Francesco Zorzi

Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks

In this paper, we investigate the actual performance of some of the best known localization algorithms when deployed in real-world indoor environments. Among the plethora of possible localization schemes, we focus on those based on radio signal strength measurements only, since they do not require extra circuitry that would result in higher cost and energy consumption. For a fair comparison, we have first gathered thousands of radio signal strength measurements in two different indoor environments. To estimate the channel model parameters and to compare the different localization algorithms these data have been used.

Range-only SLAM with a mobile robot and a Wireless Sensor Networks

This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of aWireless 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 trasmission 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

On the effects of node density and duty cycle on energy efficiency in underwater networks

Energy-efficiency in underwater networks is a key issue that affects all aspects of network design, from hardware to protocols and applications. In this paper we analyze the impact of node density on the energy consumption in transmission, reception and idle-listening, in a network where nodes follow a duty cycle scheme. We consider the energy performance of the network for different scenarios, where a different number of nodes and different values of the duty cycle are taken into account. We simulate different power settings, showing that there exists an effective network density for which the energy consumption is minimized.

Opportunistic Localization: Modeling and Analysis

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.

Opportunistic localization scheme based on Linear Matrix Inequality

Enabling self-localization of mobile nodes is an important problem that has been widely studied in the literature. The general conclusions is that an accurate localization requires either sophisticated hardware (GPS, UWB, ultrasounds transceiver) or a dedicated infrastructure (GSM, WLAN). In this paper we tackle the problem from a different and rather new perspective: we investigate how localization performance can be improved by means of a cooperative and opportunistic data exchange among the nodes. We consider a target node, completely unaware of its own position, and a number of mobile nodes with some self-localization capabilities. When the opportunity occurs, the target node can exchange data with in-range mobile nodes. This opportunistic data exchange is then used by the target node to refine its position estimate by using a technique based on Linear Matrix Inequalities and barycentric algorithm. To investigate the performance of such an opportunistic localization algorithm, we define a simple mathematical model that describes the opportunistic interactions and, then, we run several computer simulations for analyzing the effect of the nodes duty-cycle and of the native self-localization error modeling considered. The results show that the opportunistic interactions can actually improve the self-localization accuracy of a strayed node in many different scenarios.

Experimental localization application in opportunistic scenario

Opportunistic localization is a new approach to the self-localization problem that is recognized as one of the most critical for mobile users, in particular in indoor environments. The basic idea consists in allowing mobile users to exchange location information when they happen to be in radio range and to exploit this information in order to improve the self-localization accuracy of mobile users. This demo is aimed at proving the effectiveness of the opportunistic approach and identifying possible drawbacks and technical issues. To this end, we will realize a simple though realistic network deployment, where a mobile user, which performs a very basic min-max self localization procedure, tries to improve the accuracy of its location by communicating on an opportunistic basis with other nodes in spatial proximity. The demo will allow us to appreciate the actual benefit that the opportunistic paradigm brings to the self-localiztion problem and to compare the performance of different opportunistic localization algorithms.

Efficient Packet Converge-Casting: Relieving the Sink Congestion in Wireless Sensor Networks

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.

A distributed solution to estimation problems in wireless sensor networks leveraging broadcast communication

The wide variety of applications for wireless sensor networks combined with the energy constrained nature of sensor nodes has motivated research on algorithms for in-network estimation of measured physical quantities to reduce the amount of data that needs to be transmitted to the sink. The main contribution of this work is a distributed estimation solution that leverages the broadcast nature of the wireless channel in sensor networks.We present a data dissemination protocol, called Kalsip, which is designed to support the implementation of our distributed estimation algorithm based on Kalman filters. We show, through analysis and simulation, that our protocol provides accurate estimation of physical properties while minimizing the number of transmissions needed and requiring nodes to only overhear estimates broadcast by their neighbors.