UWB-assisted real-time localization in wireless sensor networks

A safety monitoring and accident warning system for underground construction site has been designed in our previous work based on wireless sensor networks (WSNs). Real-time localization of mobile targets is crucial for tracking the related incidents. However, the current RSSI-based localization approach struggles to achieve the required performance. This is due to the limited ranging accuracy of RSSI devices. In this paper, we investigate various ways of improving the localization accuracy and propose our solution of a hybrid UWB-assisted approach. We argue that a hybrid UWB-assisted RSSI ranging has the best overall performance for our application. We show with both mathematical analysis and demonstration system that, instead of implementing a full UWB network, our approach can improve the accuracy to our desired level with only a small number of additional UWB anchor nodes. © 2013 IEEE

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

Improved Correction Localization Algorithm Based on Dynamic Weighted Centroid for Wireless Sensor Networks

Abstract: For wireless sensor network applications that require location information for sensor nodes, locations of nodes can be estimated by a number of localization algorithms. However, precise location information may be unavailable due to the constraint in energy, computation, or terrain. An improved correction localization algorithm based on dynamic weighted centroid for wireless sensor networks was proposed in this paper. The idea is that each anchor node computes its position error through its neighbor anchor nodes in its range, the position error will be transform to distance error, according the distance between unknown node and anchor node and the anchor node's distance error, the dynamic weighted value will be computed. For each unknown node, it can use the coordinate of anchor node in its range and the dynamic weighted value to compute it's coordinate. Simulation results show that the localization accuracy of the proposed algorithm is better than the traditional centroid localization algorithm and weighted centroid localization algorithm, the position error of three algorithms is decreased along radius increasing, where the decreased trend of our algorithm is significant

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

AN ENERGY EFFICIENT CROSS-LAYER NETWORK OPERATION MODEL FOR MOBILE WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are modern technologies used to sense/control the environment whether indoors or outdoors. Sensor nodes are miniatures that can sense a specific event according to the end user(s) needs. The types of applications where such technology can be utilised and implemented are vast and range from households’ low end simple need applications to high end military based applications. WSNs are resource limited. Sensor nodes are expected to work on a limited source of power (e.g., batteries). The connectivity quality and reliability of the nodes is dependent on the quality of the hardware which the nodes are made of. Sensor nodes are envisioned to be either stationary or mobile. Mobility increases the issues of the quality of the operation of the network because it effects directly on the quality of the connections between the nodes

Research of Applying Machine Learning Methods to Outlier Detection in Wireless Sensor Networks

兵庫県立大学大学院201