Loreto Pescosolido

Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels

In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights (possibly) dependent on the radio channels, and we pose special attention to the effect of the propagation delay occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is reached with exponential convergence speed for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We provide a closed form expression for the global consensus, showing that the effect of delays is, in general, the introduction of a bias in the final decision. Finally, we exploit our closed form expression to devise a double-step consensus mechanism able to provide an unbiased estimate with minimum extra complexity, without the need to know or estimate the channel parameters.

Cooperative wireless networks based on distributed space-time coding

The aim of this paper is to show how cooperation among nodes of a wireless network can be useful to reduce the overall radiated power necessary to guarantee reliable links among the network nodes. The basic idea is that if the links between cooperating nodes are sufficiently reliable, the cooperating nodes can transmit in a coordinated manner in order to emulate a virtual MIMO system that can yield considerable gains in terms of diversity or capacity. In this paper, we provide first a theoretical analysis of a single-user scenario showing how the cooperation gain is related to the spatial density of the cooperating nodes. Then, we compare alternative distributed space-time coding strategies aimed at achieving the promised advantages in a multi-user context.

Detecting Low-Power Primary Signals via Distributed Sensing to Support Opportunistic Spectrum Access

Cognitive radio operation with opportunistic spectrum access has been proposed to utilize spectrum holes left unused by a primary system owning the spectrum license. The key of cognitive radio operation is the ability to detect weak primary signals and to control the transmission of cognitive users in a way that interference between the two systems is minimized. In this paper we evaluate how a sensor network deployed to provide distributed spectrum sensing can assist cognitive operation. Specifically, we consider sensor networks with regular topology, where a high level of cooperation also means that sensors far from the source of the primary signal are involved in the sensing process. Assuming energy detection and hard-decision combining we derive worst case probabilities of missed detection and false alarm, determine the necessary level of cooperation among the sensors and evaluate how the sensor density and the sensing time affect the performance of distributed sensing.

Average consensus algorithms robust against channel noise

Average consensus algorithms have attracted popularity in the wireless sensor network scenario as a simple way to compute linear combinations of the observations gathered by the sensors, in a totally decentralized fashion, i.e., without a fusion center. However, average consensus techniques involve the iterated exchange of data among sensors. In a practical implementation, this interaction is affected by noise. The goal of this paper is to bring some common adaptive signal processing techniques into the sensor network context in order to robustify the iterative exchange of data against communication noise. In particular, we will compare the performance of two algorithms: (a) a method, reminiscent of stochastic approximation algorithms, using a decreasing step size, with proper decaying law, and (b) a leakage method imposing that the consensus cannot be too distant from the initial measurements. We provide a theoretical analysis, validated by simulation results, of both methods to show how to derive the best tradeoff between the system parameters in order to get the minimum estimation variance, taking into account both observation and interaction noise.

Radar sensor networks with distributed detection capabilities

The goal of this work is to propose a distributed detection method for a network of radars interchanging their measurements through pulse-position modulation. The proposed approach does not need the presence of a fusion center and it is then fully decentralized. The approach is robust against node failures, as the only requirement to get the network gain is that the whole network remains connected, i.e., for every pair of nodes, there is a path, possibly composed of multiple hops, joining them. The decentralized approach is based on a distributed consensus mechanism. However, when conventional consensus algorithms are implemented over realistic channels, the receiver noise gives rise to an error on the final decision statistic that linearly increases with time. We avoid this inconvenient by properly modifying the consensus algorithm to make it suitable for communications over noisy channels. We analyze the performance of the proposed system considering the presence of both observation and communication noise present in the interaction among the radars. In particular, we show that even with a non-coherent integration, the whole system is able to achieve a sensitivity gain equal to the number of radars.

Distributed decision through self-synchronizing sensor networks in the presence of propagation delays and nonreciprocal channels

In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights dependent on the radio interface and we pose special attention to the effect of the propagation delays occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is achieved for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We also provide a closed form expression for the global consensus, showing that the effect of delays is, in general, to introduce a bias in the final decision. The closed form expression is also useful to modify the consensus mechanism in order to get rid of the bias with minimum extra complexity.

Throughput-Optimal Cross-Layer Design for Cognitive Radio Ad Hoc Networks

We present a distributed, integrated medium access control, scheduling, routing and congestion/rate control protocol stack for cognitive radio ad hoc networks (CRAHNs) that dynamically exploits the available spectrum resources left unused by primary licensed users, maximizing the throughput of a set of multi-hop flows between peer nodes. Using a network utility maximization (NUM) formulation, we devise a distributed solution consisting of a set of sub-algorithms for the different layers of the protocol stack (MAC, flow scheduling and routing), which result from a natural decomposition of the problem into sub-problems. Specifically, we show that: 1) The NUM optimization problem can be solved via duality theory in a distributed way, and 2) the resulting algorithms can be regarded as the CRAHN protocols. These protocols combine back-pressure scheduling with a CSMA-based random access with exponential backoffs. Our theoretical findings are exploited to provide a practical implementation of our algorithms using a common control channel for node coordination and a wireless spectrum sensor network for spectrum sensing. We evaluate our solutions through ns-2 MIRACLE-based simulations. Our results show that the proposed protocol stack effectively enables multiple flows among cognitive radio nodes to coexist with primary communications. The CRAHN achieves high utilization of the spectrum left unused by the licensed users, while the impact on their communications is limited to an increase of their packet error rate that is below 1 percent.

Optimal Decentralized Estimation Through Self-Synchronizing Networks in the Presence of Propagation Delays

In this paper we focus on a sensor network scheme whose nodes are locally coupled oscillators that evolve in time according to a differential equation, whose parameters depend on the local estimate. The proposed system is capable, by self-synchronization, to reach the network consensus that coincides with the globally optimum maximum likelihood estimate, even though each sensor is only locally coupled with nearby nodes. Our main contribution is to study the effect of propagation delay on both the synchronization capability of the system and the final estimate. We provide delay-independent conditions for the proposed system to synchronize, and we derive closed-form expression of the synchronized state. Interestingly, the effect of propagation delays is simply to introduce a bias on the final estimate, that depends on the network topology and on the values of the delays. The analysis of this bias, suggest us how to design the coupling mechanism in order to alleviate it or even remove it

Optimal radio access in femtocell networks based on markov modeling of interferers' activity

One of the most critical issues in femtocell network deployment is interference management, especially for femtocells sharing the spectrum occupied by conventional cellular networks. In this work we propose and analyze an optimal power allocation strategy based on modeling the interferers activity as a two-state Markov chain. In the single femto-user access, we show how to maximize the expected value of femto-user rate, averaged over the interference statistical model. Then, we extend the approach to the multiuser case, adopting a game-theoretic formulation to devise decentralized access strategies, particularly suitable in view of potential massive deployment of femto access points.

Decentralized Detection and Localization Through Sensor Networks Designed As a Population of Self-Synchronizing Oscillators

The detection and localization of an event through a sensor network is a topic that has attracted considerable attention recently because of many potential applications. Typically, these decisions are taken by conveying the sensor measurements to a sink node that processes the data and provides an estimate. However, the presence of a sink node creates a bottleneck that is the cause of potential congestions and it poses problems of scalability. In this work, we propose a decentralized decision scheme that is capable to achieve optimal decisions without requiring a fusion center. The network is composed of a set of mutually coupled oscillators, where each node is coupled only to the nearest nodes. We show how to achieve optimal detection for both deterministic and random signals by properly selecting the parameters of the coupling mechanism. Furthermore, if the nodes know their own positions and the network is connected, we show how to make each node able to perform a totally distributed energy-based source localization