We propose a RF-based localization system that works using a single anchor node. The anchor is equipped with a switched-beam directional antenna that installs on the ceiling of a room and collects signal strength information sufficient for absolute 2D target positioning. Indoor measurements are used to show satisfactory localization results with range-free (proximity), range-based and fingerprinting schemes. close

Implementing a localization service for a wireless sensor network is a challenging task. Sometimes the nodes are deployed in sparse topologies, while other times they are densely packed inside a building. Some environments are relatively uncluttered, while others have obstacles that impede the node placement and strongly affect the radio signal. To address the problem of localization in heterogeneous scenarios, we present a range-free scheme based on the neural network paradigm of Self-Organizing Maps (SOM). This method is lightweight, works with or without anchor nodes, and has proven effective in a variety of simulated scenarios. We propose three variants (SOM-V, SOM-A and SOM-R) that achieve accurate results in sparse topologies but are also suitable to localize nodes in dense networks or deployments with anisotropic layout. We evaluate the localization results using extensive simulations, comparisonswith other range-free techniques, Cramér-Rao bound analysis and test cases with data from in-field measurements. Finally, we demonstrate analytically that the proposed scheme has low computation and communication overheads, making it suitable for resource-constrained networks. close

Received Signal Strength (RSS) data collected within a wireless network can be used to obtain either range estimates or connectivity information. Both approaches lead to localization schemes that require no additional hardware. It is not clear, however, when a range-based scheme should be used in favor of a connectivity-based one. We use analysis of the Fisher information and the Cramér-Rao Bound (CRB) to characterize the error of both approaches. We find the existence of a critical connectivity value below which using a RSS data for range-based localization is counter-productive. We show that an approximation of the critical connectivity value can be computed as a function of the network size and the parameters of the propagation model. close

Connectivity-based localization schemes compute the node positions using proximity information collected within the network. In many cases of practical interest, Received Signal Strength (RSS) measurements are available, and connectivity data can be obtained by comparing the RSS against a threshold. We use the Cramér-Rao bound (CRB) analysis to determine the threshold value that minimizes the localization error. The CRB is based on knowledge of the propagation model's parameters and the true node positions. Since this information is not available to a localization scheme, we approximate the optimal threshold value using a function that depends only on the number of nodes in the network. We use extensive simulations and RSS data from in-field experiments to validate the results of the proposed approach. close

In sensor networks applications, localization is an essential service that computes the node positions on the basis of a limited amount of initial information. The task is particularly challenging in resource-constrained deployments typical of many real-world applications, where nodes have reduced computational capabilities, do not have hardware for range measurements, and operates in sparse topologies. In this thesis we propose a range-free, anchor-free solution that works using connectivity information only. The approach, suitable for deployments with strict cost constraints, is based on the neural network paradigm of Self-Organizing Maps (SOM). We present a lightweight SOM-based algorithm to compute virtual coordinates that are effective for location-aided routing. If absolute coordinates are required, this algorithm can efficiently exploit information of few anchor nodes to compute absolute maps. Results of extensive simulations show improvements over the popular Multi-Dimensional Scaling (MDS) scheme, especially for networks with low connectivity, which are intrinsically harder to localize, and in presence of irregular radio pattern or anisotropic deployment. We analytically demonstrate that the proposed scheme has low computation and communication overheads; hence, making it suitable for resource-constrained networks.
In the second part of this work, we introduce a directional antenna designed to operates with COTS sensor nodes. After using experimental tests and theoretical models to characterize the communication improvements, we implement a simple algorithm that exploits the directivity of the antenna to estimate the angular position of nearby nodes. Experimental results demonstrate that an inexpensive and compact antenna can be used to derive angle information useful in solving the localization problem. close

Motivated by recent interest in directional antennas for WSNs, we propose a Four-Beam Patch Antenna (FBPA) designed to meet the size, cost and complexity constraints of sensor nodes. We use in-field experiments with COTS motes to demonstrate substantial benefits to WSN applications. Used outdoors, the FBPA extends the communication range from 140m to more than 350m, while indoors it suppresses the interference due to multipath fading by reducing the signal variability of more than 70%. We also show interference suppression from IEEE 802.11g systems and discuss the use of the antenna as a form of angular diversity useful to cope with the variability of the radio signal. Experimental data are analyzed to derive model parameters intended for use in future network simulations. close

Server-based architectures used in traditional Wireless Sensor Network (WSN) applications are not suitable when the sensors are installed near the user and local access is desirable. We address this problem by proposing the Personal Sensor Network (PSN), a computer-less architecture that enables users to access the sensor data using their cell phones or any other Bluetooth enabled devices. The use of COTS hardware and widely available software resources results in a solution easy to implement and simple to interface with other WSNs. close

Localization is an essential service for many wireless sensor network applications. While several localization schemes rely on anchor nodes and range measurements to achieve fine-grained positioning, we propose a range-free, anchor-free solution that works using connectivity information only. The approach, suitable for deployments with strict cost constraints, is based on the neural network paradigm of Self-Organizing Maps (SOM). We present a lightweight SOM-based algorithm to compute virtual coordinates that are effective for location-aided routing. This algorithm can also exploit the location information, if available, of few anchor nodes to compute absolute positions. Results of extensive simulations show improvements over the popular Multi-Dimensional Scaling (MDS) scheme, especially for networks with low connectivity, which are intrinsically harder to localize, and in presence of irregular radio pattern or anisotropic deployment. We analytically demonstrate that the proposed scheme has low computation and communication overheads; hence, making it suitable for resource-constrained networks. close