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We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, our framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. If repositioning robots locally fails to attain a user-specified level of desired coverage, the central controller augments the team with additional robots from the pool. The size of the local neighborhood around the failed robot and the desired coverage over the region are two objectives that can be varied to achieve a user-specified balance. We investigate the trade-off between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. Through extensive simulations and an experiment with a team of seven quadrotors we verify the effectiveness of our framework. We show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.

In this paper, we design algorithms to protect swarm-robotics applications against sensor denial-of-service (DoS) attacks on robots. We focus on applications requiring the robots to jointly select actions, e.g., which trajectory to follow, among a set of available ones. Such applications are central in large-scale robotic applications, such as multi-robot motion planning for target tracking. But the current attack-robust algorithms are centralized. In this paper, we propose a general-purpose distributed algorithm towards robust optimization at scale, with local communications only. We name it Distributed Robust Maximization (DRM). DRM proposes a divide-and-conquer approach that distributively partitions the problem among cliques of robots. Then, the cliques optimize in parallel, independently of each other. We prove DRM achieves a close-to-optimal performance. We demonstrate DRM's performance in both Gazebo and MATLAB simulations, in scenarios of active target tracking with swarms of robots. In the simulations, DRM achieves computational speed-ups, being 1-2 orders faster than the centralized algorithms; yet, it nearly matches the tracking performance of the centralized counterparts. Since, DRM overestimates the number of attacks in each clique, in this paper we also introduce an Improved Distributed Robust Maximization (IDRM) algorithm. IDRM infers the number of attacks in each clique less conservatively than DRM by leveraging 3-hop neighboring communications. We verify IDRM improves DRM's performance in simulations.

We study a distributed coordination problem in which a group of robots collaborates to maximize a submodular objective function. We consider an adversarial scenario where a subset of the robots can be attacked and cannot contribute to the objective function. We do not know which robots will be attacked, but know an upper bound of how many can be attacked. We study the distributed version of the problem, where each robot must choose its own actions only by communicating with its neighbors. We present a distributed resilient submodular maximization algorithm that guarantees performance within a constant factor of the optimal. Our analysis uses the curvature of submodular set functions. We show that the algorithm is scalable, runs in polynomial time, and is faster than its centralized version. We demonstrate the efficacy of our algorithm through simulations of a multi-robot target tracking scenario.

We introduce and study the problem of planning a trajectory for an agent to carry out a reconnaissance mission while avoiding being detected by an adversarial guard. This introduces a multi-objective version of classical visibility-based target search and pursuit-evasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the guard. The objective is to find a finite-horizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability.We model this problem as a discrete, sequential, two-player, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search trees can yield the optimal policy but may require possibly exponential computational time and space. We propose several pruning techniques to reduce the computational cost while still preserving optimality guarantees. Simulation results show that the proposed strategy prunes approximately three orders of magnitude nodes as compared to the brute-force strategy. We also find that the Monte-Carlo search tree saves approximately one order of computational time as compared to the minimax search tree.

We study the problem of designing spoofing signals to corrupt and mislead the output of a Kalman filter. Unlike existing works that focus on detection and filtering algorithms for the observer, we study the problem from the attacker's point-of-view. In our model, the attacker can corrupt the measurements by adding spoofing signals. The attacker seeks to create a separation between the estimate of the Kalman filter with and without spoofing signals. We present a number of results on how to generate such spoofing signals, while minimizing the signal magnitude. The resulting algorithms are evaluated through simulations along with theoretical proofs.

We study the problem of reducing the amount of communication in a distributed target tracking problem. We focus on the scenario where a team of robots are allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past that guarantee that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on reducing the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information from the neighbor. We prove that this strategy converges to an optimal formation. We compare the two approaches (constant communication and self-triggered communication) through simulations of tracking stationary and mobile targets.

Industry 4.0 (the fourth industrial revolution) encapsulates future industry development trends to achieve more intelligent manufacturing processes, including reliance on Cyber-Physical Systems (CPS), construction of Cyber-Physical Production Systems (CPPS), and implementation and operation of smart factories. This paper introduces relevant aspects of Industry 4.0 in relation to strategic planning, key technologies, opportunities, and challenges. Strategic planning includes construction of a CPS network, discussion of two major themes which are based on the smart factory and intelligent production, achieving three integrations (horizontal integration, vertical integration and end-to-end integration) and achieving eight plans which consist of the formulation of system standardization, efficient management etc. Finally, it referred to the enlightenment for China's manufacturing industries, to build China's Industry 4.0.

This paper presents a distributed model predictive control(DMPC) strategy to achieve cooperative tasks like consensus and synchronization of linear systems with coupled constraints. Based on the error of state between each subsystem and the center of its cooperative set, a novel DMPC algorithm is designed with its robust feasibility guaranteed by permitting only one subsystem to optimize at each time instant, while renew other subsystems' plans. Illustrative examples are finally conducted to verify the effectiveness of the DMPC strategy.

Multi-hop relaying is an important concept in future generation wireless networks. It can address the inherent problems of limited capacity and coverage in cellular networks. However, most existing multi-hop relaying architectures waste energy. In this paper, our goal is to find a power-effective multi-hop relaying architecture, since the problem of coverage extension may occur in the given target service area where users' traffic density is relatively low or moderate, this paper introduces several popular topologies and the corresponding coverage extension methods for each case. Further, sets up the optimized mathematical model and obtain the simulation results which show that Type-D is the best topology which consumes less power than Type-A, Type-B and Type-C.

In this paper, we exploit the channel-orthogonal space-time block codings (CO-STBCs) in multiple-input multiple-output (MIMO) system. First we provide principles for designing CO-STBC schemes with angle feedback and propose two COSTBC schemes (Scheme A and Scheme B) based on quasi-orthogonal STBC (QO-STBC) with different orthogonality strategies. Then we analyze the quantization of the feedback angle information and deduct its errors in the proposed COSTBC schemes. Simulations verify the effectiveness of the proposed CO-STBCs, and the quantization process brings different errors to these schemes. It is demonstrated that the Scheme A outperforms Scheme B with the same number of feedback bits, and Scheme B always needs one more bit for feedback than Scheme A in order to reach the same performance.

In this paper, we propose an adaptive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system based on quasi-orthogonal space time/frequency block coding (QO-STBC/SFBC) with an improved angle feedback scheme. First we exploit the improved feedback scheme which can minimize the non-orthogonality of QO-STBC/SFBC by rotating constellation symbols. Then we investigate its quantification method and the related criterions. Furthermore, we apply the adaptive modulation technique to this scheme in frequency selective channel. Simulation results show that the proposed feedback scheme can provide higher signal-to-noise ratio (SNR) gains, and systems with such scheme can get significant improvement by applying adaptive modulation technique.

In this paper, we introduce a novel end-to-end approach to QoS management with respect to the diagnosis of QoS violations. We first use a set of end-to-end flow traffic statistics to describe a QoS violation. Subsequently, neural network techniques are engaged to identify and differentiate QoS violations through classification of the collected statistics. Through experiments, we find that our scheme outperforms traditional rule-based methods which require clear margins of QoS parameters in asserting a QoS violation.

In this paper, we propose a novel cross-domain QoS framework for coordinated end-to-end QoS adaptation. The framework treats each autonomous QoS service on the end-to-end path as a QoS component and models its capabilities and behavior with respect to the requirements of end-to-end QoS coordination. Based on such information, the end-to-end QoS framework runtime copes with a QoS violation by selecting a suitable QoS adaptation solution among all the end-to-end candidate choices. Preliminary simulation results demonstrate the feasibility of our framework for distributed end-to-end QoS coordination.

Quality of Service is commonly specified through either a set of pre-defined APIs or a specification language. The former approach has inherent limitations that make QoS specification heuristic, while the latter largely remains at syntax level, which is strong in representation formats, but weak in the semantic interpretation of QoS specification. To overcome these limitations, we propose here a semantic QoS specification (SQS) framework, which (1) provides a uniform framework for QoS specification, (2) offers tailored specification support for different application domains, and (3) facilitates machine processing of QoS specification. We adopt an ontology-based approach in the design of SQS and introduce two generic models for a more flexibile, accurate and expressive QoS specification. Moreover, the ontology modeling principle of SQS is sufficiently general to apply to the modeling of other QoS systems, which facilitates information sharing and negotiation among QoS entities along the end-to-end path. Our experimental results show that SQS have exhibited the above advantages for QoS specification with acceptable overhead in term of processing latency.

In this paper, we propose a formal knowledge modeling methodology based on ontology to assist in the logic reasoning of distributed QoS information which is commonly not available in traditional QoS modeling and support. As part of our project toward a semantic-based QoS control and management framework, the knowledge modeling scheme presented in this paper enables an explicit representation of various QoS concepts and their relations, thus provides a foundation for understanding and decision-making of QoS during setup and runtime. The technical feasibility of our knowledge-based QoS scheme has been demonstrated in our prototype, which yields the aforementioned benefits with acceptable processing overhead on PCs and laptops.

Future distributed applications are expected to be deployed in an environment that is more dynamic and heterogeneous than ever before. The environment features are difficult to predict beforehand due to their variations. To cope with such variations, it is important that the protocol stack is adaptable to changing requirements. This paper proposes a component-based Dynamical Protocol Framework (DPF) for multimedia applications. DPF dynamically builds an adaptive protocol stack by automatic discovery of protocol components and runtime configuration/reconfiguration of the protocol stack. Performance measurement shows that compared with directly using interfaces provided by JMF, using DPF to set up a dynamic protocol stack incurs slight overhead in unicast, and a similar condition is observed in multicast. This is the trade-off for the abilities to configure and reconfigure protocol stack during stream set-up and run time.

Recognizing the need to provide end-to-end QoS for performance sensitive applications, and the need to manage such QoS provisions in the presence of diverse application QoS requirements as well as heterogeneous network environment, we propose an adaptive end-to-end QoS management architecture QMan, to address such issues. QMan, as one of the key components of OCTOPUS middleware, makes no presumption of the underneath QoS provisions for end-to-end QoS management, but can take advantage of such provisions if there are. QMan allows applications to exchange QoS requirements and negotiate for a suitable protocol stack configuration with the assistance from dynamic protocol framework (DPF). During data transmission, QMan monitors QoS violations and performs QoS adaptations according to the rules defined through a combination of three possible conduits in an end-to-end fashion: network level, host level and application level. These rules are pre-determined according to application requirements or network policies and can be re-configured for customization of QoS adaptations during run-time. Performance evaluations of our implementation demonstrate the feasibility of QMan's design with satisfactory performance.