Energy efficient decision fusion for differential space-time block codes in wireless sensor networks

147-156The non-coherent techniques that do not require the channel state information have gained significant interest especially when multiple transmitter and receiver nodes are involved in communication. In this paper, we analyze the energy efficiency of differential and coherent cooperative Multiple-input Multiple-output (MIMO) method using space-time block codes (STBC). We exploit the benefits of the extension of the observation interval of differential STBC to three blocks in Wireless sensor networks (WSNs). We propose an energy efficient decision fusion (EEDF) algorithm in WSNs which utilizes the benefits of Multiple symbol differential detection (MSDD) decision fusion by optimally selecting the ring amplitude of the differential amplitude phase shift keying (DAPSK) constellation. The simulation results show that processing differential multiple symbols provides significant energy saving compared to the conventional two-symbol processing. Furthermore, significant performance gain is achieved for the proposed algorithm compared to 16 DPSK MSDD decision fusions

Energy efficient decision fusion for differential space-time block codes in wireless sensor networks

The non-coherent techniques that do not require the channel state information have gained significant interest especially when multiple transmitter and receiver nodes are involved in communication. In this paper, we analyze the energy efficiency of differential and coherent cooperative Multiple-input Multiple-output (MIMO) method using space-time block codes (STBC). We exploit the benefits of the extension of the observation interval of differential STBC to three blocks in Wireless sensor networks (WSNs). We propose an energy efficient decision fusion (EEDF) algorithm in WSNs which utilizes the benefits of Multiple symbol differential detection (MSDD) decision fusion by optimally selecting the ring amplitude of the differential amplitude phase shift keying (DAPSK) constellation. The simulation results show that processing differential multiple symbols provides significant energy saving compared to the conventional two-symbol processing. Furthermore, significant performance gain is achieved for the proposed algorithm compared to 16 DPSK MSDD decision fusions

Cognitive Radio Connectivity for Railway Transportation Networks

Reliable wireless networks for high speed trains require a significant amount of data communications for enabling safety features such as train collision avoidance and railway management. Cognitive radio integrates heterogeneous wireless networks that will be deployed in order to achieve intelligent communications in future railway systems. One of the primary technical challenges in achieving reliable communications for railways is the handling of high mobility environments involving trains, which includes significant Doppler shifts in the transmission as well as severe fading scenarios that makes it difficult to estimate wireless spectrum utilization. This thesis has two primary contributions: (1) The creation of a Heterogeneous Cooperative Spectrum Sensing (CSS) prototype system, and (2) the derivation of a Long Term Evolution for Railways (LTE-R) system performance analysis. The Heterogeneous CSS prototype system was implemented using Software-Defined Radios (SDRs) possessing different radio configurations. Both soft and hard-data fusion schemes were used in order to compare the signal source detection performance in real-time fading scenarios. For future smart railways, one proposed solution for enabling greater connectivity is to access underutilized spectrum as a secondary user via the dynamic spectrum access (DSA) paradigm. Since it will be challenging to obtain an accurate estimate of incumbent users via a single-sensor system within a real-world fading environment, the proposed cooperative spectrum sensing approach is employed instead since it can mitigate the effects of multipath and shadowing by utilizing the spatial and temporal diversity of a multiple radio network. Regarding the LTE-R contribution of this thesis, the performance analysis of high speed trains (HSTs) in tunnel environments would provide valuable insights with respect to the smart railway systems operating in high mobility scenarios in drastically impaired channels

A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry

The demand for autonomous vehicles is increasing gradually owing to their enormous potential benefits. However, several challenges, such as vehicle localization, are involved in the development of autonomous vehicles. A simple and secure algorithm for vehicle positioning is proposed herein without massively modifying the existing transportation infrastructure. For vehicle localization, vehicles on the road are classified into two categories: host vehicles (HVs) are the ones used to estimate other vehicles' positions and forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV transmits modulated data from the tail (or back) light, and the camera of the HV receives that signal using optical camera communication (OCC). In addition, the streetlight (SL) data are considered to ensure the position accuracy of the HV. Determining the HV position minimizes the relative position variation between the HV and FV. Using photogrammetry, the distance between FV or SL and the camera of the HV is calculated by measuring the occupied image area on the image sensor. Comparing the change in distance between HV and SLs with the change in distance between HV and FV, the positions of FVs are determined. The performance of the proposed technique is analyzed, and the results indicate a significant improvement in performance. The experimental distance measurement validated the feasibility of the proposed scheme

Decentralized Maximum Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems

In this paper we propose a decentralized sensor network scheme capable to reach a globally optimum maximum likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network and we validate our theoretical findings with simulation results.Comment: Journal paper accepted on IEEE Transactions on Signal Processin

Compressive Privacy for a Linear Dynamical System

We consider a linear dynamical system in which the state vector consists of both public and private states. One or more sensors make measurements of the state vector and sends information to a fusion center, which performs the final state estimation. To achieve an optimal tradeoff between the utility of estimating the public states and protection of the private states, the measurements at each time step are linearly compressed into a lower dimensional space. Under the centralized setting where all measurements are collected by a single sensor, we propose an optimization problem and an algorithm to find the best compression matrix. Under the decentralized setting where measurements are made separately at multiple sensors, each sensor optimizes its own local compression matrix. We propose methods to separate the overall optimization problem into multiple sub-problems that can be solved locally at each sensor. We consider the cases where there is no message exchange between the sensors; and where each sensor takes turns to transmit messages to the other sensors. Simulations and empirical experiments demonstrate the efficiency of our proposed approach in allowing the fusion center to estimate the public states with good accuracy while preventing it from estimating the private states accurately

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201