Deterministic Recurrent Communication and Synchronization in Restricted Sensor Networks

Monitoring physical phenomena in Sensor Networks requires guaranteeing permanent communication between nodes. Moreover, in an eective implementation of such infrastructure, the delay between any two consecutive communications should be minimized. The problem is challenging because, in a restricted Sensor Network, the communication is carried out through a single and shared radio channel without collision detection. Dealing with collisions is crucial to ensure eective communication between nodes. Additionally, minimizing them yields energy consumption minimization, given that sensing and computational costs in terms of energy are negligible with respect to radio communication. In this work, we present a deterministic recurrent-communication protocol for Sensor Networks. After an initial negotiation phase of the access pattern to the channel, each node running this protocol reaches a steady state, which is asymptotically optimal in terms of energy and time effciency. As a by-product, a protocol for the synchronization of a Sensor Network is also proposed. Furthermore, the protocols are resilient to an arbitrary node power-up schedule and a general node failure model

Compensation of distributed delays in integrated communication and control systems

The concept, analysis, implementation, and verification of a method for compensating delays that are distributed between the sensors, controller, and actuators within a control loop are discussed. With the objective of mitigating the detrimental effects of these network induced delays, a predictor-controller algorithm was formulated and analyzed. Robustness of the delay compensation algorithm was investigated relative to parametric uncertainties in plant modeling. The delay compensator was experimentally verified on an IEEE 802.4 network testbed for velocity control of a DC servomotor

Robust synchronization of a class of coupled delayed networks with multiple stochastic disturbances: The continuous-time case

In this paper, the robust synchronization problem is investigated for a new class of continuous-time complex networks that involve parameter uncertainties, time-varying delays, constant and delayed couplings, as well as multiple stochastic disturbances. The norm-bounded uncertainties exist in all the network parameters after decoupling, and the stochastic disturbances are assumed to be Brownian motions that act on the constant coupling term, the delayed coupling term as well as the overall network dynamics. Such multiple stochastic disturbances could reflect more realistic dynamical behaviors of the coupled complex network presented within a noisy environment. By using a combination of the Lyapunov functional method, the robust analysis tool, the stochastic analysis techniques and the properties of Kronecker product, we derive several delay-dependent sufficient conditions that ensure the coupled complex network to be globally robustly synchronized in the mean square for all admissible parameter uncertainties. The criteria obtained in this paper are in the form of linear matrix inequalities (LMIs) whose solution can be easily calculated by using the standard numerical software. The main results are shown to be general enough to cover many existing ones reported in the literature. Simulation examples are presented to demonstrate the feasibility and applicability of the proposed results

model checking for data anomaly detection

Abstract Data tipically evolve according to specific processes, with the consequent possibility to identify a profile of evolution: the values it may assume, the frequencies at which it changes, the temporal variation in relation to other data, or other constraints that are directly connected to the reference domain. A violation of these conditions could be the signal of different menaces that threat the system, as well as: attempts of a tampering or a cyber attack, a failure in the system operation, a bug in the applications which manage the life cycle of data. To detect such violations is not straightforward as processes could be unknown or hard to extract. In this paper we propose an approach to detect data anomalies. We represent data user behaviours in terms of labelled transition systems and through the model checking techniques we demonstrate the proposed modeling can be exploited to successfully detect data anomalies

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Bio-inspired vision-based leader-follower formation flying in the presence of delays

Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles) to date. Towards this goal, we make three contributions in this paper: (i) we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii) we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii) unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents