Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches

TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption

A survey on gas leakage source detection and boundary tracking with wireless sensor networks

Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs

CAF: Cluster Algorithm and A-Star with Fuzzy Approach for Lifetime Enhancement in Wireless Sensor Networks

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria

Towards Spatial Queries over Phenomena in Sensor Networks

Today, technology developments enable inexpensive production and deployment of tiny sensing and computing nodes. Networked through wireless radio, such senor nodes form a new platform, wireless sensor networks, which provide novel ability to monitor spatiotemporally continuous phenomena. By treating a wireless sensor network as a database system, users can pose SQL-based queries over phenomena without needing to program detailed sensor node operations. DBMS-internally, intelligent and energyefficient data collection and processing algorithms have to be implemented to support spatial query processing over sensor networks. This dissertation proposes spatial query support for two views of continuous phenomena: field-based and object-based. A field-based view of continuous phenomena depicts them as a value distribution over a geographical area. However, due to the discrete and comparatively sparse distribution of sensor nodes, estimation methods are necessary to generate a field-based query result, and it has to be computed collaboratively ‘in-the-network’ due to energy constraints. This dissertation proposes SWOP, an in-network algorithm using Gaussian Kernel estimation. The key contribution is the use of a small number of Hermite coefficients to approximate the Gaussian Kernel function for sub-clustered sensor nodes, and processes the estimation result efficiently. An object-based view of continuous phenomena is interested in aspects such as the boundary of an ‘interesting region’ (e.g. toxic plume). This dissertation presents NED, which provides object boundary detection in sensor networks. NED encodes partial event estimation results based on confidence levels into optimized, variable length messages exchanged locally among neighboring sensor nodes to save communication cost. Therefore, sensor nodes detect objects and boundaries based on moving averages to eliminate noise effects and enhance detection quality. Furthermore, the dissertation proposes the SNAKE-based approach, which uses deformable curves to track the spatiotemporal changes of such objects incrementally in sensor networks. In the proposed algorithm, only neighboring nodes exchange messages to maintain the curve structures. Based on in-network tracking of deformable curves, other types of spatial and spatiotemporal properties of objects, such as area, can be provided by the sensor network. The experimental results proved that our approaches are resource friendly within the constrained sensor networks, while providing high quality query results

A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles

The evolution in micro-electro-mechanical systems technology (MEMS) has triggered the need for the development of wireless sensor network (WSN). These wireless sensor nodes has been used in many applications at many areas. One of the main issues in WSN is the energy availability, which is always a constraint. In a previous research, a relocating algorithm for mobile sensor network had been introduced and the goal was to save energy and prolong the lifetime of the sensor networks using Particle Swarm Optimization (PSO) where both of sensing radius and travelled distance had been optimized in order to save energy in long-term and shortterm. Yet, the previous research did not take into account obstacles’ existence in the field and this will cause the sensor nodes to consume more power if obstacles are exists in the sensing field. In this project, the same centralized relocating algorithm from the previous research has been used where 15 mobile sensors deployed randomly in a field of 100 meter by 100 meter where these sensors has been deployed one time in a field that obstacles does not exist (case 1) and another time in a field that obstacles existence has been taken into account (case 2), in which these obstacles has been pre-defined positions, where these two cases applied into two different algorithms, which are the original algorithm of a previous research and the modified algorithm of this thesis. Particle Swarm Optimization has been used in the proposed algorithm to minimize the fitness function. Voronoi diagram has also used in order to ensure that the mobile sensors cover the whole sensing field. In this project, the objectives will be mainly focus on the travelling distance, which is the mobility module, of the mobile sensors in the network because the distance that the sensor node travels, will consume too much power from this node and this will lead to shortening the lifetime of the sensor network. So, the travelling distance, power consumption and lifetime of the network will be calculated in both cases for original algorithm and modified algorithm, which is a modified deployment algorithm, and compared between them. Moreover, the maximum sensing range is calculated, which is 30 meter, by using the binary sensing model even though the sensing module does not consume too much power compared to the mobility module. Finally, the comparison of the results in the original method will show that this algorithm is not suitable for an environment where obstacle exist because sensors will consume too much power compared to the sensors that deployed in environment that free of obstacles. While the results of the modified algorithm of this research will be more suitable for both environments, that is environment where obstacles are not exist and environment where obstacles are exist, because sensors in this algorithm .will consume almost the same amount of power at both of these environments