Alvi Ataur Khalil

Alvi Ataur Khalil, Javier Franco, Imtiaz Parvez et al.

Blockchain has become a key technology in a plethora of application domains owing to its decentralized public nature. The cyber-physical systems (CPS) is one of the prominent application domains that leverage blockchain for myriad operations, where the Internet of Things (IoT) is utilized for data collection. Although some of the CPS problems can be solved by simply adopting blockchain for its secure and distributed nature, others require complex considerations for overcoming blockchain-imposed limitations while maintaining the core aspect of CPS. Even though a number of studies focus on either the utilization of blockchains for different CPS applications or the blockchain-enabled security of CPS, there is no comprehensive survey including both perspectives together. To fill this gap, we present a comprehensive overview of contemporary advancement in using blockchain for enhancing different CPS operations as well as improving CPS security. To the best of our knowledge, this is the first paper that presents an in-depth review of research on blockchain-enabled CPS operation and security.

Nur Imtiazul Haque, Mohammad Ashiqur Rahman, Md Hasan Shahriar et al.

Smart healthcare systems (SHSs) are providing fast and efficient disease treatment leveraging wireless body sensor networks (WBSNs) and implantable medical devices (IMDs)-based internet of medical things (IoMT). In addition, IoMT-based SHSs are enabling automated medication, allowing communication among myriad healthcare sensor devices. However, adversaries can launch various attacks on the communication network and the hardware/firmware to introduce false data or cause data unavailability to the automatic medication system endangering the patient's life. In this paper, we propose SHChecker, a novel threat analysis framework that integrates machine learning and formal analysis capabilities to identify potential attacks and corresponding effects on an IoMT-based SHS. Our framework can provide us with all potential attack vectors, each representing a set of sensor measurements to be altered, for an SHS given a specific set of attack attributes, allowing us to realize the system's resiliency, thus the insight to enhance the robustness of the model. We implement SHChecker on a synthetic and a real dataset, which affirms that our framework can reveal potential attack vectors in an IoMT system. This is a novel effort to formally analyze supervised and unsupervised machine learning models for black-box SHS threat analysis.

Alvi Ataur Khalil, Alexander J Byrne, Mohammad Ashiqur Rahman et al.

Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employing fleets of UAVs have become popular. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a Partially Observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.