Analyzing the Performance of Centralized Clustering Techniques for Realistic Wireless Sensor Network Topologies

AbstractClustering techniques in wireless sensor networks enables energy efficient coordination among the densely deployed nodes for data delivery till the base station. Many clustering protocols have been suggested in the recent past. The topology of the nodes, mostly seen in the literature, is of random type. This paper analyzes the performance aspects of various centralized clustering techniques for wireless sensor networks. LEACH-Centralized, KMeans-CP, FCM-CP and HSA-CP protocols have been compared with respect to clustering and data delivery process for various realistic topologies. The simulations were performed for these protocols and performance of the protocols has been critically analyzed. HSA-CP clustering method performs better compared to other techniques for almost each topology examined in the paper

Energy Efficient Protocol with Static Clustering (EEPSC) Comparing with Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol

A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization. Keywords—Clustering methods, energy efficiency, routing protocol, wireless sensor network

Multiple Region Coverage Path Planning for Autonomous Underwater Vehicle.

Coverage path planning methodology for an autonomous underwater vehicle to search multiple non-overlapping regions has been proposed in the paper. The proposed methodology is based on the genetic algorithm (GA). The GA used in the proposed methodology has been tuned for the specific problem, using design of experiment on an equivalent travelling salesman problem benchmark instance. Optimality of the generated paths was analysed through simulation studies. Results indicated that the proposed methodology generated shorter paths in comparison to conventional methods

Particle Swarm Optimization for the Clustering of Wireless Sensors

Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a \u27swarm\u27 of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network

Online) An Open Access

ABSTRACT The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. This paper presents a new centralized adaptive Energy Based Clustering protocol through the application of Self organizing map neural networks (called EBC-S) which can cluster sensor nodes, based on multi parameters; energy level and coordinates of sensor nodes. We applied some maximum energy nodes as weights of SOM map units; so that the nodes with higher energy attract the nearest nodes with lower energy levels. Therefore, formed clusters may not necessarily contain adjacent nodes. The new algorithm enables us to form energy balanced clusters and equally distribute energy consumption. Simulation results and comparison with previous protocols (LEACH and LEA2C) prove that our new algorithm is able to extend the lifetime of the network. Keywords: Energy Based Clustering, Self Organizing Map Neural Networks, Wireless Sensor Networks INTRODUCTION The most important difference of Wireless Sensor Network (WSNs) with other wireless networks may be constraints of their resources, especially energy which usually arise from small size of sensor nodes and their batteries which is a prerequisite to WSNs main applications. The main and most important reason of WSNs creation was continuous monitoring of environments where are too hard or impossible for human to access or stay. So there is often low possibility to replace or recharge the dead nodes as well. The other important requirement is that we need a continuous monitoring so the lifetime and network coverage of these networks are our great concerns. As a result, as energy conservation is the main concern in WSNs, but also it should be gained with balanced distribution in whole network space. Balanced distribution of energy in whole network will lead to balanced death of nodes in all regions preventing from lacking network coverage in a rather large part of the network. Energy conservation should be gained by wisely management of energy sources. Several energy conservation schemes have been proposed in the literature while there is a comprehensive survey of energy conservation methods for WSNs and the taxonomy of all into three main approaches (duty-cycling, data reduction, and mobility based approaches

A Mobile BS and Multi-Hop LEACH-C Extension for WSNs

It is critical for wireless sensor networks (WSNs) to have an uninterrupted power source. Increasing the lifetime of WSNs will require employing an energy preservation mechanism. In many WSN applications, sensors are used to detect events and collect environmental data, which are then delivered to a sink node or a base station node (BS) through a communication link. Sensors consume energy during wireless data communication, which is higher than the computational energy. This paper proposes an enhanced LEACH-C protocol that manages the network energy consumption and prolongs sensors lifetime. The proposed protocol is named Leach-C Multihop and Mobile (LEACH-CM). The proposed LEACH-CM protocol distributes the energy consumption between the network nodes and enables more data to be transmitted over a WSN. The proposed LEACH-CM protocol is simulated in the NS2 simulation, which is supported by the μ-AMPS project and is developed by MIT researchers. The simulation result shows that the proposed LEACH-CM protocol can decrease the energy consumption, and increase the amount of transmitted data compared to the LEACH-C protocol. Furthermore, the LEACH-CM protocol outperforms the LEACH-C protocol when comparing the dead time of the first node, which is a good indication of network stability

Project schedule optimisation utilising genetic algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class