When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Methodologies for the analysis of value from delay-tolerant inter-satellite networking

In a world that is becoming increasingly connected, both in the sense of people and devices, it is of no surprise that users of the data enabled by satellites are exploring the potential brought about from a more connected Earth orbit environment. Lower data latency, higher revisit rates and higher volumes of information are the order of the day, and inter-connectivity is one of the ways in which this could be achieved. Within this dissertation, three main topics are investigated and built upon. First, the process of routing data through intermittently connected delay-tolerant networks is examined and a new routing protocol introduced, called Spae. The consideration of downstream resource limitations forms the heart of this novel approach which is shown to provide improvements in data routing that closely match that of a theoretically optimal scheme. Next, the value of inter-satellite networking is derived in such a way that removes the difficult task of costing the enabling inter-satellite link technology. Instead, value is defined as the price one should be willing to pay for the technology while retaining a mission value greater than its non-networking counterpart. This is achieved through the use of multi-attribute utility theory, trade-space analysis and system modelling, and demonstrated in two case studies. Finally, the effects of uncertainty in the form of sub-system failure are considered. Inter-satellite networking is shown to increase a system's resilience to failure through introduction of additional, partially failed states, made possible by data relay. The lifetime value of a system is then captured using a semi-analytical approach exploiting Markov chains, validated with a numerical Monte Carlo simulation approach. It is evident that while inter-satellite networking may offer more value in general, it does not necessarily result in a decrease in the loss of utility over the lifetime.In a world that is becoming increasingly connected, both in the sense of people and devices, it is of no surprise that users of the data enabled by satellites are exploring the potential brought about from a more connected Earth orbit environment. Lower data latency, higher revisit rates and higher volumes of information are the order of the day, and inter-connectivity is one of the ways in which this could be achieved. Within this dissertation, three main topics are investigated and built upon. First, the process of routing data through intermittently connected delay-tolerant networks is examined and a new routing protocol introduced, called Spae. The consideration of downstream resource limitations forms the heart of this novel approach which is shown to provide improvements in data routing that closely match that of a theoretically optimal scheme. Next, the value of inter-satellite networking is derived in such a way that removes the difficult task of costing the enabling inter-satellite link technology. Instead, value is defined as the price one should be willing to pay for the technology while retaining a mission value greater than its non-networking counterpart. This is achieved through the use of multi-attribute utility theory, trade-space analysis and system modelling, and demonstrated in two case studies. Finally, the effects of uncertainty in the form of sub-system failure are considered. Inter-satellite networking is shown to increase a system's resilience to failure through introduction of additional, partially failed states, made possible by data relay. The lifetime value of a system is then captured using a semi-analytical approach exploiting Markov chains, validated with a numerical Monte Carlo simulation approach. It is evident that while inter-satellite networking may offer more value in general, it does not necessarily result in a decrease in the loss of utility over the lifetime

Compilation Optimizations to Enhance Resilience of Big Data Programs and Quantum Processors

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), exist, though they can be difficult to implement, expensive, and may be limited to only protecting against errors in specific locations. Researchers have been exploring software detection and correction methods as an alternative, commonly trading either overhead in execution time or memory usage to protect against faults. Quantum computers, a relatively recent advancement in computing technology, experience similar errors on a much more severe scale. The errors are more frequent, costly, and difficult to detect and correct. Error correction algorithms like Shor’s code promise to completely remove errors, but they cannot be implemented on current noisy intermediate-scale quantum (NISQ) systems due to the low number of available qubits. Until the physical systems become large enough to support error correction, researchers instead have been studying other methods to reduce and compensate for errors. In this work, we present two methods for improving the resilience of classical processes, both single- and multi-threaded. We then introduce quantum computing and compare the nature of errors and correction methods to previous classical methods. We further discuss two designs for improving compilation of quantum circuits. One method, focused on quantum neural networks (QNNs), takes advantage of partial compilation to avoid recompiling the entire circuit each time. The other method is a new approach to compiling quantum circuits using graph neural networks (GNNs) to improve the resilience of quantum circuits and increase fidelity. By using GNNs with reinforcement learning, we can train a compiler to provide improved qubit allocation that improves the success rate of quantum circuits

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis