Taking Cooperative Decisions in Group-Based Wireless Sensor Networks

Several studies have demonstrated that communications are more efficient when cooperative group-based architectures are used in wireless sensor networks (WSN). This type of architecture allows increasing sensor nodes' lifetime by decreasing the number of messages in network. But, the main gap is to know how to take cooperative decisions in order to make the right communication. In this paper, we analyze the main aspects related to collaborative decisions in WSNs. A mathematical analysis will be presented in order to take the correct decision. Finally, the simulations will show the efficiency of the method used to make cooperative decisions in WSNs. © 2011 Springer-Verlag.García Pineda, M.; Lloret, J.; Sendra Compte, S.; Rodrigues, JJPC. (2011). Taking Cooperative Decisions in Group-Based Wireless Sensor Networks. En Lecture Notes in Computer Science. Springer Verlag (Germany). 61-65. doi:10.1007/978-3-642-23734-8_9S6165Garcia, M., Bri, D., Sendra, S., Lloret, J.: Practical Deployments of Wireless Sensor Networks: a Survey. Int. Journal on Advances in Networks and Services 3(3-4), 170–185 (2010)Lloret, J., Garcia, M., Tomas, J.: Improving Mobile and Ad-hoc Networks performance using Group-Based Topologies. In: Wireless Sensor and Actor Networks II. IFIP, vol. 264, pp. 209–220 (2008)Garcia, M., Lloret, J.: A Cooperative Group-Based Sensor Network for Environmental Monitoring. In: Luo, Y. (ed.) CDVE 2009. LNCS, vol. 5738, pp. 276–279. Springer, Heidelberg (2009)Garcia, M., Sendra, S., Lloret, J., Lacuesta, R.: Saving Energy with Cooperative Group-Based Wireless Sensor Networks. In: Luo, Y. (ed.) CDVE 2010. LNCS, vol. 6240, pp. 73–76. Springer, Heidelberg (2010)Parsa, S., Parand, F.-A.: Cooperative decision making in a knowledge grid environment. Future Generation Computer Systems 23, 932–938 (2007)Soubie, J.-L., Zaraté, P.: Distributed Decision Making: A Proposal of Support Through Cooperative Systems. J. Group Decisions and Negotiation 14(2), 147–158 (2005)Kraemer, K.L., King, J.L.: Computer-based systems for cooperative work and group decision making. ACM Computer Survey 20(2), 115–146 (1988)Kernan, J.B.: Choice Criteria, Decision Behavior, and Personality. Journal of Marketing Research 5(2), 155–164 (1968

Run-Time Selection of Coordination Mechanisms in Multi-Agent Systems

This paper presents a framework that enables autonomous agents to dynamically select the mechanism they employ in order to coordinate their inter-related activities. Adopting this framework means coordination mechanisms move from the realm of being imposed upon the system at design time, to something that the agents select at run-time in order to fit their prevailing circumstances and their current coordination needs. Empirical analysis is used to evaluate the effect of various design alternatives for the agent's decision making mechanisms and for the coordination mechanisms themselves

E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Task-Based Information Compression for Multi-Agent Communication Problems with Channel Rate Constraints

A collaborative task is assigned to a multiagent system (MAS) in which agents are allowed to communicate. The MAS runs over an underlying Markov decision process and its task is to maximize the averaged sum of discounted one-stage rewards. Although knowing the global state of the environment is necessary for the optimal action selection of the MAS, agents are limited to individual observations. The inter-agent communication can tackle the issue of local observability, however, the limited rate of the inter-agent communication prevents the agent from acquiring the precise global state information. To overcome this challenge, agents need to communicate their observations in a compact way such that the MAS compromises the minimum possible sum of rewards. We show that this problem is equivalent to a form of rate-distortion problem which we call the task-based information compression. We introduce a scheme for task-based information compression titled State aggregation for information compression (SAIC), for which a state aggregation algorithm is analytically designed. The SAIC is shown to be capable of achieving near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a rendezvous problem and its performance is compared with several benchmarks. Numerical experiments confirm the superiority of the proposed algorithm.Comment: 13 pages, 9 figure