In multiagent surveillance missions, a group of agents monitors some points of interest to provide situational awareness. For agents with local communication capabilities, one way to transmit the surveillance information to a base is streaming by the instantaneous data via multihop communications over a connected network. However, a connected communication network may become disconnected if some agents leave the surveillance area (for example, for refueling). This paper presents a locally applicable, efficient, and scalable strategy that guarantees a connected communication network between the base and the agents in the face of any agent removal. The proposed decentralized strategy is based on a sequence of local replacements, which are initiated by the agent leaving the network. It is shown that the replacement sequence always ends with the relocation of an agent, for which the absence from its current position does not disconnect the network. Furthermore, the optimality (that is, the minimum number of r...
Maintaining connectivity against the removal of multiple agents is a more challenging problem than maintaining connectivity against multiple edge removal @cite_9 . In the last few years, there has been a significant interest in addressing agent loss problem in networked systems. In @cite_9 and @cite_2 , the main focus is on the design of robust network topologies that can tolerate a finite number of agent removals. In @cite_2 and @cite_14 , the authors propose self-repair strategies that create new connections among the neighbors of the failing agent. In addition, a connectivity maintenance strategy based on decentralized estimation of algebraic connectivity is presented in @cite_7 . Based on their estimations, agents increase or decrease their broadcast radii for satisfying connectivity requirements.
@cite_9 @cite_14 @cite_7 @cite_2
2313205331 2093172757 2087097376 1590585877
Connectivity is a fundamental property in many networked systems for communication and coordination among the agents. Removal of any agent from a network may destroy its connectivity. In order to maintain the connectivity of mobile networked systems in agent loss, one can design a robust network topology such that the network is tolerant to a finite number of agent losses, and or develop a control strategy, such that the network reconfigures itself until the connectivity requirements are satisfied. In this paper, we introduce a decentralized control scheme based on a sequence of replacements, each of which occurs between an agent and one of its neighbors. The proposed scheme always maintains the graph connectivity, and it does not rely on any gathering of global information either a priori or at the intermediate steps of a mission. Moreover, the proposed scheme does not cause any increase in the total number of edges and the maximum node degree in a graph, thus it assures no increase in the overall communication cost due to the topological changes. In this study, we validate the control strategy by means of an analytical proof and simulation results. In this paper, we address the problem of agent loss in vehicle formations and sensor networks via two separate approaches: (1) perform a ‘self-repair’ operation in the event of agent loss to recover desirable information architecture properties or (2) introduce robustness into the information architecture a priori such that agent loss does not destroy desirable properties. We model the information architecture as a graph G(V, E), where V is a set of vertices representing the agents and E is a set of edges representing information flow amongst the agents. We focus on two properties of the graph called rigidity and global rigidity, which are required for formation shape maintenance and sensor network self-localization, respectively. For the self-repair approach, we show that while previous results permit local repair involving only neighbours of the lost agent, the repair cannot always be implemented using only local information. We present new results that can be applied to make the local repair using only local information. We describe implementation and illustrate with algorithms and examples. For the robustness approach, we investigate the structure of graphs with the property that rigidity or global rigidity is preserved after removing any single vertex (we call the property as 2-vertex-rigidity or 2-vertex-global-rigidity, respectively). Information architectures with such properties would allow formation shape maintenance or self-localization to be performed even in the event of agent failure. We review a characterization of a class of 2-vertex-rigidity and develop a separate class, making significant strides towards a complete characterization. We also present a characterization of a class of 2-vertex-global-rigidity. Copyright © 2008 John Wiley & Sons, Ltd. Consider a team of mobile software agents deployed to capture a (possibly hostile) intruder in a network. All agents, including the intruder move along the network links; the intruder could be arbitrarily fast, and aware of the positions of all the agents. The problem is to design the agents' strategy for capturing the intruder. The main efficiency parameter is the size of the team. This is an instance of the well known graph-searching problem whose many variants have been extensively studied in the literature. In all existing solutions, and in all the variants of the problem, it is assumed that agents can be removed from their current location and placed in another network site arbitrarily and at any time. As a consequence, the existing optimal strategies cannot be employed in situations for which agents cannot access the network at any point, or cannot "jump" across the network, or cannot reach an arbitrary point of the network via an internal travel through insecure zones. This motivates the contiguous search problem in which agents cannot be removed from the network, and clear links must form a connected sub-network at any time, providing safety of movements. This new problem is NP-complete in general. We study it for tree networks, and we consider its more general version, the weighted case, which arises naturally when considering networks whose nodes and links are of different nature and thus require a different number of agents to be explored. We give a linear-time algorithm that computes, for any tree @math , the minimum number of agents to capture the intruder, and the corresponding search strategy. Beside its optimality in time, our algorithm is naturally distributed: if @math is a processor-network, then the minimal search strategy for @math can be computed by @math in a decentralized manner, using a linear number of messages. Designing robust algorithms for mobile agents with reliable communication is difficult due to the distributed nature of computation, in mobile ad hoc networks (MANETs) the matter is exacerbated by the need to ensure connectivity. Existing distributed algorithms provide coordination but typically assume connectivity is ensured by other means. We present a connectivity service that encapsulates an arbitrary motion planner and can refine any plan to preserve connectivity (the graph of agents remains connected) and ensure progress (the agents advance towards their goal). The service is realized by a distributed algorithm that is modular in that it makes no assumptions of the motion-planning mechanism except the ability for an agent to query its position and intended goal position, local in that it uses 1-hop broadcast to communicate with nearby agents but doesn't need any network routing infrastructure, and oblivious in that it does not depend on previous computations. We prove the progress of the algorithm in one round is at least Ω(min(d, r)), where d is the minimum distance between an agent and its target and r is the communication radius. We characterize the worst case configuration and show that when d ≥ r this bound is tight and the algorithm is optimal, since no algorithm can guarantee greater progress. Finally we show all agents get Ɛ-close to their targets within O(D0 r+n2 Ɛ) rounds where n is the number of agents and D0 is the sum of the initial distances to the targets.