Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Computational Intelligence Algorithms for Optimisation of Wireless Sensor Networks

Recent studies have tended towards incorporating Computation Intelligence, which is a large umbrella for all Machine Learning and Metaheuristic approaches into wireless sensor network (WSN) applications for enhanced and intuitive performance. Meta-heuristic optimisation techniques are used for solving several WSN issues such as energy minimisation, coverage, routing, scheduling and so on. This research designs and develops highly intelligent WSNs that can provide the core requirement of energy efficiency and reliability. To meet these requirements, two major decisions were carried out at the sink node or base station. The first decision involves the use of supervised and unsupervised machine learning algorithms to achieve an accurate decision at the sink node. This thesis presents a new hybrid approach for event (fire) detection system using k-means clustering on aggregated fire data to form two class labels (fire and non-fire). The resulting data outputs are trained and tested by the Feed Forward Neural Network, Naive Bayes, and Decision Trees classifier. This hybrid approach was found to significantly improve fire detection performance against the use of only the classifiers. The second decision employs a metaheuristic approach to optimise the solution of WSNs clustering problem. Two metaheuristic-based protocols namely the Dynamic Local Search Algorithm for Clustering Hierarchy (DLSACH) and Heuristics Algorithm for Clustering Hierarchy (HACH) are proposed to achieve an evenly balanced energy and minimise the net residual energy of each sensor nodes. This thesis proved that the two protocols outperforms state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of network lifetime and maintains a favourable performance even under different energy heterogeneity settings

Review of Optimization Problems in Wireless Sensor Networks

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