Cortina is a distributed Real-Time Location System (RTLS) designed to track assets or people moving indoors. Our solution leverages a low-cost, low-power Wireless Sensor Network (WSN) based on the IEEE 802.15.4 radio standard. The network, which consists of wall-plugged nodes, is designed to be self-configuring, self-healing and self-calibrating, thus reducing deployment and maintenance costs. Assets and people are tracked using small battery operated wireless tags that collect Received Signal Strength (RSS) measurements from nearby nodes. The tags also include an accelerometer for activity recognition, and a barometric pressure sensor to detect the floor plan. We have conducted experiments over a 2000 m 2 area instrumented with eighteen sensor nodes. Our initial results show that the system can track people in real-time with an average error of 2.8 m

RF-based localization has gained popularity as a low-cost solution to support position awareness in ad hoc networks. The received signal strength (RSS) measured by pairs of nodes can be used to obtain either range estimates or connectivity information. It is not clear, however: 1) when a range-based scheme should be used in favor of a connectivity-based one, and 2) how to optimally convert the RSS into connectivity data. This paper uses analysis of the Fisher information and the Cramér-Rao bound (CRB) to answer these questions. Solutions are found by comparing the network connectivity against two values: the critical connectivity (CC) and the optimal connectivity (OC). After discussing the properties of both values, we show how their approximation can be used to improve the performance of RF-based localization systems

Recent years have witnessed the emergence of novel application paradigms such as the Wireless Sensor Network and Context Aware computing. Among the challenges posed by these applications, localization – i.e. the process of locating people and/or devices – has emerged as a key problem that has found only partial answers. Although GPS receivers are common on many consumer electronic devices, alternative solutions are needed when locating devices that strive to be small and inexpensive, as in sensor networks, or when supporting indoor positioning. This dissertation focuses on radio-based positioning schemes suitable for applications where GPS is not a viable solution. [...]

This paper presents the theoretical analysis and the experimental evaluation of a new switched beam antenna designed to operate at 2.45 GHz. The antenna enables direction of arrival estimation using six directional planar elements arranged to form a platonic solid geometry. It also supports polarization diversity, and it is suitable for single-anchor indoor positioning applications. We adopt the Cramer-Rao bound to study the estimation accuracy of the proposed antenna in absolute 2-D target positioning using received signal strength measurements. First, we describe the design principles for the radiators, we provide an extensive characterization of the switched antenna prototype, and we discuss positioning applications. We then report experimental data that support the results of the theoretical analysis and show consistency between theoretical expectation and the measurements. Finally, we discuss results from proof-of-concept operative indoor positioning example, showing an average localization error as low as 1.7 m.

Switched Beam Antennas support radio positioning via Angle Of Arrival (AOA) information collected from nearby devices. Using an analytical approach, first we present the Cramér-Rao Bound (CRB) for AOA estimates using identically and equally spaced antenna elements. Then we analyze the results to devise design guidelines for improved AOA estimation. The design parameters considered are: 1)the number of antenna elements, 2) their directivity, and 3) the type of polarization. The effect of each parameter is discussed in detail; additionally, experimental results at 2.45 GHz are reported to evaluate the effect of different antenna polarization on the CRB.

This paper proposes a new switched beam array optimized for 2.45 GHz wireless indoor applications. The antenna supports directional communication to enable spatial reusability, and polarization diversity to mitigate multipath propagation. It also supports absolute 2D target localization using measurements from a single anchor node. The paper describes the antenna design, the implementation and the experimental characterization, along with its positioning applications. The localization results obtained with data collected from indoor measurements, showing an average localization error as low as 1.7 m.

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.

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.

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.

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.

We measured the attenuation of signal strength for Telos-class motes between 25°C to 65°C, with a maximum loss of 8 dB at 65°C. A linear model for the combined reduction of the transmit power and receiver sensitivity is presented, which suggests significant impact on the transmission range and network services. Path loss and link budget analysis indicate a communication range reduction of up to 60%. Network simulations show that the maximum range reduction severely decreases average node connectivity and disrupts multihop data collection. When the received signal strength (RSS) values are used for localization without temperature compensation, ranging error increases by up to 150%. Moreover, Cramér-Rao Bound (CRB) analysis shows that even when the RSS values are compensated, localization errors increase as a result of reduced connectivity

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.

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.

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.

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.