy only variant of Equation (15). Its worth mentioning that RL-based distance only variant performed much better compared to both conventional Dijktsra and Dijkstra with UAV.…”

Section: Reinforcement-based Routingmentioning

confidence: 99%

“…While m 2 and n 2 are the sender and receiver nodes associated to the UAV 2 . In the case, sender m is in the direct range of CC, the cost will be calculated using Equation (15). The above equations can also be used for all other two UAV schemes presented below.…”

Section: Combined Rl and Uav(s) Trajectory Optimizationmentioning

confidence: 99%

“…The CHs can also rely on each other in forwarding data to the UAV and subsequently to the CC. Multiple clustering techniques are studied in the literature based on different applications [8][9][10][11][12][13][14][15][16][17][18][19][20]. For example, the Link Cluster Algorithm (LCA) introduces the concept of a Gateway (GW) node to provide better connectivity among neighboring CHs [8].…”

Section: Introductionmentioning

confidence: 99%

“…In [15], the authors proposed a new clustering scheme in which first clusters are formed by dividing the area into multiple small partitions and then selecting the CH in those partitions. These will be selected based on energy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks

Minhas

Ahmad

Ahmed

et al. 2021

Sensors

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In Public Safety Networks (PSNs), the conservation of on-scene device energy is critical to ensure long term connectivity to first responders. Due to the limited transmit power, this connectivity can be ensured by enabling continuous cooperation among on-scene devices through multipath routing. In this paper, we present a Reinforcement Learning (RL) and Unmanned Aerial Vehicle- (UAV) aided multipath routing scheme for PSNs. The aim is to increase network lifetime by improving the Energy Efficiency (EE) of the PSN. First, network configurations are generated by using different clustering schemes. The RL is then applied to configure the routing topology that considers both the immediate energy cost and the total distance cost of the transmission path. The performance of these schemes are analyzed in terms of throughput, energy consumption, number of dead nodes, delay, packet delivery ratio, number of cluster head changes, number of control packets, and EE. The results showed an improvement of approximately 42% in EE of the clustering scheme when compared with non-clustering schemes. Furthermore, the impact of UAV trajectory and the number of UAVs are jointly analyzed by considering various trajectory scenarios around the disaster area. The EE can be further improved by 27% using Two UAVs on Opposite Axis of the building and moving in the Opposite directions (TUOAO) when compared to a single UAV scheme. The result showed that although the number of control packets in both the single and two UAV scenarios are comparable, the total number of CH changes are significantly different.

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An energy‐efficient and balanced clustering approach for improving throughput of wireless sensor networks

Karmaker

Alam

Hasan

et al. 2019

Int J Communication

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SummaryMaximizing the lifespan of wireless sensor networks is currently drawing a lot of attention in the research community. In order to reduce energy consumption, sensor nodes that are far from the base station avoid sending data directly. As a result, several disjoint clusters are formed, and nodes within a cluster send their data through the cluster head to avoid long transmissions. However, several parameters related to transmission cost need to be considered when selecting a cluster head. While most of the existing research work considers energy and distance as the most stringent parameters to reduce energy consumption, these approaches fail to create a fair and balanced cluster. Consequently, unbalanced clusters are formed, resulting in the degradation of overall performance. In this research work, a cluster head selection algorithm is proposed that covers all parts of the sensing area in a balanced manner, saving a significant amount of energy. Furthermore, a capture effect–based intracluster communication mechanism is proposed that efficiently utilizes the time slot under various traffic conditions. A Näive Bayes classifier is used to adapt the window size dynamically according to the traffic pattern. Finally, a simulation model using OMNeT++ is developed to compare the proposed approach with the pioneer clustering approach, LEACH, and the contemporary LEACH‐MAC protocol in terms of performance. The results of the simulation indicate that the proposed approach improves the overall performance in terms of network lifetime, energy efficiency, and throughput.

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Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)

Cited by 8 publications

References 45 publications

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks

An energy‐efficient and balanced clustering approach for improving throughput of wireless sensor networks

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