Fuzzy C Means based Hierarchical Routing Protocol in WSN with Ant Colony Optimization

Ghosh, Swaroop; Mondal, Sanjoy; Biswas, Utpal

doi:10.1109/icatcct.2016.7912021

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…In this section, the statistical inference of the network lifetime for the baseline protocols in the above scenarios was illustrated by using the paired t-test, as shown in [42].…”

Section: ) Statistical Inference For Network Lifetimementioning

confidence: 99%

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

et al. 2020

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A wireless sensor network (WSN) is an important part of the Internet of Things (IoT). However, sensor nodes of a WSN-based IoT network are constraining with the energy resources. A clustering protocol provides an efficient solution to ensure energy saving of nodes and prolong the network lifetime by organizing nodes into clusters to reduce the transmission distance between the sensor nodes and base station (BS). However, existing clustering protocols suffer from issues concerning the clustering structure that adversely affects the performance of these protocols. In this study, we propose an improved energyefficient clustering protocol (IEECP) to prolong the lifetime of the WSN-based IoT. The proposed IEECP consists of three sequential parts. First, an optimal number of clusters is determined for the overlapping balanced clusters. Then, the balancedstatic clusters are formed on the basis of a modified fuzzy C-means algorithm by combining this algorithm with a mechanism to reduce and balance the energy consumption of the sensor nodes. Lastly, cluster heads (CHs) are selected in optimal locations with rotation of the CH function among members of the cluster based on a new CH selection-rotation algorithm by integrating a back-off timer mechanism for CH selection and rotation mechanism for CH rotation. In particular, the proposed protocol reduces and balances the energy consumption of nodes by improving the clustering structure, where IEECP is suitable for networks that require a long lifetime. The evaluation results prove that the IEECP performs better than existing protocols.

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“…In this section, the statistical inference of the network lifetime for the baseline protocols in the above scenarios was illustrated by using the paired t-test, as shown in [42].…”

Section: ) Statistical Inference For Network Lifetimementioning

confidence: 99%

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The proposed protocol utilizes ACO to discovery the optimum path from the sensor nodes to the sink and uses the AIS to solve the packet loop problem and to control route direction. Ghosh et al [28] proposed a cluster-based and chain-based routing algorithm (FCM-ACO) to improve the data fusion performance and extend the network lifetime. Under FCM-ACO, Fuzzy C Means clustering [29] is used to divide the network into several clusters.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks

Keegan

Mtenzi

et al. 2019

IEEE Access

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Wireless Sensor Networks (WSNs) are a type of self-organizing networks with limited energy supply and communication ability. One of the most crucial issues in WSNs is to use an energy-efficient routing protocol to prolong the network lifetime. We therefore propose the novel Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) for WSNs. EBAR adopts a pseudo-random route discovery algorithm and an improved pheromone trail update scheme to balance the energy consumption of the sensor nodes. It uses an efficient heuristic update algorithm based on a greedy expected energy cost metric to optimize the route establishment. Finally, in order to reduce the energy consumption caused by the control overhead, EBAR utilizes an energy-based opportunistic broadcast scheme. We simulate WSNs in different application scenarios to evaluate EBAR with respect to performance metrics such as energy consumption, energy efficiency, and predicted network lifetime. The results of this comprehensive study show that EBAR provides a significant improvement in comparison to the state-of-the-art approaches EEABR, SensorAnt, and IACO.

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“…However, determining the optimal distribution path can be a complex challenge, especially when large and complex distribution networks are involved. To address this complex problem, the Ant Colony Optimization (ACO) method has become an attractive research focus in distribution line optimization (Ghosh, 2016;Zhao et al, 2021;Li et al, 2022). This method is inspired by the behavior of ant colonies in finding the shortest path from the nest to the food source and has been successfully applied in various optimization applications.…”

Section: Introductionmentioning

confidence: 99%

Determining The Optimal Milk Distribution Route Using The Ant Colony Optimization Method on Milk Industry

Hakim Gusti,

Aryanny

2023

Spek Ind

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A good distribution system will ensure the availability of products or goods needed by the community. The objective of this research is to identify the most efficient route for distributing UHT milk on Milk Industry, with the goal of minimizing the overall distance traveled during distribution. The contribution of this study is to determine the optimal distribution route for UHT milk on Milk Industry. Data analysis or data calculation is done using Ant Colony optimization. The independent variables used are the initial distribution route data of the company, agent location data, product demand data, and distance data between agents. The results of this study found that the company's route resulted in a total route distance of 251.95 km while for the optimal total distance Ant Colony Optimization method is the total route distance of 230.2 km. Thus, the optimal route of the distribution of Ant Colony Optimization method is better than the initial route of the company with a total route distance savings of 21.75 km with a percentage of savings of 8.63%.

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Fuzzy C Means based Hierarchical Routing Protocol in WSN with Ant Colony Optimization

Cited by 11 publications

References 14 publications

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks

Determining The Optimal Milk Distribution Route Using The Ant Colony Optimization Method on Milk Industry

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