Routing Using Safe Reinforcement Learning

The ever increasing number of connected devices has lead to a metoric rise in the amount data to be processed. This has caused computation to be moved to the edge of the cloud increasing the importance of efficiency in the whole of cloud. The use of this fog computing for time-critical control applications is on the rise and requires robust guarantees on transmission times of the packets in the network while reducing total transmission times of the various packets. We consider networks in which the transmission times that may vary due to mobility of devices, congestion and similar artifacts. We assume knowledge of the worst case tranmssion times over each link and evaluate the typical tranmssion times through exploration. We present the use of reinforcement learning to find optimal paths through the network while never violating preset deadlines. We show that with appropriate domain knowledge, using popular reinforcement learning techniques is a promising prospect even in time-critical applications

Improving Performance of Feedback-Based Real-Time Networks using Model Checking and Reinforcement Learning

Traditionally, automatic control techniques arose due to need for automation in mechanical systems. These techniques rely on robust mathematical modelling of physical systems with the goal to drive their behaviour to desired set-points. Decades of research have successfully automated, optimized, and ensured safety of a wide variety of mechanical systems. Recent advancement in digital technology has made computers pervasive into every facet of life. As such, there have been many recent attempts to incorporate control techniques into digital technology. This thesis investigates the intersection and co-application of control theory and computer science to evaluate and improve performance of time-critical systems. The thesis applies two different research areas, namely, model checking and reinforcement learning to design and evaluate two unique real-time networks in conjunction with control technologies. The first is a camera surveillance system with the goal of constrained resource allocation to self-adaptive cameras. The second is a dual-delay real-time communication network with the goal of safe packet routing with minimal delays.The camera surveillance system consists of self-adaptive cameras and a centralized manager, in which the cameras capture a stream of images and transmit them to a central manager over a shared constrained communication channel. The event-based manager allocates fractions of the shared bandwidth to all cameras in the network. The thesis provides guarantees on the behaviour of the camera surveillance network through model checking. Disturbances that arise during image capture due to variations in capture scenes are modelled using probabilistic and non-deterministic Markov Decision Processes (MDPs). The different properties of the camera network such as the number of frame drops and bandwidth reallocations are evaluated through formal verification.The second part of the thesis explores packet routing for real-time networks constructed with nodes and directed edges. Each edge in the network consists of two different delays, a worst-case delay that captures high load characteristics, and a typical delay that captures the current network load. Each node in the network takes safe routing decisions by considering delays already encountered and the amount of remaining time. The thesis applies reinforcement learning to route packets through the network with minimal delays while ensuring the total path delay from source to destination does not exceed the pre-determined deadline of the packet. The reinforcement learning algorithm explores new edges to find optimal routing paths while ensuring safety through a simple pre-processing algorithm. The thesis shows that it is possible to apply powerful reinforcement learning techniques to time-critical systems with expert knowledge about the system

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Learning in Real-Time Search: A Unifying Framework

Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agents current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only is a potential user confronted with a hodgepodge of algorithms, but he also faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*, SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks

Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians.  Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics.  In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works

Learning Algorithms for Minimizing Queue Length Regret

We consider a system consisting of a single transmitter/receiver pair and $N$ channels over which they may communicate. Packets randomly arrive to the transmitter's queue and wait to be successfully sent to the receiver. The transmitter may attempt a frame transmission on one channel at a time, where each frame includes a packet if one is in the queue. For each channel, an attempted transmission is successful with an unknown probability. The transmitter's objective is to quickly identify the best channel to minimize the number of packets in the queue over $T$ time slots. To analyze system performance, we introduce queue length regret, which is the expected difference between the total queue length of a learning policy and a controller that knows the rates, a priori. One approach to designing a transmission policy would be to apply algorithms from the literature that solve the closely-related stochastic multi-armed bandit problem. These policies would focus on maximizing the number of successful frame transmissions over time. However, we show that these methods have $\Omega(\log{T})$ queue length regret. On the other hand, we show that there exists a set of queue-length based policies that can obtain order optimal $O(1)$ queue length regret. We use our theoretical analysis to devise heuristic methods that are shown to perform well in simulation.Comment: 28 Pages, 11 figure

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields