Actively searching for targets using a multi-agent system in an unknown environment poses a two-pronged prob-lem, where on the one hand we need agents to cover as much of the environment as possible and on the other have a higher density of agents where there are potential targets to maximize detection performance. This paper proposes a fully distributed solution for an ad hoc network of agents to cooperatively search an unknown environment and actively track found targets. The solution combines a distributed pheromone-based coverage control strategy with a distributed target selection mechanism.

This article revisits a multiagent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the assumptions of instantaneous communication and synchronous actions by agents (request and response actions). In this article, we propose a fully asynchronous communication aware solution to the multiagent deployment problem that uses an event-triggered broadcasting strategy. Unlike all existing triggered solutions, our event-triggered broadcasting algorithm relies on agents to decide when to broadcast (push) information to others in the network without the need for a response from other agents. In addition, the proposed strategy determines how best to move when up-to-date information is unavailable and cannot be requested. The algorithm is capable of achieving similar levels of performance to that of a continuous or periodic strategy. Our solution is proven to achieve asymptotic convergence, and simulation results are provided to demonstrate that the proposed event-triggered broadcasting algorithm can achieve an adequate level of performance under the communication constraints.

The sheer size of IoT networks being deployed today presents an "attack surface" and poses significant security risks at a scale never before encountered. In other words, a single device/node in a network that becomes infected with malware has the potential to spread malware across the network, eventually ceasing the network functionality. Simply detecting and quarantining the malware in IoT networks does not guarantee to prevent malware propagation. On the other hand, use of traditional control theory for malware confinement is not effective, as most of the existing works do not consider real-time malware control strategies that can be implemented using uncertain infection information of the nodes in the network or have the containment problem decoupled from network performance. In this work, we propose a two-pronged approach, where a runtime malware detector (HaRM) that employs Hardware Performance Counter (HPC) values to detect the malware and benign applications is devised. This information is fed during runtime to a stochastic model predictive controller to confine the malware propagation without hampering the network performance. With the proposed solution, a runtime malware detection accuracy of 92.21% with a runtime of 10ns is achieved, which is an order of magnitude faster than existing malware detection solutions. Synthesizing this output with the model predictive containment strategy lead to achieving an average network throughput of nearly 200% of that of IoT networks without any embedded defense.

Generalized from image and language translation, graph translation aims to generate a graph in the target domain by conditioning an input graph in the source domain. This promising topic has attracted fast-increasing attention recently. Existing works are limited to either merely predicting the node attributes of graphs with fixed topology or predicting only the graph topology without considering node attributes, but cannot simultaneously predict both of them, due to substantial challenges: 1) difficulty in characterizing the interactive, iterative, and asynchronous translation process of both nodes and edges and 2) difficulty in discovering and maintaining the inherent consistency between the node and edge in predicted graphs. These challenges prevent a generic, end-to-end framework for joint node and edge attributes prediction, which is a need for real-world applications such as malware confinement in IoT networks and structural-to-functional network translation. These real-world applications highly depend on hand-crafting and ad-hoc heuristic models, but cannot sufficiently utilize massive historical data. In this paper, we termed this generic problem "multi-attributed graph translation" and developed a novel framework integrating both node and edge translations seamlessly. The novel edge translation path is generic which is proven to be a generalization of the existing topology translation models. Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed to learn and maintain the consistency of the predicted nodes and edges. Finally, extensive experiments on both synthetic and real-world application data demonstrated the effectiveness of the proposed method.

In this paper we consider an online planning problem for unmanned aerial vehicle (UAV) operations. Specifically, a UAV has the task of reaching a goal from a set of possible goals while minimizing the amount of energy required. Due to unforeseen disturbances, it is possible that initially attractive goals might end up being very expensive during the execution. Thus, two main problems are investigated here: i) how to predict and plan the motion of the UAV at run time to minimize its energy consumption and ii) when to schedule next replanning time to avoid unnecessary periodic re-evaluation executions. Our approach considers a nonlinear model of the system for which a model predictive controller is used to determine the desired control inputs for each possible goal. These control inputs are then used to estimate the energy required to reach the different goals. Finally, a self-triggered scheduling policy determines how long to wait before replanning the goal to aim for. The proposed framework is validated through simulations and experiments in which a quadrotor must choose and reach some goal while being subject to external disturbances.