Ugo Maria Colesanti

On the accuracy of omnet++ in the wireless sensornetworks domain: simulation vs. testbed

In this paper we present a first effort in assessing the reliability of OMNeT++ and the MAC Simulator framework insimulating Wireless Sensor Networks. A collection of metricson the flooding algorithm running on a simple testbed made of few Tmote Sky is used as reference to evaluate the qualityof the simulation results. Our experiments show that simulation results tend to over-estimate the metrics collected inthe testbed. A correcting factor derived from experimental evidences must be considered in order to improve the simulation results. At the best of our knowledge, this is thefirst result about the accuracy of OMNet++ in the wireless sensor network domain.

The impact of network topology on collection performance

The network topology has a significant impact on the performance of collection protocols in wireless sensor networks. In this paper, we introduce an unobtrusive methodology to quantify the impact of the topology on the performance of collection protocols. Specifically, we propose a protocol-independent metric, the Expected Network Delivery, that quantifies the delivery performance that a collection protocol can be expected to achieve given the network topology. Experimental evidence obtained with two collection protocols on numerous topologies on testbeds shows that our approach enables a systematic evaluation of protocol performance.

DISSense: An adaptive ultralow-power communication protocol for wireless sensor networks

This paper presents DISSense, an adaptive, ultralow-power communication protocol for wireless sensor networks. DISSense is specifically designed for long-term environmental monitoring applications and it provides for both data collection and data dissemination services. By automatically adapting the length of its active phases, DISSense can guarantee for both a very low duty cycle and reliable data delivery. We tested the performance of DISSense on both a testbed and on the TOSSIM simulation environment. Our experimental results show that a sensor network running DISSense can provide for average data delivery ratios above 98% and at the same time achieve a lifetime of several years. Our TinyOS 2.1 implementation of DISSense is publicly available.

Recommending items in pervasive scenarios: models and experimental analysis

In this paper, we propose and investigate the effectiveness of fully decentralized, collaborative filtering techniques. These are particularly interesting for use in pervasive systems of small devices with limited communication and computational capabilities. In particular, we assume that items are tagged with smart tags (such as passive RFIDs), storing aggregate information about the visiting patterns of users that interacted with them in the past. Users access and modify information stored in smart tags transparently, by smart reader devices that are already available on commercial mobile phones. Smart readers use private information about previous behavior of the user and aggregate information retrieved from smart tags to recommend new items that are more likely to meet user expectations. Note that we do not assume any transmission capabilities between smart tags: Information exchange among them is mediated by users’ collective and unpredictable navigation patterns. Our algorithms do not require any explicit interaction among users and can be easily and efficiently implemented. We analyze their theoretical behavior and assess their performance in practice, by simulation on both synthetic and real, publicly available data sets. We also compare the performance of our fully decentralized solutions with that of state-of-the-art centralized strategies.

MagoNode: Advantages of RF Front-ends in Wireless Sensor Networks

This chapter introduces the MagoNode: a new low-power wireless device for Wireless Sensor Networks operating in the ISM 2.4 Ghz band. This platform is based on a highly efficient RF front-end that greatly improves RF performance, in terms of radio range and sensibility, still limiting energy consumption. Indeed, the device outperforms other existing amplified platforms available on the market and is comparable to most commonly known unamplified motes. Moreover, the MagoNode is tailored to operate in various countries with different wireless regulations. Our platform is also TinyOS-compatible and supports the Contiki operating system.

Structural health monitoring in an underground construction site: the roman experience

This poster presents the work done to monitor the structural health of a Rome B1 underground construction site through a battery-powered Wireless Sensor Network (WSN). We illustrate the specific requirements and challenges of working with wireless sensors underground, and we describe the solutions adopted for obtaining a working WSN that provides a robust solution of on line monitoring. We conclude by presenting an assessment and testing of these solutions, along with the design insights and the lessons we learned during this on-the-field experience.

REACTIVE: A peaceful coexistence between deluge and Low Power Listening

In this paper we analyze the interaction between two communication protocols implemented in TinyOS 2.x: Deluge T2, an over-the-air programming protocol, and the Low Power Listening (LPL) implementation provided with the standard Medium Access Control layer, BoX-MAC. We show how the characteristics of the two layers deeply diverge, leading to a sensible performance degradation. We present REACTIVE, a simple algorithm that, integrated in Deluge, is able to dynamically disable and re-enable LPL in order to boost Deluge performance, leveraging on the critical aspects of the layers integration. REACTIVE is able to increase the performance of Deluge, compared to its standard implementation, by a factor of 2.6 in terms of energy efficiency and 7 in terms of dissemination time.

A lightweight privacy preserving SMS-based recommendation system for mobile users

In this paper, we propose a fully decentralized approach for recommending new contacts in the social network of mobile phone users. With respect to existing solutions, our approach is characterized by some distinguishing features. In particular, the application we propose does not assume any centralized coordination: It transparently collects and processes user information that is accessible in any mobile phone, such as the log of calls, the list of contacts or the inbox/outbox of short messages and exchanges it with other users. This information is used to recommend new friendships to other users. Furthermore, the information needed to perform recommendation is collected and exchanged between users in a privacy preserving way. Finally, information necessary to implement the application is exchanged transparently and opportunistically, by using the residual space in standard short messages occasionally exchanged between users. As a consequence, we do not ask users to change their habits in using SMS.

Introducing the MagoNode platform

The purpose of this demo is to introduce a new low-power wireless device for Wireless Sensor Networks (WSN) operating in the ISM 2.4Ghz band: the MagoNode. Thanks to a highly efficient RF front-end, that extends the radio range and increases link reliability, the MagoNode features out-standing RF performance, still containing energy consumption. The platform has been designed at the Department of Computer, Control, and Management Engineering Antonio Ruberti of the University of Rome La Sapienza in collaboration with the spin-off WSense [1].

Adaptive random sensor selection for field reconstruction in wireless sensor networks

Wireless sensor networks (WSNs) allow for the sampling of a physical phenomenon over long periods of time and across extended geographical areas [1]. Once reported to a central collecting unit, the samples may be used to reconstruct the developing of the physical phenomenon of interest -- also referred to as signal or sensor field -- in both time and space. Work in information theory [3] shows that a reliable signal reconstruction is possible if a sufficiently large number of nodes sample the signal at sufficiently close time and space intervals. Clearly, the achievable quality of the reconstruction can be maximized by letting the highest possible number of nodes collect and report samples. However, on typical sensor nodes, sensing and communication modules require the largest amount of energy and their continuous use can rapidly deplete node batteries [1]. Limiting the number of nodes actively participating in sensing and communication is thus the most effective way to increase the lifetime of both single sensor nodes and the network as a whole [2, 5]. Sensor selection algorithms can be used to schedule individual sensing activity in order to balance the accuracy of the reconstruction with energy consumption. In real WSNs deployments, the irregular spatial distribution of the nodes typically produces nonuniform sampling geometries that and reconstruction techniques able to deal with scattered samples must thus be used. In this context, the ACT reconstruction algorithm [3, Chapter 6] is one of the most computationally efficient and robust techniques known in literature and appears as a perfect fit to perform field reconstruction in WSNs. In particular, the ACT can deal with both very irregular sampling geometries and presence of noise in the data. However, the more the sampling geometry resembles a uniform grid, the better the performance of the ACT. With these considerations in mind, we investigate sensor selection strategies able to generate, given the constraints of the physical network topology, sampling geometries providing limited number of samples but still enabling the ACT to work properly. To this scope, we resort to random sensor selection strategies [2] and propose an adaptive method to determine, in a distributed fashion, the probability of activation of single sensor nodes. Our preliminary experimental results show that our approach succeeds in making the ACT able to reconstruct the sensor field with good accuracy, thereby using a lower number of sensors with respect to other random sensor selection strategies.