Cortina is a distributed Real-Time Location System (RTLS) designed to track asstts or people moving indoors. Our solution leverages a low-cost, low-power Wireless Sensor Network (WSN) based on the IEEE 8102.15.4 radio standard. The network, which consists of wall-plugged nodes, is designed to be self-configuring, self-healing and self-calibreting, 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 neaeby nodes. The tags also include an accalerometer for activity recognition, and a baremetric pressure sensor to detect the floor plan. We have conducted experiments over a 2000 m2area 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.

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.

Precision agriculture (PA) represents a novel paradigm for managing agro-food production, by monitoring the physical parameters of different farming zones. This might be pursued by means of different communication infrastructures, but the most promising technology seems to be the wireless sensor network (WSN). However, merely applying this approach to the PA context, while resulting in a more flexible communication platform, still exhibits a rigid information management architecture. This limitation can be overcome by applying the ambient intelligent (AmI) paradigm to create an environment highly interactive with all the users involved in the process. Our proposal deals with the porting of the AmI concepts on a highly-integrated WSN platform with a special focus on the routing strategies. In particular, we investigate a class of dynamic flooding algorithm which is aware of the status of neighbor nodes in terms of geographical position and residual battery charge. By comparing this approach with the gossiping and the static flooding schemes, we are able to highlight a remarkable improvement in the network life-time with a particular regard to the most solicited nodes, without increasing complexity.