This paper presents a distributed localization system for indoor environment based on a network of compact anchor nodes. The system is based on available low-cost commercial components and it is capable of standard WiFi connectivity at 2.45 GHz. The basic node is equipped with a switch-beam antenna, which is the enabling technology for a Direction of Arrival (DoA) estimation based on Maximum-Likelihood criteria. The adopted DoA estimation procedure is tolerant to noisy power measurements, making it suitable for low-cost effective solutions based on RSSI measurements. The node is designed to be placed in an unobtrusive area for the movement of a mobile target node and is capable to operating as an independent anchor, but it is optimized to cooperate with other identical nodes to form a coordinated network of anchors, capable to monitor an extended indoor area. An experimental characterization demonstrates the DoA performance of the single node.

Non intrusive systems-based traffic monitoring, is a paradigm for Intelligent Transportation Systems (ITS). Real time systems capable of providing prompt information, like loop detectors, laser/radar systems and video cameras, were proposed in the past but they are affected by some straits in deployment. In this paper a Traffic Monitoring Wireless Sensor Network system (TM-WSN), based on acoustic arrays and powered by effective post-processing, is proposed. The system is composed by an hierarchical scheme of Master Nodes and Sensor Nodes linked with a multi-hop protocol. Key features are traffic monitoring and queue detection to be performed in real-time at unprecedented space scale.

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.

This paper deals with a communication infrastructure needed to allow a swarm of robots performing dust cleaning and garbage collection task in an urban area. It outlines the required communication links, analyses them regarding security, describes in details the implementation and figure out some performance test results.

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.

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.

The adoption of wireless sensor networks (WSN) for wide area environmental monitoring is currently considered one of the most challenging application scenario for this emerging technology. The promise of an unmanaged, self-configuring and self-powered wireless infrastructure, with a continuously decreasing cost per unit, attracts the attention of both final users and system integrators, replacing previously deployed wired solutions and opening new business opportunities. This challenge might be afforded by resorting to a complete system level design to jointly address and optimize all the involved aspects. Besides, the design guide lines need to be inspired by the application requirements rather than technology driven. Following this approach, this paper presents an overall solution focusing both on node, network and remote user interface issues. The proposed platform in finally applied to a realistic user defined scenario oriented to agro-food production phase monitoring within GoodFood Integrated Project. The results of the validation phase presented highlight remarkable advantages both in terms of cost and complexity reduction and experienced QoS enhancement as well and, consequently, validating the WSN technology adoption.