Biblio

Swarm robotics is a network based multi-device system designed to achieve shared objectives in a synchronized way. This system is widely used in industries like farming, manufacturing, and defense applications. In recent implementations, swarm robotics is integrated with Blockchain based networks to enhance communication, security, and decentralized decision-making capabilities. As most of the current blockchain applications are based on complex consensus algorithms, every individual robot in the swarm network requires high computing power to run these complex algorithms. Thus, it is a challenging task to achieve consensus between the robots in the network. This paper will discuss the details of designing an effective consensus algorithm that meets the requirements of swarm robotics network.

In this paper, we propose a novel method to obtain the damage model (connectivity) of a power distribution system (PDS) based on distributed consensus algorithm. The measurement and sensing units in the distribution network are modeled as an agent with limited communication capability that exchanges the information (switch status) to reach an agreement in a consensus algorithm. Besides, a communication graph is designed for agents to run the consensus algorithm which is efficient and robust during the disaster event. Agents can dynamically communicate with the other agent based on available links that are established and solve the distributed consensus algorithm quickly to come up with the correct topology of PDS. Numerical simulations are performed to demonstrate the effectiveness of the proposed approach with the help of an IEEE 123-node test case with 3 different sub-graphs.

Distributed consensus is a prototypical distributed optimization and decision making problem in social, economic and engineering networked systems. In collaborative applications investigating the effects of adversaries is a critical problem. In this paper we investigate distributed consensus problems in the presence of adversaries. We combine key ideas from distributed consensus in computer science on one hand and in control systems on the other. The main idea is to detect Byzantine adversaries in a network of collaborating agents who have as goal reaching consensus, and exclude them from the consensus process and dynamics. We describe a novel trust-aware consensus algorithm that integrates the trust evaluation mechanism into the distributed consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also introduce a trust propagation scheme in order to take into account evidences of other nodes in the network. The resulting algorithm is flexible and extensible, and can incorporate more complex designs of decision rules and trust models. To demonstrate the power of our trust-aware algorithm, we provide new theoretical security performance results in terms of miss detection and false alarm rates for regular and general trust graphs. We demonstrate through simulations that the new trust-aware consensus algorithm can effectively detect Byzantine adversaries and can exclude them from consensus iterations even in sparse networks with connectivity less than 2f+1, where f is the number of adversaries.