Development of Application Specific Clustering Protocols for Wireless Sensor Networks

Abstract

Applications in wireless sensor networks (WSNs) span over various areas like weather forecasting to measuring soil parameters in agriculture, and from battle_eld to health monitoring. Constrained battery power of sensor nodes make the network design a challenging task. Amongst several research areas in WSN, designing energy e_cient protocols is a prominent area. Clustering is a proven solution to enhance the network lifetime by utilizing the availablebattery power e_ciently. In this thesis, a hypothetical overview has been done to study the strengths and weaknesses of existing clustering algorithms that inspired the design of distributed and energy e_cient clustering in WSN. Distributed Dynamic Clustering Protocol (DDCP) has been proposed to allow all the nodes to take part in the cluster formation scheme and data transmission process. This protocol consists of a cluster-head selection algorithm, a cluster formation scheme and a routing algorithm for the data transmission between cluster-heads and the base station. All the sensor nodes present in the network takes part in the cluster-head selection process. Staggered Clustering Protocol (SCP) has been proposed to develop a new energy e_cient clustering protocol for WSN. This algorithm is aiming at choosing cluster-heads that ensure both the intra-cluster data transmission and inter-cluster data transmission are energy-e_cient. The cluster formation scheme is accomplished by exchanging messages between non-cluster-head nodes and the cluster-head to ensure a balanced energy loadamong cluster-heads. An energy e_cient clustering algorithm for wireless sensor networks using particle swarm optimization (EEC-PSO) has been proposed to ensure energy e_ciency by creating optimized number of clusters. It also improves the link quality among the cluster-heads with the cluster member nodes. Finding a set of suitable cluster-heads from N sensor nodes is considered as non-deterministic polynomial (NP)-hard optimization problem. The application of WSN in brain computer interface (BCI) has been proposed to detect the drowsiness of a driver on wheels. The sensors placed in a braincap worn by the driver are divided into small clusters. Then the sensed data, known as EEG signal, are transferred towards the base station through the cluster-heads. The base station may be placed at a nearby location of the driver. The received data is processed to take a decision when to trigger the warning tone.