A Message Passing Approach for Decision Fusion in Adversarial Multi-Sensor Networks

We consider a simple, yet widely studied, set-up in which a Fusion Center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corrupted decisions provided by byzantine nodes, i.e. nodes which deliberately alter the result of the local decision to induce an error at the fusion center. When independent states are considered, the optimum fusion rule over a batch of observations has already been derived, however its complexity prevents its use in conjunction with large observation windows. In this paper, we propose a near-optimal algorithm based on message passing that greatly reduces the computational burden of the optimum fusion rule. In addition, the proposed algorithm retains very good performance also in the case of dependent system states. By first focusing on the case of small observation windows, we use numerical simulations to show that the proposed scheme introduces a negligible increase of the decision error probability compared to the optimum fusion rule. We then analyse the performance of the new scheme when the FC make its decision by relying on long observation windows. We do so by considering both the case of independent and Markovian system states and show that the obtained performance are superior to those obtained with prior suboptimal schemes. As an additional result, we confirm the previous finding that, in some cases, it is preferable for the byzantine nodes to minimise the mutual information between the sequence system states and the reports submitted to the FC, rather than always flipping the local decision

On the sphere-decoding algorithm II. Generalizations, second-order statistics, and applications to communications

In Part 1, we found a closed-form expression for the expected complexity of the sphere-decoding algorithm, both for the infinite and finite lattice. We continue the discussion in this paper by generalizing the results to the complex version of the problem and using the expected complexity expressions to determine situations where sphere decoding is practically feasible. In particular, we consider applications of sphere decoding to detection in multiantenna systems. We show that, for a wide range of signal-to-noise ratios (SNRs), rates, and numbers of antennas, the expected complexity is polynomial, in fact, often roughly cubic. Since many communications systems operate at noise levels for which the expected complexity turns out to be polynomial, this suggests that maximum-likelihood decoding, which was hitherto thought to be computationally intractable, can, in fact, be implemented in real-time-a result with many practical implications. To provide complexity information beyond the mean, we derive a closed-form expression for the variance of the complexity of sphere-decoding algorithm in a finite lattice. Furthermore, we consider the expected complexity of sphere decoding for channels with memory, where the lattice-generating matrix has a special Toeplitz structure. Results indicate that the expected complexity in this case is, too, polynomial over a wide range of SNRs, rates, data blocks, and channel impulse response lengths

Graph-theoretic channel modeling and topology control protocols for wireless sensor networks

This report addresses two different research problems: (i) It presents a wireless channel model that reduces the complexity associated with high order Markov chains; and (ii) presents energy efficient topology control protocols which provide reliability while maintaining the topology in an energy efficient manner. For the above problems, real wireless sensor network traces were collected and extensive simulations were performed for evaluating the proposed protocols. Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this report, a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget is presented. The implication of unused states in complexity reduction of higher order Markov model is also explained. The trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models. Furthermore, a methodology to preserve energy is presented to increase the network lifetime by reducing the node degree forming an active backbone while considering network connectivity. However, in energy stringent wireless sensor networks, it is of utmost importance to construct the reduced topology with the minimal control overhead. Moreover, most wireless links in practice are lossy links with connectivity probability which desires that a routing protocol provides routing flexibility and reliability at a minimum energy consumption cost. For this purpose, distributed and semi-distributed novel graph-theoretic topology construction protocols are presented that exploit cliques and polygons in a WSN to achieve energy efficiency and reliability. The proposed protocols also facilitate load rotation under topology maintenance, thereby extending the network lifetime. In addition to the above, the report also evaluates why the backbone construction using connected dominating set (CDS) in certain cases remains unable to provide connected sensing coverage in the area covered. For this purpose, a novel protocol that reduces the topology while considering sensing area coverage is presented

Path Planning and Communication Strategies to Enable Connectivity in Robotic Systems

There has been considerable interest in the area of communication-aware robotics in recent years, where the sensing, communication and motion objectives of robotic systems are jointly optimized. One particular open problem in this area is that of exploiting the mobility of unmanned vehicles in order to improve or satisfy communication objectives in realistic communication environments. Progress in this field could not only affect robust networked operation of unmanned vehicles but also would improve communication systems of the future (e.g. 5G), thus contributing to both areas of robotics and communications. This mobility-enabled connectivity and communication is the main area of interest in this dissertation.This dissertation is focused on path planning and communication strategies for robotic systems seeking to satisfy certain communication objectives in realistic communication environments experiencing path loss, shadowing and multipath fading. We consider realistic communication environments by leveraging and incorporating a probabilistic channel prediction framework that allows the robots to predict the channel quality at unvisited locations. This thesis then contributes to the area of mobility and connectivity through three main topics 1) energy-optimal distributed beamforming, 2) finding the statistics of the distance traveled until connectivity, and 3) path planning for connectivity. First, in energy-optimal distributed beamforming, we utilize the motion of a group of initially unconnected mobile robots to enable new forms of connectivity. More specifically, we co-optimize their locations and transmission powers to cooperatively enable connectivity through distributed beamforming. We further bring a foundational theoretical understanding to robotic distributed beamforming. Next, in finding the statistics of the distance traveled until connectivity, we analytically characterize the probability density function of the distance traveled by an initially unconnected robot until it gets connected to a remote node as it moves along a given path. We utilize tools from the stochastic differential equation literature to develop this characterization. Finally, in path planning for connectivity, we actively plan the path of a mobile robot such that it finds a connected spot with a minimum expected traveled distance (i.e., energy). The scenario considered in this part is in fact a more general one, and tackles the problem of path planning on a graph to minimize the expected cost incurred until the successful completion of a task. This framework has applications beyond path planning for connectivity, in areas such as celestial body imaging, human-robot collaboration, and search scenarios. We bring a foundational understanding to this problem. We show how this problem is inherently hard to solve (NP-complete) and also propose a path planner, based on a game-theoretic framework, that provides an asymptotic optimality guarantee.Overall, this thesis proposes novel strategies for utilizing the mobility of unmanned vehicles and enabling connectivity while considering the underlying energy constraints. We also provide a rigorous theoretical analysis of the aforementioned problems using a wide range of tools from communications theory, game theory, optimal control and time series literature. Moreover, through extensive realistic numerical studies using real channel parameters/data, we show the efficiency and performance of our proposed approaches