Coverage in wireless sensor networks: models and algorithms

2013 - 2014Due to technological advances which enabled their deployment in relevant and diverse scenarios, Wireless Sensor Networks (WSNs) have been object of intense study in the last few years. Possible application contexts include environmental monitoring, tra c control, patient monitoring in healthcare and intrusion detection, among others (see, for example, [1], [2], [3]). The general structure of a WSN is composed of several hardware devices (sensors) deployed over a given region of interest. Each sensor can collect information or measure physical quantities for a subregion of the space around it (its sensing area), and more in particular for speci c points of interest (target points or simply targets) within this area. The targets located in the sensing area of a given sensor s are covered by s. Individual sensors are usually powered by batteries which make it possible to keep them functional for a limited time interval, with obvious constraints related to cost and weight factors. Using a network of such devices in a dynamic and coordinated fashion makes it possible to overcome the limitations in terms of range extension and battery duration which characterize each individual sensor, enabling elaborate monitoring of large regions of interest. Extending the amount of time over which such monitoring activity can be carried out represents a very relevant issue. This problem, generally known as Maximum Lifetime Problem (MLP), has been widely approached in the literature by proposing methods to determine: i) several subsets of sensors each one able to provide coverage for the target points and ii) the activation time of these subsets so that the battery constraints are satis ed. It should be noted that while sensors could be considered as belonging to di erent states during their usage in the intended application (such as receiving, transmitting, or idle) in this context two essential states can be identi ed. That is, each sensor may currently be active (i.e. used in the current cover, and consuming its battery) or not. Activating a cover refers therefore to switching all its sensors to the active state, while switching o all the other ones. This research thesis shows a detailed overview about the wireless sensor networks, about their applications but mainly about typical coverage issues in this eld. In particular, this work focuses on the issue of maximizing the amount of time over which a set of points of interest (target points), located in a given area, can be monitored by means of such wireless sensor networks. More in detail, in this research work we addressed the maximum lifetime problem on wireless sensor networks considering the classical problem in which all targets have to be covered (classical MLP) and a problem variant in which a portion of them can be neglected at all times ( -MLP) in order to increase the overall network lifetime. We propose an Column Generation approach embedding an e cient genetic algorithm aimed at producing new covers. The obtained algorithm is shown to be very e ective and e cient outperforming the previous algorithms proposed in the literature for the same problems. In this research work we also introduce two variants of MLP problem with heterogeneous sensors. Indeed, wireless sensor networks can be composed of several di erent types of sensor devices, which are able to monitor di erent aspects of the region of interest including temperature, light, chemical contaminants, among others. Given such sensor heterogeneity, di erent sensor types can be organized to work in a coordinated fashion in many relevant application contexts. Therefore in this work, we faced the problem of maximizing the amount of time during which such a network can remain operational while assuring globally a minimum coverage for all the di erent sensor types. We considered also some global regularity conditions in order to assure that each type of sensor provides an adequate coverage to each target. For both these problem variants we developed another hybrid approach, which is again based on a column generation algorithm whose subproblem is either solved heuristically by means of an appropriate genetic algorithm or optimally by means of ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to meaningfully speed up the global procedure, enabling the resolution of large-scale instances within reasonable computational times. To the best of our knowledge, these two problem variants has not been previously studied in the literature. [edited by author

WI-FI APPLICATIONS IN SEISMIC AND GEODETIC MONITORING SYSTEM

e Istituto Nazionale di Geo sica e Vulcanologia (INGV) uses di erent transmission systems for the seismic and GPS data from remote sites. ose di erent types of transmission makes the Italian seismic monitoring reliable and redundant

RING and ReCal GPS networks: two Italian geodetic infrastructures and their data management, sharing and dissemination

Geographic data sharing and collection are becoming key activities among geological and geophysical studies worldwide, and the recent increase of infrastructures is demanding to scientific and civil community an effort to manage and disseminate their products as efficiently as possible. With this effort in mind, INGV began some years ago to collaborate with civilian and commercial subjects in order to promote the integration and sharing of data from GNSS (Global Navigation Satellite System) networks existing in Italy. Since 2004, INGV deployed a permanent, integrated and real-time monitoring CGPS network (RING, Rete Integrata Nazionale GPS, http://ring.gm.ingv.it), which is now constituted by about 170 stations all over Italy (Selvaggi et al., 2006; Avallone et al, 2010). All stations have high quality GPS monuments (D’Ambrosio, 2007; Minichiello et al., 2010) and most of them are co-located with broadband or very broadband seismometers and strong motion sensors. This scientific network is aimed to monitor crustal deformation in Italy in order to study earthquake deformation processes, from interseismic strain accumulation to rupture processes, and is giving an effective contribute to Italian Civil Protection for seismic hazard monitoring. Moreover, in the last years, local Authorities, nation-wide industries and other scientific institutions started to establish GPS/GNSS networks all over the Italian territory mainly for cartographic and positioning purposes. More than 500 CGPS stations are actually operating in Italy. The INGV acquire and analyze most of these networks, promoting at the same time actions to integrate the RING with the ones managed by regional and national data providers (D’Anastasio et al., 2010). The Regione Calabria in 2009 planned and established a network of 17 CGPS stations for cartographic and civil protection purposes covering the Calabria region (hereafter ReCal network). The CGPS stations are good quality monument connected in real time and, in the next future, will start to furnish to the civil community a positioning service. In order to share the RING and ReCal data and relative products, a synergy between the CNT-INGV (Centro Nazionale Terremoti) and the Regione Calabria started in 2011. An official agreement between the two institutions state the sharing of CGPS data, the collaboration between CNT-INGV and Regione Calabria to test the efficiency and the positioning service of ReCal network, and the contribution of ReCal network to scientific monitoring of Calabria, one of the most seismically active region in Italy. Moreover, this agreement included also the commissioning of the ReCal network and of its positioning services performed by CNT-INGV. Figure 1 shows the GPS and GNSS stations currently operating in Italy. In the inset it could be noticed how the RING and ReCal networks are integrated in order to have the best spatial coverage of the Calabrian territory. We will present the first results of the agreement between INGV-CNT and Regione Calabria, and of the commissioning of ReCal network. Moreover, we will focus on the infrastructure already existing and developed by CNT-INGV to manage data acquisition, storage, distribution and access (Cecere, 2007; Cardinale et al., 2010; Falco, 2006; 2008; Memmolo et al., 2010; Pignone et al., 2009). INGV developed dedicated facilities including new softwares for data acquisition and a web-based collaborative environment for management of data and metadata. These facilities are used to manage data coming from the RING as well as from agreements with ReCal and other CGPS networks in Italy. We believe that this infrastructure represents an important reality in the framework of GNSS data sharing development in Italy

Emergenza sismica nel centro Italia 2016-2017. Secondo rapporto del gruppo operativo SISMIKO. Sviluppo e mantenimento della rete sismica mobile a seguito del terremoto di Amatrice Mw 6.0 (24 agosto 2016, Italia centrale)

La rete sismica temporanea installata dal gruppo operativo INGV SISMIKO a seguito del terremoto del 24 agosto 2016 tra i Monti della Laga e la Valnerina, è stata ampliata nel settore settentrionale a seguito dei forti terremoti avvenuti alla fine del mese di ottobre 2016. Successivamente alle due scosse di Mw 5.4 e 5.9 che il 26 ottobre hanno interessato l’area al confine Marche-Umbria tra i Comuni di Castelsantangelo sul Nera (MC), Norcia (PG) e Arquata del Tronto (AP), la geometria della rete è stata estesa di circa 25 km verso nord con l’attivazione di ulteriori tre stazioni temporanee di cui una, da subito, disposta per la trasmissione dei dati in tempo reale e per l’inserimento nel sistema di sorveglianza sismica dell’Istituto Nazionale di Geofisica e Vulcanologia (INGV). Un’ultima stazione è stata inoltre installata nei pressi di Campello del Clitunno in provincia di Perugia ad ovest della sequenza, a seguito del terremoto Mw 6.5 che la mattina del 30 ottobre ha interessato l’intera area già fortemente provata dalla sequenza in corso; questo è stato il più forte terremoto registrato negli ultimi 30 in Italia. A circa 5 mesi dall’inizio dell’emergenza sismica, la rete temporanea conta quindi 23 stazioni che da metà dicembre sono tutte trasmesse in tempo reale ai diversi centri di acquisizione INGV, ovvero Milano, Ancona e Grottaminarda ma soprattutto Roma dove i dati vengono contestualmente archiviati nell’European Integrated Data Archive (EIDA) e integrati nel sistema di monitoraggio e sorveglianza sismica dell’INGV; per la sorveglianza sono incluse solo parte delle stazioni. Nelle ultime settimane, le attività di campagna del gruppo operativo SISMIKO sono state costantemente focalizzate alla cura e alla manutenzione della strumentazione per garantire la continuità della trasmissione e dell’acquisizione dei dati, a volte compromesse da malfunzionamenti legati al maltempo. Alla data di aggiornamento del presente report, non è ancora stata decretata una dismissione o una rimodulazione della geometria della rete sismica temporanea, anche in considerazione della attività sismica in corso a tutt’oggi molto sostenuta. Tutti i dati acquisiti dalle stazioni temporanee SISMIKO, sono distribuiti senza alcun vincolo, al pari dei dati della Rete Sismica Nazionale (RSN, codice di rete IV), ed utilizzati per prodotti scientifici in tempo reale (localizzazioni di sala, calcolo dei Time Domain Moment Tensor -TDMT delle ShakeMaps, ecc) e per l’aggiornamento dei database dell’INGV come l’Italian Seismological Instrumental and Parametric Database (ISIDe) con la revisione del Bollettino Sismico Italiano (BSI), dell’INGV Strong Motion Data (ISMD) e dell’ITalian ACcelerometric Archive (ITACA), dell’European-Mediterranean Regional Centroid Moment Tensors (RCMT) e nei lavori scientifici che utilizzano forme d’onda velocimetriche ed accelerometriche (ri- localizzazioni, studi della sorgente sismica ecc.).Istituto Nazionale di Geofisica e Vulcanologia (INGV)Published1SR. TERREMOTI - Servizi e ricerca per la Societ

Rapporto Preliminare Sulle Attività Svolte Nel Primo Mese Di Emergenza Dal Gruppo Operativo Sismiko A Seguito Del Terremoto Di Amatrice Mw 6.0 (24 Agosto 2016, Italia Centrale)

Sintesi delle attività svolte dal coordinamento delle reti sismiche mobili INGV in emergenza, denominato SISMIKO, nel primo mese della sequenza sismica “Amatrice” seguita al terremoto di Mw 6.0 del 24 agosto 2016 (01:36 UTC). Descrizione della rete sismica implementata e prime analisi dei dati acquisiti. Report on the activities in the first month of emergency by coordination of mobile seismic networks INGV emergency, called SISMIKO, after the Mw 6.0 Amatrice earthquake (August 24th, 2016, central italy). Description of the temporary seismic network implemented and preliminary analysis of the acquired data.INGV DPCPublished1IT. Reti di monitoraggi

SISMIKO:emergency network deployment and data sharing for the 2016 central Italy seismic sequence

At 01:36 UTC (03:36 local time) on August 24th 2016, an earthquake Mw 6.0 struck an extensive sector of the central Apennines (coordinates: latitude 42.70° N, longitude 13.23° E, 8.0 km depth). The earthquake caused about 300 casualties and severe damage to the historical buildings and economic activity in an area located near the borders of the Umbria, Lazio, Abruzzo and Marche regions. The Istituto Nazionale di Geofisica e Vulcanologia (INGV) located in few minutes the hypocenter near Accumoli, a small town in the province of Rieti. In the hours after the quake, dozens of events were recorded by the National Seismic Network (Rete Sismica Nazionale, RSN) of the INGV, many of which had a ML > 3.0. The density and coverage of the RSN in the epicentral area meant the epicenter and magnitude of the main event and subsequent shocks that followed it in the early hours of the seismic sequence were well constrained. However, in order to better constrain the localizations of the aftershock hypocenters, especially the depths, a denser seismic monitoring network was needed. Just after the mainshock, SISMIKO, the coordinating body of the emergency seismic network at INGV, was activated in order to install a temporary seismic network integrated with the existing permanent network in the epicentral area. From August the 24th to the 30th, SISMIKO deployed eighteen seismic stations, generally six components (equipped with both velocimeter and accelerometer), with thirteen of the seismic station transmitting in real-time to the INGV seismic monitoring room in Rome. The design and geometry of the temporary network was decided in consolation with other groups who were deploying seismic stations in the region, namely EMERSITO (a group studying site-effects), and the emergency Italian strong motion network (RAN) managed by the National Civil Protection Department (DPC). Further 25 BB temporary seismic stations were deployed by colleagues of the British Geological Survey (BGS) and the School of Geosciences, University of Edinburgh in collaboration with INGV. All data acquired from SISMIKO stations, are quickly available at the European Integrated Data Archive (EIDA). The data acquired by the SISMIKO stations were included in the preliminary analysis that was performed by the Bollettino Sismico Italiano (BSI), the Centro Nazionale Terremoti (CNT) staff working in Ancona, and the INGV-MI, described below

Le attività del gruppo operativo INGV "SISMIKO" durante la sequenza sismica "Amatrice 2016",

SISMIKO è un gruppo operativo dell’Istituto Nazionale di Geofisica e Vulcanologia (INGV) che coordina tutte le Reti Sismiche Mobili INGVPublishedLecce3T. Sorgente sismica4T. Sismicità dell'Italia8T. Sismologia in tempo reale1SR TERREMOTI - Sorveglianza Sismica e Allerta Tsunami2SR TERREMOTI - Gestione delle emergenze sismiche e da maremoto3SR TERREMOTI - Attività dei Centr

Verbale di Collaudo in Corso d'Opera della Rete GPS Permanente della Regione Puglia (RGRP)

CNT-INGVPublished1.9. Rete GPS nazionaleope

An exact algorithm to extend lifetime through roles allocation in sensor networks with connectivity constraints

We face the problem of scheduling optimally the activities in a wireless sensor network in order to ensure that, in each instant of time, the activated sensors can monitor all points of interest (targets) and route the collected information to a processing facility. Each sensor is allocated to a role, depending on whether it is actually used to monitor the targets, to forward information or kept idle, leading to different battery consumption ratios. We propose a column generation algorithm that embeds a highly efficient genetic metaheuristic for the subproblem. Moreover, to optimally solve the subproblem, we introduce a new formulation with fewer integer variables than a previous one proposed in the literature. Finally, we propose a stopping criterion to interrupt the optimal resolution of the subproblem as soon as a favorable solution is found. The results of our computational tests show that our algorithm consistently outperforms previous approaches in the literature, and also improves the best results known to date on some benchmark instances

A hybrid exact approach for maximizing lifetime in sensor networks with complete and partial coverage constraints

In this paper we face the problem of maximizing the amount of time over which a set of target points, located in a given geographic region, can be monitored by means of a wireless sensor network. The problem is well known in the literature as Maximum Network Lifetime Problem (MLP). In the last few years the problem and a number of variants have been tackled with success by means of different resolution approaches, including exact approaches based on column generation techniques. In this work we propose an exact approach which combines a column generation approach with a genetic algorithm aimed at solving efficiently its separation problem. The genetic algorithm is specifically aimed at the Maximum Network α-Lifetime Problem (α-MLP), a variant of MLP in which a given fraction of targets is allowed to be left uncovered at all times; however, since α-MLP is a generalization of MLP, it can be used to solve the classical problem as well. The computational results, obtained on the benchmark instances, show that our approach overcomes the algorithms, available in the literature, to solve both MLP and α-MLP