This paper addresses the problem of resilient innetwork consensus in the presence of misbehaving nodes. Secure and fault-tolerant consensus algorithms typically assume knowledge of nonlocal information; however, this assumption is not suitable for large-scale dynamic networks. To remedy this, we focus on local strategies that provide resilience to faults and compromised nodes. We design a consensus protocol based on local information that is resilient to worst-case security breaches, assuming the compromised nodes have full knowledge of the network and the intentions of the other nodes. We provide necessary and sufficient conditions for the normal nodes to reach asymptotic consensus despite the influence of the misbehaving nodes under different threat assumptions. We show that traditional metrics such as connectivity are not adequate to characterize the behavior of such algorithms, and develop a novel graph-theoretic property referred to as network robustness. Network robustness formalizes the notion of redundancy of direct information exchange between subsets of nodes in the network, and is a fundamental property for analyzing the behavior of certain distributed algorithms that use only local information.

Recently, many applications have arisen in distributed control that require consensus protocols. Concurrently, we have seen a proliferation of malicious attacks on large-scale distributed systems. Hence, there is a need for (i) consensus problems that take into consideration the presence of adversaries and specify correct behavior through appropriate conditions on agreement and safety, and (ii) algorithms for distributed control applications that solve such consensus problems resiliently despite breaches in security. This paper addresses these issues by (i) defining the adversarial asymptotic agreement problem, which requires that the uncompromised agents asymptotically align their states while satisfying an invariant condition in the presence of adversaries, and (ii) by designing a low complexity consensus protocol, the Adversarial Robust Consensus Protocol (ARC-P), which combines ideas from distributed computing and cooperative control. Two types of omniscient adversaries are considered: (i) Byzantine agents can convey different state trajectories to different neighbors in the network, and (ii) malicious agents must convey the same information to each neighbor. For each type of adversary, sufficient conditions are provided that ensure ARC-P guarantees the agreement and safety conditions in static and switching network topologies, whenever the number of adversaries in the network is bounded by a constant. The conservativeness of the conditions is examined, and the conditions are compared to results in the literature.

A practical approach for outdoors distributed target localization in wireless sensor networks

Abstract—In this paper, a distributed asynchronous al-gorithm for intelligent lighting is presented that minimizes collective power use while meeting multiple user lighting con-straints simultaneously and requires very little communication among agents participating in the distributed computation. Consequently, the approach is arbitrarily scalable, adapts to exogenous disturbances, and is robust to failures of individual agents. This algorithm is an example of a decentralized primal-space algorithm for constrained non-linear optimization that achieves coordination between agents using stigmergic memory cues present in the physical system as opposed to explicit communication and synchronization. Not only does this work make of stigmergy, a property first used to describe decentralized decision making in eusocial insects, but details of the algorithm are inspired by classic social foraging theory and more recent results in eusocial-insect macronutrient regulation. This theoretical analysis in this paper guarantees that the decentralized stigmergically coupled system converges to within a finite neighborhood of the optimal resource allocation. These results are validated using a hardware implementation of the algorithm in a small-scale intelligent lighting scenario. There are other real-time distributed resource allocation applications that are amenable to these methods, like distributed power generation; in general, this paper means to provide proof of concept that physical variables in cyberphysical systems can be leveraged to reduce the communication burden of algorithms. Keywords-Optimization algorithms, Resource allocation, Pareto optimality, Constrained optimization, Intelligent light-ing, Bioinspiration, Agents and autonomous systems, Decen-