Review: Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Communication protocols for wireless sensor networks: A survey and comparison

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

https://www.mdpi.com/1424-8220/19/2/322/pdf

Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

Data mining and approaches based on it have always been of approaches that have been considered in solving problems in the field of computer, but on some issues, this approach has been neglected. The area of wireless sensor networks and specifically the issue of optimal determining of the cluster head node are of these issues. To solve the problem of optimal determining of the cluster head node, Naive Bayes that is the subset of data mining techniques is used in this paper. The results obtained after simulation of the presented algorithm show that the efficiency of this algorithm is significantly higher compared with other approaches that have so far been used to solve this problem, and thus it can be said that using this algorithm will lead to improved outcomes of solving this problem.

Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks

On the basis of analyzing the Low-Energy Adaptive Clustering Hierarchy (LEACH), a clustering routing algorithm for Wireless Sensor Network (WSN) based on ant colony algorithm (ACA) is proposed in this paper. We applied the ACA to inter-cluster routing mechanism and looked for the best path from cluster heads to base station. Thanks to the algorithm, the transmission of information, for the cluster heads node away from the base station (Sink), along the best path was achieved and the energy consuming of cluster heads node was decreased. Meanwhile, not only the node residual energy, but also the distance between the cluster heads was considered for the selection of cluster heads. It resulted in the more even distribution of cluster heads. Simulation result indicates that the new algorithm has a more than 30% increase in extension of network life compared with LEACH.

An Ant Colony Clustering Routing Algorithm for Wireless Sensor Networks

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An Ant Colony Clustering Routing Algorithm for Wireless Sensor Networks