Fuzzy-TOPSIS based Cluster Head selection in mobile wireless sensor networks

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.

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

The optimum use of energy in wireless sensor networks WSNs is very important. The recent researches show that organising the network nodes in some clusters leads to higher efficiency of energy and finally it increases the lifetime of the network. So, controlling the number and the location of the clusters head CHs and also the size of the clusters about the node number leads to a balance in energy use of the CHs and increasing the lifetime of the network. Clustering-based routing protocols are energy-efficient protocols that improve the lifetime of a wireless sensor network. The objective of the clustering is to minimise the total transmission power by aggregating into a single path for prolonging the network lifetime. In this paper, fuzzy C-means FCM algorithm is used for the optimum number of the CHs and their location. Using FCM in WSNs helps changing the LEACH protocol parameters while execution. The results show that the hybrid algorithm increases the lifetime of the network in comparison to the LEACH algorithm.

A new approach for decreasing energy in wireless sensor networks with hybrid LEACH protocol and fuzzy C-means algorithm

This paper presents a survey of applications of fuzzy logic in real Wireless Sensor Networks (WSNs). The use of fuzzy logic in WSNs is shown to be a promising technique since it allows combining and evaluating diverse parameters in an efficient manner. Fuzzy logic is a good approach due to the execution requirements can be easily supported by sensor nodes, while it is able to improve the overall network performance. The potential of fuzzy logic goes beyond traditional systems and can be used in many research fields (statistics, quality control, optimization techniques . . . ), allowing multidisciplinary approaches and performance improvements. In WSNs, fuzzy logic has been used to improve decision-making, reduce resource consumption, and generally increase performance through efficient deployment, localization, clustering and cluster head election, routing, data aggregation, security, etc.

Fuzzy logic and Wireless Sensor Networks – A survey

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

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Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

A Fuzzy Based Clustering Protocol for Energy-efficient Wireless Sensor Networks

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A Fuzzy Based Clustering Protocol for Energy-efficient Wireless Sensor Networks