Searching for black holes in subways.

Abstract Current mobile agent algorithms for mapping faults in computer networks assume that the network is static. However, for large classes of highly dynamic networks (e.g., wireless mobile ad hoc networks, sensor networks, vehicular networks), the topology changes as a function of time. These networks, called delay-tolerant, challenged, opportunistic, etc., have never been investigated with regard to locating faults. We consider a subclass of these networks modelled on an urban subway system. We examine the problem of creating a map of such a subway. More precisely, we study the problem of a team of asynchronous computational entities (the mapping agents) determining the location of black holes in a highly dynamic graph, whose edges are defined by the asynchronous movements of mobile entities (the subway carriers). We determine necessary conditions for the problem to be solvable. We then present and analyze a solution protocol; we show that our algorithm solves the fault mapping problem in subway networks with the minimum number of agents possible, k = γ + 1, where γ is the number of carrier stops at black holes. The number of carrier moves between stations required by the algorithm in the worst case is , where n C is the number of subway trains, and l R is the length of the subway route with the most stops. We establish lower bounds showing that this bound is tight. Thus, our protocol is both agent-optimal and move-optimal

Faulty node repair and dynamically spawned black hole search

New threats to networks are constantly arising. This justifies protecting network assets and mitigating the risk associated with attacks. In a distributed environment, researchers aim, in particular, at eliminating faulty network entities. More specifically, much research has been conducted on locating a single static black hole, which is defined as a network site whose existence is known a priori and that disposes of any incoming data without leaving any trace of this occurrence. However, the prevalence of faulty nodes requires an algorithm able to (a) identify faulty nodes that can be repaired without human intervention and (b) locate black holes, which are taken to be faulty nodes whose repair does require human intervention. In this paper, we consider a specific attack model that involves multiple faulty nodes that can be repaired by mobile software agents, as well as a virus v that can infect a previously repaired faulty node and turn it into a black hole. We refer to the task of repairing multiple faulty nodes and pointing out the location of the black hole as the Faulty Node Repair and Dynamically Spawned Black Hole Search. Wefirst analyze the attack model we put forth. We then explain (a) how to identify whether a node is either (1) a normal node or (2) a repairable faulty node or (3) the black hole that has been infected by virus v during the search/repair process and, (b) how to perform the correct relevant actions. These two steps constitute a complex task, which, we explain, significantly differs from the traditional Black Hole Search. We continue by proposing an algorithm to solve this problem in an

Fault Recovery in Swarm Robotics Systems using Learning Algorithms

When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy

Autonomous Decision Making Facing Uncertainty, Risk, and Complexity

The creation of autonomous decision makers to handle potentially hazardous or logistically complicated tasks is desirable for the purpose of reducing human labor and exposure to risk, and for increasing the general performance of autonomous systems in a wide variety of environments. However, traditional autonomous decision makers perform poorly when faced with the uncertainty, risk, and complexity of these situations. In this dissertation, I explore these problems through three distinct case studies, and make significant improvements in the performance of autonomous decision makers through the application and synthesis of concepts from decision theory, risk assessment, and computational cognition. The first case study is a terrain navigation problem which shows how the availability of information and information fidelity affect performance, and develops and validates techniques to overcome these problems. Next, an automated design problem is used to explore and evaluate techniques for handling intractably large decision spaces. Finally, a mission planning and command simulation is performed to investigate how multiple time delineated decisions can be made efficiently. Through these three case studies, I show that autonomous decision-makers can be made to perform efficiently and effectively under uncertain, high-risk, and incredibly complex circumstances

ContextErlang: A language for distributed context-aware self-adaptive applications

Self-adaptive software modifies its behavior at run time to satisfy changing requirements in a dynamic environment. Context-oriented programming (COP) has been recently proposed as a specialized programming paradigm for context-aware and adaptive systems. COP mostly focuses on run time adaptation of the application’s behavior by supporting modular descriptions of behavioral variations. However, self-adaptive applications must satisfy additional requirements, such as distribution and concurrency, support for unforeseen changes and enforcement of correct behavior in the presence of dynamic change. Addressing these issues at the language level requires a holistic design that covers all aspects and takes into account the possibly cumbersome interaction of those features, for example concurrency and dynamic change. We present ContextErlang, a COP programming language in which adaptive abstractions are seamlessly integrated with distribution and concurrency. We define ContextErlang’s formal semantics, validated through an executable prototype, and we show how it supports formal proofs that the language design ensures satisfaction of certain safety requirements. We provide empirical evidence that ContextErlang is an effective solution through case studies and a performance assessment. We also show how the same design principles that lead to the development of ContextErlang can be followed to systematically design contextual extensions of other languages. A concrete example is presented concerning ContextScala

Swarm robotics: Cooperative navigation in unknown environments

Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection