Models, Statistics, and Rates of Binary Correlated Sources

This paper discusses and analyzes various models of binary correlated sources, which may be relevant in several distributed communication scenarios. These models are statistically characterized in terms of joint Probability Mass Function (PMF) and covariance. Closed-form expressions for the joint entropy of the sources are also presented. The asymptotic entropy rate for very large number of sources is shown to converge to a common limit for all the considered models. This fact generalizes recent results on the information-theoretic performance limit of communication schemes which exploit the correlation among sources at the receiver.Comment: submitted for publicatio

Efficient Distributed Detection for Wireless Sensor Networks

Negli ultimi anni si è assistito ad una crescita esponenziale delle tecnologie per la fabbricazione di micro dispositivi ed, in particolare, di sensori. Il costo di tali sensori si è ridotto, portando ad un crescente interesse in reti di sensori, ad esempio, per il monitoraggio ambientale. D'altro canto, l'utilizzo di reti di sensori nel campo militare ha una lunga storia. In tutti i casi, l'obiettivo di una rete di sensori è quello di identificare lo stato di un fenomeno di interesse attraverso l'azione collaborativo di più sensori. Un esempio di tale azione è la rivelazione distribuita. In questa tesi, viene studiato come incorporare le caratteristiche intrisìnseche del fenomeno sotto osservazione nella progettazione di algoritmi di rivelazione distribuita in reti di sensori.Recent years have witnessed an exponential growth of micro device manufacturing techniques and, in particular, of powerful sensor devices. The costs of these sensors have dropped, leading to an increasing interest on sensor networks for civilian applications, e.g., environmental monitoring. The use of sensor networks in the military field has, on the other hand, a long history. In all cases, the goal of a sensor network is to identify the status of a phenomenon of interest through a collaborative action of the sensors. An instance of this collaborative action is given by distributed detection. The increasing interest for sensor networks has, therefore, spurred a significant activity on the design of efficient distributed detection techniques. In this thesis, we investigate how the structural properties of the physical phenomenon under observation can be taken into account in designing distributed detection algorithms for sensor networks

Low-complexity in-sensor audio detection with experimental validation

none3noIn this paper, we present a low-complexity detection algorithm for the recognition of different audio signal patterns. The proposed detection algorithm evolves through two main processing phases: (a) coarse and (b) fine. The evolution between these two phases is described through a finite state machine (FSM) model. The use of different processing phases is expedient to reduce the computational complexity, thus making our algorithm suitable for wireless sensor networking scenarios, where the in-sensor energy consumption needs to be kept as low as possible. In fact, fine processing (in the frequency domain) is carried out only when an “atypical” audio signal is detected. On the other hand, coarse processing (in the time domain), performed a larger number of times, has a much lower complexity. The proposed approach is validated through audio signals experimentally acquired with a commercial microphone, embedded in a wireless sensor node. The obtained results show that our processing technique allows to detect efficiently the presence of signals of interest (identified by properly selected spectral signatures) and to reliably distinguish different audio signal patterns, e.g., speech and non-speech audio signals.M. Martalò; G. Ferrari; C. MalavendaMartalo', Marco; G., Ferrari; C., Malavend

A multi-dimensional characterization of clustered Zigbee networks: performance trade-offs

In this paper, we characterize Zigbee networks composed by a set of source nodes which transmit to an access point (AP) either directly or through intermediate relay nodes. In the latter case, both uniform and non-uniform clustering configurations are considered. We evaluate the delay (between transmission and reception of data packets) as a function of the network transmission rate (relative to successful packet transmissions) and tolerable network death level (which will be properly defined). Our results show the existence of a characteristic multidimensional performance surface describing the behavior of a Zigbee network in terms of the three mentioned performance indicators. We heuristically derive a closed-form expression for the network performance surface, by interpolating it through the sum of bidimensional Gaussian surfaces

Markov chain-based performance evaluation of IEEE 802.15.4 multihop wireless sensor networks

In this paper, we propose a Markov chain-based analytical framework for modeling the behavior of the medium access control (MAC) protocol in IEEE 802.15.4 multihop wireless sensor networks. First, we present an analytical framework for 1-hop networking scenarios, i.e., scenarios where all the sensor nodes communicate directly to the network coordinator. Then, we extend our framework to 2-hop networking scenarios, i.e., scenarios where sensor nodes communicate to the coordinator through an intermediate relay node which forwards the packets received from the sources (i.e., the sensors) toward the final destination (i.e., the coordinator). No acknowledgment messages are transmitted to confirm successful data packet deliveries, and communications are beaconed (i.e., they make use of synchronization packets denoted as "beacons"). The aggregate network throughput and the packet delivery delay are evaluated. Our results show a good agreement between the proposed analytical model and realistic ns-2 simulation results

Clustered decentralized binary detection: an information-theoretic approach

This paper presents an information-theoretic approach to decentralized binary detection in sensor networks. In particular, we consider a Bayesian approach for the minimization of the probability of decision error. Two scenarios are considered: (i) a scenario where clusters are identical (uniform clustering) and (ii) a scenario where clusters are different (non-uniform clustering). The performance analysis obtained with a classical “communication-theoretic” approach is extended to the “information-theoretic” realm using the concept of mutual information. We then propose a simplified binary symmetric channel (BSC) model to analyze the clustered schemes, and we show that it allows to accurately predict their realistic performance. Our results show that uniform clustering leads to negligible performance degradation beyond the first clustering subdivision. Moreover, we show that for a given value of the system mutual information, the probability of decision error is uniquely determined. The results predicted by the analytical framework are confirmed by simulations

Markov chain-based optimization of multihop IEEE 802.15.4 wireless sensor networks

In this work, we propose an optimization framework for the IEEE 802.15.4 medium access control (MAC) protocol. More precisely, we derive a theoretical tool providing reliable guidelines for tuning the parameters of the MAC protocol. The presented tool could be used in two different directions: (i) for fixed network topology, it might be of interest to determine the MAC protocol configuration able to guarantee the best performance according to some quality of service metric; (ii) for fixed parameters of the MAC protocol, it might be of interest to determine the optimal network topology. Both these situations appear in practical situations. In particular, the first scenario happens in all cases where one cannot decide a-priori the node positions, because of some randomness or for some physical constraints, so that the only degree of freedom is given by the MAC protocol itself. The second set of scenarios occurs in the circumstances where one can decide the nodes displacement, thus introducing more degrees of freedom, but making also the problem solution more complicated. The proposed optimization tool applies some classical operative research instruments to a recently proposed Markov-Chain based model that has shown to be suitable for the performance analysis of a generic Cluster-Tree (CT) multihop IEEE 802.15.4 network. We will also show that this tool could be effectively used in a real scenario with a low-cost low-energy hardware platform