Hierarchical WSN protocol with fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB-ER)

Mehta, Deepak; Saxena, Sharad

doi:10.1007/s11042-020-09633-8

Cited by 31 publications

(29 citation statements)

References 32 publications

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“…For resolving unbalanced clustering, load, frequent CH switching, and communication failure, a novel DLB-fACO clustering protocol is investigated in this research. The proposed protocol provided degree-load analysis-based clustering T A B L E 2 Fuzzy rules for optimized route selection analysis are provided against LEACH, 13 M-LEACH, 47 LEACH-C, 13 LEACH-CC, 48 Fuzzy-PSO, 49 Fuzzy-ACO, 50 Fuzzy-Cuckoo, 51 and FMCB-ER 52 protocols. The analysis results are provided in terms of dead nodes, alive nodes, roundbased packet communication, residual energy of nodes, and the average energy of nodes.…”

Section: Resultsmentioning

confidence: 99%

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Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Juneja

2021

Int J Communication

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A sensor network is a situation-based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load-balanced and fuzzy-ACO (DLB-fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree-and energy-balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster-based hierarchical routing. Communication is performed at two levels. In the first level, node-to-cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy-integrated ACO method is applied for inter-cluster route formation. The route optimization is here performed under fuzzy-based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed loadbalanced protocol is analyzed against the LEACH, LEACH-C, LEACH-CC, M-LEACH, Fuzzy-PSO, Fuzzy-ACO, Fuzzy-Cuckoo, and FMCB-ER protocols.The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols.

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Section: Resultsmentioning

confidence: 99%

“…The configuration parameters of sensor nodes and the environment are listed in Table 3. 52 In this section, the simulation and analysis results are provided and discussed.…”

Section: Resultsmentioning

confidence: 99%

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Juneja

2021

Int J Communication

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“…Mehta and Saxena 31 introduced Fuzzy Multi‐criteria Clustering and Bio‐inspired energy‐efficient routing (FMAB‐ER) protocol to improve the lifetime of network. In the FMAB‐ER approach, the cluster was formed using a grid‐based clustering model.…”

Section: Related Workmentioning

confidence: 99%

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

Kumar

Dhaliwal

Singh

2023

Trans Emerging Tel Tech

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In the recent days, the Internet of Things (IoT) has assumed a significant role in automized farming applications. The farmers can receive frequent information corresponding to the soil moisture, water used, humidity, temperature, moisture, etc., by utilizing actuators and sensor nodes. Various clustering techniques used in wireless sensor network (WSN) applications have achieved energy‐efficient results. However, IoT‐enabled smart farming (SF) applications require scalable networks and long‐distance communication. The existing clustering techniques cannot provide SF solutions, and work related to communication overhead, end‐to‐end delay, latency etc., has not been taken up exhaustively. Therefore, in this article, a cross‐layer‐based hybrid particle Swarm wild horse optimizer (PSWHO) and stable routing technique (CL‐HPWSR) is designed to minimize energy consumption, communication delay, latency etc. in SF applications. Initially, the network is designed to be Bi‐concentric Hexagons. The cross‐layer‐based optimal cluster head (CH) selection mechanism is designed using the hybrid optimization algorithm‐named PSWHO to mitigate excess energy consumption problems in WSN. While clustering, cross‐layer parameters from different layers such as physical, medium access control, and network are used to compute the probability of each sensor node. Moreover, the deep learning‐based routing algorithm extended deep golden eagle neural network (EDGENN) is introduced to explore the optimal route for transmitting the data. The performance of CL‐HPWSR is implemented in Matlab by focusing energy‐efficiency, computational‐efficiency and QoS‐efficiency factors. Finally, in comparison with state‐of‐the‐art‐based SF approaches such as CL‐IoT, FEEC‐IIR, BiHCLR, PSO‐ECHS, and PSO‐C, the CL‐HPWSR has performed better in terms of energy‐efficiency, QoS‐efficiency and computational‐efficiency.

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“…In wireless communication, the process of routing mechanisms and allocating global records in sensed data is highly complex and critical (Khalaf and Abdulsahib, 2020). Transmission of data, image analysis, and equipment functioning are the 3 primary uses of energy in sensor networks (Mehta and Saxena, 2020). As a result, the data communication method must be streamlined to maximize the system's lifetime (Srivastava et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Energy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor Networks

Srinivasalu¹,

Umadevi²

2023

Journal of Computer Science

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In recent years, Wireless Sensor Networks (WSNs) have received increased international attention due to advancements in the communication, electronics, and information fields. These sensor networks integrate a huge number of sensor nodes to track rapidly changing physical events. The key improvement is that these nodes can be easily positioned in any environment due to their small size. Therefore, maintaining network connectivity is crucial in WSNs. If some nodes become unavailable, the connectivity of the routing path fails, resulting in significant packet loss in the WSN. Therefore, many types of research in WSNs have focused on energy efficiency, where energy consumption is minimized to improve the network lifetime. Here, Energy and Distance-aware Multi-Objective Firebug Swarm Optimization (ED-MOFSO) is projected to achieve an energy efficient process. Furthermore, ED-MOFSO minimizes delays to enhance performance measures. From the overall simulation, it shows that ED-MOFSO achieves improved metrics, including residual energy (14.29 J), delay (11.1 ms), packet delivery ratio (0.994), routing overhead (0.10), and throughput (1.233 Mbps) when compared to conventional Elephant Herding Optimization (EHO) greedy and Ant Colony Optimization Integrated Glow-worm Swarm Optimization (ACI-GSO).

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Hierarchical WSN protocol with fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB-ER)

Cited by 31 publications

References 32 publications

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

Energy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor Networks

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