Fuzzy-based clustering solution for hot spot problem in wireless sensor networks

Naghibzadeh, Mahmoud; Taheri, Hoda; Neamatollahi, Peyman

doi:10.1109/istel.2014.7000798

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…The DUCF uses a non‐probabilistic approach for CH selection where the residual energy, node degree, and distance to BS are the considered fuzzy input parameters for both CH selection and cluster radius computation. The fuzzy‐based unequal clustering (UCF) is another fuzzy‐based unequal clustering approach for WSNs in which a nonprobabilistic approach is used for CH selection by considering the residual energy of a node. For cluster radius computation, the distance to BS and the node density are taken as the fuzzy input parameters.…”

Section: Related Workmentioning

confidence: 99%

“…So the cluster radius of a CH remains the same, no matter how much energy remains in it. The fuzzy logic-based approaches have been found to be very effective for unequal clustering [36][37][38][39][40][41][42]44 since it is capable of sensibly blending several parameters. The researchers have used fuzzy logic in a distributed manner to compute the cluster radius for CHs.…”

Section: Related Workmentioning

confidence: 99%

“…To avoid such problem, some unequal clustering algorithms have been discussed in recent years. 8,[36][37][38][39][40][41][42][43] In these algorithms, the cluster size closer to the BS is kept smaller as compared with the clusters away from the BS. This ensures availability of more CHs closer to the BS to share the high data traffic load.…”

Section: Introductionmentioning

confidence: 99%

“…This problem is termed as energy hole or hot spot problem. To avoid such problem, some unequal clustering algorithms have been discussed in recent years . In these algorithms, the cluster size closer to the BS is kept smaller as compared with the clusters away from the BS.…”

Section: Introductionmentioning

confidence: 99%

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Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Mazumdar

2018

Int J Communication

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Summary Due to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Mazumdar

2018

Int J Communication

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“…A good load balancing algorithm must be capable of balancing the entire sensor nodes through a fair distribution of the entire packets across available sensor nodes by considering packet characteristics and sensor node capacity in order to gain optimal node utilization. The hotspot problem where certain sensor nodes are under heavy traffic load can be solved while considering the load balancing aspect (Naghibzadeh et al, 2014).…”

Section: Load Balancingmentioning

confidence: 99%

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Nasir¹,

Ku-Mahamud²,

Kamioka³

2017

Journal of Computer Science

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Wireless Sensor Network (WSN) has been widely implemented in large sectors such as military, habitat, business, industrial, health and environment. WSN is part of a distributed system where elements such as routing, load balancing, energy efficiency, node localization, time synchronization, data aggregation and security need to be addressed to improve its efficiency, robustness, extendibility, applicability and reliability. Despite multiple approaches proposed to improve all these aspects, there is still room for improvement in order to enhance the capability of WSN in terms of routing and energy efficiency. Ant Colony Optimization (ACO) is one of the approaches used to extend WSN capabilities because its heuristic nature is very suitable with distributed and dynamic environments. This study covers the common WSN aspects and performance evaluation criteria in addition to the list of previous studies that have used ACO approaches in WSN.

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Load‐based node ranked low‐energy adaptive clustering hierarchy: An enhanced energy‐efficient algorithm for cluster head selection in wireless sensor networks

Mehta

Saxena

2021

Concurrency and Computation

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A Wireless Sensor Network typically uses a large number of power‐constrained sensor nodes for sensing phenomenal data and for its processing. The lifetime of a sensor node is limited and this is a great challenge for researchers to sustain the network for a long time. Since, the data transmission from a sensor node to the base station is more power consuming, the hierarchical routing protocols play a major role in multihop communication and to prolong the network life time. It uses a clustering approach with an overhead of cluster head (CH) selection to attain this. This overhead needs proper addressing and to be reduced by some means. The work in this paper employs a new approach based on low‐energy adaptive clustering hierarchy (LEACH) and considers network load for electing CH in addition to number of links, residual sensor energy and distance. Here, energy and time are used for calculating load on a CH. The simulation results show that the proposed protocol performs better than LEACH, LEACH‐I, and NR‐LEACH algorithms on the parameters of average energy ratio throughput, end‐to‐end delay, packet delivery ratio, and a number of alive nodes.

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Fuzzy-based clustering solution for hot spot problem in wireless sensor networks

Cited by 11 publications

References 14 publications

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Load‐based node ranked low‐energy adaptive clustering hierarchy: An enhanced energy‐efficient algorithm for cluster head selection in wireless sensor networks

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