Efficient Location Training Protocols for Localization in Heterogeneous Sensor and Actor Networks

International audienceAbstract--In this work we consider a large-scale geographic area populated by tiny sensors and some more powerful devices called actors, authorized to organize the sensors in their vicinity into short-lived, actor-centric sensor networks. The tiny sensors run on miniature non-rechargeable batteries, are anonymous and are unaware of their location. The sensors differ in their ability to dynamically alter their sleep times. Indeed, the periodic sensors have sleep periods of predefined lengths, established at fabrication time; by contrast, the free sensors can dynamically alter their sleep periods, under program control. The main contribution of this work is to propose an energy-efficient location training protocol for heterogeneous actor-centric sensor networks where the sensors acquire coarse-grain location awareness with respect to the actor in their vicinity. Our analytical analysis, confirmed by experimental evaluation, show that the proposed protocol outperforms the best previously-known location training protocols in terms of the number of sleep/awake transitions, overall sensor awake time, and energy consumption

Efficient Binary scheme for Training Heterogeneous Sensor Actor Networks

International audienceSensor networks are expected to evolve into long-lived, autonomous networked systems whose main mission is to provide in-situ users – called actors – with real-time information in support of specific goals supportive of their mission. The network is populated with a heterogeneous set of tiny sensors. The free sensors alternate between sleep and awake periods, under program control in response to computational and communication needs. The periodic sensors alternate between sleep periods and awake periods of predefined lengths, established at the fabrication time. The architectural model of an actor-centric network used in this work comprises in addition to the tiny sensors a set of mobile actors that organize and manage the sensors in their vicinity. We take the view that the sensors deployed are anonymous and unaware of their geographic location. Importantly, the sensors are not, a priori, organized into a network. It is, indeed, the interaction between the actors and the sensor population that organizes the sensors in a disk around each actor into a short-lived, mission-specific, network that exists for the purpose of serving the actor and that will be disbanded when the interaction terminates. The task of setting up this form of actor-centric network involves a training stage where the sensors acquire dynamic coordinates relative to the actor in their vicinity. The main contribution of this work is to propose an energy- efficient training protocol for actor-centric heterogeneous sensor networks. Our protocol outperforms all know training protocols in the number of sleep/awake transitions per sensor needed by the training process. Specifically, in the presence of $k$ coronas, no sensor will experience more than$\lceil log(k)\rceil$ sleep/awake transitions and awake periods

Access Control for Data Integration in Presence of Data Dependencies

International audienceDefining access control policies in a data integration scenario is a challenging task. In such a scenario typically each source specifies its local access control policy and cannot anticipate data inferences that can arise when data is integrated at the mediator level. Inferences, e.g., using functional dependencies, can allow malicious users to obtain, at the mediator level, prohibited information by linking multiple queries and thus violating the local policies. In this paper, we propose a framework, i.e., a methodology and a set of algorithms, to prevent such violations. First, we use a graph-based approach to identify sets of queries, called violating transactions, and then we propose an approach to forbid the execution of those transactions by identifying additional access control rules that should be added to the mediator. We also state the complexity of the algorithms and discuss a set of experiments we conducted by using both real and synthetic datasets. Tests also confirm the complexity and upper bounds in worst-case scenarios of the proposed algorithms

Algoritmi Paralleli - Sincroni, Concorrenti, Distribuiti

Il presente testo e` dedicato alla progettazione ed all'analisi della complessita` di algoritmi paralleli, dove e` ammessa l'esecuzione contemporanea di piu` istruzioni dello stesso algoritmo da parte di piu` esecutori distinti. Sono trattati i principali modelli di parallelismo, che dipendono dal fatto che ci sia o no sincronismo tra le istruzioni eseguite contemporaneamente e che la memoria sia o no condivisa tra tutti gli esecutori. Sono considerati algoritmi per modelli PRAM (sincroni con memoria condivisa), reti a grado limitato (sincroni senza memoria condivisa), concorrenti (asincroni con memoria condivisa), e distribuiti (asincroni senza memoria condivisa). Tra i modelli sincroni senza memoria condivisa, sono trattati anche il modello VLSI e quello delle mesh riconfigurabili, mentre tra quelli distribuiti sono considerate anche le reti non cablate ("wireless'') di calcolatori. Per rendere il testo autosufficiente, sono riassunte brevemente in un capitolo introduttivo le necessarie nozioni di base, riguardanti sia la progettazione e l'analisi degli usuali algoritmi sequenziali sia nozioni elementari di matematica discreta. Nel testo sono proposti circa 80 esercizi d'esame, dei quali circa 60 sono completamente svolti