This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of a Wireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the transmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks.

Localization and tracking functionalities can benefit a number of applications. Despite the large number of algorithms and technologies that have been proposed in this context, the literature still lacks a widely accepted solution, capable of cutting a tradeoff between service quality (i.e., localization accuracy) and device/architecture cost and complexity. In this paper, we tackle the problem from a different and rather new perspective: we investigate how the localization accuracy of nodes can be ameliorated by opportunistically exchanging localization information among heterogeneous nodes that occasionally happen to be in proximity. To this end, we define a simple though accurate opportunistic meeting model and, then, we develop a mathematical framework that permits to analyze the performance of an opportunistic localization strategy based on a Maximum Likelihood argument.

In this paper, we consider a wireless sensor network (WSN) where packets are forwarded to a central collector node (the sink). As all network traffic converges to this point, congestion builds up. We deal with this problem by assuming that the nodes around the sink are allowed to use a different, more efficient protocol for advancing data, namely a method designed to yield more throughput and reduce collisions, at the expense of some coordination efforts. We carry out extensive simulations, and compare our solution (which is composed of two protocols, one for the sink and its neighbors and one for the rest of the network) to a simpler collision avoidance approach used throughout the whole network. This allows to highlight the tradeoff between coordinating and interfacing two protocols (which requires better design, but achieves higher effectiveness) and using just one protocol (simpler, but more prone to congestion and losses). We show how our two-protocol solution outperforms the simpler one.