CHAPTER ONE

INTRODUCTION

1.1       Background to Study

A sensor node is usually a low size, weight and power (SWAP) device with an antenna, a CPU, an expansion connector, a power switch, a radio, and is powered by battery. Figure 1.1 shows an example of a sensor node. A sensor network, which consists of multiple sensor nodes, is a group of specialized transducers with a communications infrastructure intended to monitor and record conditions at diverse locations. It can be used to monitor quantities such as location, temperature, humidity, pressure, among others [1–3], and can be either wired or wireless depending on the connection between sensor nodes. In a wired sensor network, two sensor nodes are connected through a wire. In a wireless sensor network (WSN), sensor nodes communicate with each other through agreed protocols. Therefore, comparing wired sensor networks with WSNs, wired sensor networks are more secure and faster than wireless sensor networks in data transfer speed [4]. However, they lack flexibility. Meanwhile, the implementation of a wired sensor network is more expensive than a WSN due to the cost of wires, connectors and labor. Also, a large wired sensor network is more difficult to manage than a WSN. On the other hand, WSNs are more flexible and power efficient than wired sensor networks [2].

A WSN can be either fully connected, in which case all sensor nodes communicate with each other, or partly connected, in which case one sensor node only communicates with its neighbors. In a fully connected WSN, sensor nodes exchange information by transmitting and receiving signals from all other nodes. On the other hand, in a partly connected WSN, each sensor node collects limited information. Therefore, a fully connected WSN benefits from a global network knowledge and provides more accurate results than a partly connected WSN, but costs more in terms of energy and bandwidth. A WSN can be either homogeneous or heterogeneous [5]. In a homogeneous network, all sensor nodes are identical in terms of battery life, communication range, and hardware complexity. On the other hand, in heterogeneous networks, sensor nodes have different communication ranges and functions. Generally speaking, the algorithms which are designed for homogeneous networks are not suitable for heterogeneous networks.

Comparing to traditional devices, the greatest advantages of WSNs are improved robustness and scalability [3]. In general, WSNs have energy advantage compared to other devices since sensors are small, have low power cost and detection advantage since a more dense sensor field improves the odds of detecting a signal source within the range. Although the main driving forces for WSNs are fault tolerance, energy gain and spatial capacity gain, WSNs have bandwidth limits [10]. Meanwhile, due to mobile applications, one of the most important constraints on sensor nodes is the low power consumption requirements [2]. Therefore, sensor network protocols must focus primarily on power conservation. Also, to make sure the nodes work efficiently, these nodes must operate in high volumetric densities, have low production cost and be dispensable, be autonomous and operate unattended and be adaptive to the environment [2,11,12].

In many applications, measured sensor data are meaningful only when the location of sensors is accurately known. Nowadays, the most widely used technique for localization purpose is the Global Positioning System (GPS), which was developed in 1973 to overcome the limitations of previous navigation systems [24, 25] and it has been used for both military and industry purposes. GPS offers 3D localization based on direct line-of-sight(LOS) with at least four satellites, providing an accuracy up to three meters. However, GPS has some limitations [26–28]. First of all, GPS cannot be implemented under harsh environments. For example, in the presence of dense forests, mountains or other obstacles that block the LOS from GPS satellite, GPS cannot work. Second, GPS cannot be implemented under the indoor environment. Third, while the cost for GPS equipment has been dropping over the years, it is still not suited for mass-produced cheap sensor boards, phones and even PDAs. On the other hand,the Federal Communications Commission (FCC) in the US has required wireless providers to locate mobile users within 10 meters for 911 calls [29]. Therefore, the accurate estimation of position should be performed even in challenging environments. To overcome GPS limitations, researchers have developed fully GPS-free techniques for locating nodes as well as techniques where few nodes, commonly called anchors, use GPS to determine their location and, by broadcasting it, help other nodes in calculating their own position without using GPS. Therefore, the problem of location estimation using WSNs is formulated. To localize a node, several reference nodes, termed anchors with known locations are used to localize nodes with unknown locations. Localization in WSNs has been used in many applications, such as inventory tracking, forest fire tracking, home automation and patient monitoring [30]. When both anchors and other nodes communicate with the node that needs to be localized, a sensor network is called a cooperative WSN. In general, WSNs can be classified as cooperative and non-cooperative WSNs. The concept of cooperative WSNs relies on direct communication between nodes, which means nodes can communicate with each other and in localization problems, a node can estimate its location by sending or receiving signals from other nodes [31]. On the other hand, in non-cooperative WSNs, no communications take place between nodes. Nodes can only communicate with anchors and estimate their locations through anchors. Figure 1.4 shows an example of cooperative WSNs. In the figure, node 1, 2 and 3 communicate with each other, which indicates that distance measurements d12, d13, and d23 are available, and the network is a cooperative WSN because nodes communicates with each other. Figure 1.5 shows an example of non-cooperative WSNs. In the figure, the link between node 1 and node 2 is not present. Therefore, the network is a non-cooperative WSN. It is against this background that this study was carried out on sensor nodes localization using RSS measurements in the presence of sensor location errors.

1.2       Statement of Problem

In recent papers, detailed survey on recent localization techniques and concepts with their fundamental limits, challenges and applications are presented. Although literature survey on localization techniques is available, only a few papers exist that focus on range free localization techniques without focusing on recent advanced techniques and applications. Thus, the researches in these areas are outdated, whereas focus only on ultrasonic positioning systems. However, they do not discuss positioning neither from the perspective of energy efficiency nor from the requirement in recent applications, such as ambient assisted and health living applications. The surveys also provide notable categorization of various fingerprint-based outdoor positioning techniques, discussing how each method works. So, we intend to present a survey focused specially on range free techniques. Moreover, the rapid growth of various localization approaches in this field and the need for a complete and up-to-date survey of the techniques, applications and future trends, provide the motivation for this study.

In other applications, such as fire protection in a building, one node is placed inside each room, whose location is known to all anchors. If a fire actives a node in any room, the active node at this known location needs to be detected. In the absence of transmission from a node, each anchor only receives noise, and in the presence of transmission from a node, each anchor receives signal plus noise. Therefore, location detection is needed to decide whether a node is active or not. In this case, the problem of location detection using WSNs is formulated. In the literature, location detection in WSNs has been studied in [57], which discretizes the problem to obtain an N-ary hypothesis testing problem. However, the performance depends on the grid size. In [58], a centralized sensor network with unknown fading coefficients has been studied. Although centralized methods may give a better performance, it is costly in large WSNs. None of these works have studied location detection using distributed methods, also none of these works consider fading environments with explicit incorporation of the fading distribution in deriving the threshold for location detection. Hence, distributed location detection problems in the absence of fading and in the presence of fading need to be studied

1.3       Research Objectives

This study has the main objective of assessing sensor nodes localization using RSS measurements in the presence of sensor location errors. Specifically it seeks to;

1. Determine the effects of measurement errors on sensor nodes location
2. Assess the localization techniques available to solve positioning problems in the WSNs such as the RSS
3. Ascertain the effectiveness of using RSS measurements in sensor nodes localization considering its pitfalls and challenges