Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation

We study distributed average consensus problems in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the nodes, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol on quantized values and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value represented as the ratio of two integer that is equal to the exact average. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm

Event-triggered Consensus Frameworks for Multi-agent Systems

Recently, distributed multi-agent systems (MAS) have been widely studied for a variety of engineering applications, including cooperative vehicular systems, sensor networks, and electrical power grids. To solve the allocated tasks in MASs, each agent autonomously determines the appropriate actions using information available locally and received from its neighbours. Many cooperative behaviours in MAS are based on a consensus algorithm. Consensus, by definition, is to distributively agree on a parameter of interest between the agents. Depending on the application, consensus has different configurations such as leader-following, formation, synchronization in robotic arms, and state estimation in sensor networks. Consensus in MASs requires local measurements and information exchanges between the neighbouring agents. Due to the energy restriction, hardware limitation, and bandwidth constraint, strategies that reduce the amount of measurements and information exchanges between the agents are of paramount interest. Event-triggering transmission schemes are among the most recent strategies that efficiently reduce the number of transmissions. This dissertation proposes a number of event-triggered consensus (ETC) implementations which are applicable to MASs. Different performance objectives and physical constraints, such as a desired convergence rate, robustness to uncertainty in control realization, information quantization, sampled-data processing, and resilience to denial of service (DoS) attacks are included in realization of the proposed algorithms. A novel convex optimization is proposed which simultaneously designs the control and event-triggering parameters in a unified framework. The optimization governs the trade-off between the consensus convergence rate and intensity of transmissions. This co-design optimization is extended to an advanced class of event-triggered schemes, known as the dynamic event-triggering (DET), which is able to substantially reduce the amount of transmissions. In the presence of DoS attacks, the co-design optimization simultaneously computes the control and DET parameters so that the number of transmissions is reduced and a desired level of resilience to DoS is guaranteed. In addition to consensus, a formation-containment implementation is proposed, where the amount of transmissions are reduced using the DET schemes. The performance of the proposed implementations are evaluated through simulation over several MASs. The experimental results demonstrate the effectiveness of the proposed implementations and verify their design flexibility

Fault-tolerant Stochastic Distributed Systems

The present doctoral thesis discusses the design of fault-tolerant distributed systems, placing emphasis in addressing the case where the actions of the nodes or their interactions are stochastic. The main objective is to detect and identify faults to improve the resilience of distributed systems to crash-type faults, as well as detecting the presence of malicious nodes in pursuit of exploiting the network. The proposed analysis considers malicious agents and computational solutions to detect faults. Crash-type faults, where the affected component ceases to perform its task, are tackled in this thesis by introducing stochastic decisions in deterministic distributed algorithms. Prime importance is placed on providing guarantees and rates of convergence for the steady-state solution. The scenarios of a social network (state-dependent example) and consensus (time- dependent example) are addressed, proving convergence. The proposed algorithms are capable of dealing with packet drops, delays, medium access competition, and, in particular, nodes failing and/or losing network connectivity. The concept of Set-Valued Observers (SVOs) is used as a tool to detect faults in a worst-case scenario, i.e., when a malicious agent can select the most unfavorable sequence of communi- cations and inject a signal of arbitrary magnitude. For other types of faults, it is introduced the concept of Stochastic Set-Valued Observers (SSVOs) which produce a confidence set where the state is known to belong with at least a pre-specified probability. It is shown how, for an algorithm of consensus, it is possible to exploit the structure of the problem to reduce the computational complexity of the solution. The main result allows discarding interactions in the model that do not contribute to the produced estimates. The main drawback of using classical SVOs for fault detection is their computational burden. By resorting to a left-coprime factorization for Linear Parameter-Varying (LPV) systems, it is shown how to reduce the computational complexity. By appropriately selecting the factorization, it is possible to consider detectable systems (i.e., unobservable systems where the unobservable component is stable). Such a result plays a key role in the domain of Cyber-Physical Systems (CPSs). These techniques are complemented with Event- and Self-triggered sampling strategies that enable fewer sensor updates. Moreover, the same triggering mechanisms can be used to make decisions of when to run the SVO routine or resort to over-approximations that temporarily compromise accuracy to gain in performance but maintaining the convergence characteristics of the set-valued estimates. A less stringent requirement for network resources that is vital to guarantee the applicability of SVO-based fault detection in the domain of Networked Control Systems (NCSs)

Multi-sensing Data Fusion: Target tracking via particle filtering

In this Master's thesis, Multi-sensing Data Fusion is firstly introduced with a focus on perception and the concepts that are the base of this work, like the mathematical tools that make it possible. Particle filters are one class of these tools that allow a computer to perform fusion of numerical information that is perceived from real environment by sensors. For this reason they are described and state of the art mathematical formulas and algorithms for particle filtering are also presented. At the core of this project, a simple piece of software has been developed in order to test these tools in practice. More specifically, a Target Tracking Simulator software is presented where a virtual trackable object can freely move in a 2-dimensional simulated environment and distributed sensor agents, dispersed in the same environment, should be able to perceive the object through a state-dependent measurement affected by additive Gaussian noise. Each sensor employs particle filtering along with communication with other neighboring sensors in order to update the perceived state of the object and track it as it moves in the environment. The combination of Java and AgentSpeak languages is used as a platform for the development of this application

Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels

Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications

Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence

Inferring events of interest by fusing data from multiple heterogeneous sources has been an interesting and important topic in recent years. Several issues related to inference using heterogeneous data with complex and nonlinear dependence are investigated in this dissertation. We apply copula theory to characterize the dependence among heterogeneous data. In centralized detection, where sensor observations are available at the fusion center (FC), we study copula-based fusion. We design detection algorithms based on sample-wise copula selection and mixture of copulas model in different scenarios of the true dependence. The proposed approaches are theoretically justified and perform well when applied to fuse acoustic and seismic sensor data for personnel detection. Besides traditional sensors, the access to the massive amount of social media data provides a unique opportunity for extracting information about unfolding events. We further study how sensor networks and social media complement each other in facilitating the data-to-decision making process. We propose a copula-based joint characterization of multiple dependent time series from sensors and social media. As a proof-of-concept, this model is applied to the fusion of Google Trends (GT) data and stock/flu data for prediction, where the stock/flu data serves as a surrogate for sensor data. In energy constrained networks, local observations are compressed before they are transmitted to the FC. In these cases, conditional dependence and heterogeneity complicate the system design particularly. We consider the classification of discrete random signals in Wireless Sensor Networks (WSNs), where, for communication efficiency, only local decisions are transmitted. We derive the necessary conditions for the optimal decision rules at the sensors and the FC by introducing a hidden random variable. An iterative algorithm is designed to search for the optimal decision rules. Its convergence and asymptotical optimality are also proved. The performance of the proposed scheme is illustrated for the distributed Automatic Modulation Classification (AMC) problem. Censoring is another communication efficient strategy, in which sensors transmit only informative observations to the FC, and censor those deemed uninformative . We design the detectors that take into account the spatial dependence among observations. Fusion rules for censored data are proposed with continuous and discrete local messages, respectively. Their computationally efficient counterparts based on the key idea of injecting controlled noise at the FC before fusion are also investigated. In this thesis, with heterogeneous and dependent sensor observations, we consider not only inference in parallel frameworks but also the problem of collaborative inference where collaboration exists among local sensors. Each sensor forms coalition with other sensors and shares information within the coalition, to maximize its inference performance. The collaboration strategy is investigated under a communication constraint. To characterize the influence of inter-sensor dependence on inference performance and thus collaboration strategy, we quantify the gain and loss in forming a coalition by introducing the copula-based definitions of diversity gain and redundancy loss for both estimation and detection problems. A coalition formation game is proposed for the distributed inference problem, through which the information contained in the inter-sensor dependence is fully explored and utilized for improved inference performance