A Survey on Sensor Networks from a Multiagent Perspective

Sensor networks (SNs) have arisen as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical systems ease the development and proliferation of this kind of networks in a wide range of actual-world applications. Multiagent systems (MAS) have been identified as one of the most suitable technologies to contribute to the deployment of SNs that exhibit flexibility, robustness and autonomy. The purpose of this survey is 2-fold. On the one hand, we review the most relevant contributions of agent technologies to this emerging application domain. On the other hand, we identify the challenges that researchers must address to establish MAS as the key enabling technology for SNs.This work has been funded by projects IEA(TIN2006-15662-C02-01), Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010), EVE (TIN2009-14702-C02-01,TIN2009-14702-C02-02) and Generalitat de Catalunya under the gran t2009-SGR-1434. Meritxell Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe

Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law

In this paper we propose a novel message-passing algorithm, the so-called Action-GDL, as an extension to the generalized distributive law (GDL) to ef¿ciently solve DCOPs. Action-GDL provides a unifying perspective of several dynamic programming DCOP algorithms that are based on GDL, such as DPOP and DCPOP algorithms. We empirically show how Action-GDL using a novel distributed post-processing heuristic can outperform DCPOP, and by extension DPOP, even when the latter uses the best arrangement provided by multiple state-of-the-art heuristics.Work funded by IEA (TIN2006-15662-C02-01), AT (CONSOLIDER CSD2007-0022, INGENIO 2010) and EVE (TIN2009-14702-C02-01 and 02). Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe

Social Value Propagation for Supply Chain Formation

Supply Chain Formation is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized supply chain formation appears as a highly intricate task because agents only possess local information, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. State-of-the-art decentralized supply chain formation approaches can either: (i) #12;find supply chains of high value at the expense of high resources usage; or (ii) fi#12;nd supply chains of low value with low resources usage. This work presents chainme, a novel decentralized supply chain formation algorithm. Our results show that chainme fi#12;nds supply chains with higher value than state-of-the-art decentralized algorithms whilst decreasing the amount of resources required from one up to four orders of magnitude.Peer Reviewe

Decentralised Parallel Machine Scheduling for Multi-Agent Task Allocation

Abstract. Multi-agent task allocation problems pervade a wide range of real-world applications, such as search and rescue in disaster management, or grid computing. In these applications, where agents are given tasks to perform in parallel, it is often the case that the performance of all agents is judged based on the time taken by the slowest agent to complete its tasks. Hence, efficient distribution of tasks across heterogeneous agents is important to ensure a short completion time. An equivalent problem to this can be found in operations research, and is known as scheduling jobs on unrelated parallel machines (also known as R||Cmax). In this paper, we draw parallels between unrelated parallel machine scheduling and multi-agent task allocation problems, and, in so doing, we present the decentralised task distribution algorithm (DTDA), the first decentralised solution to R||Cmax. Empirical evaluation of the DTDA is shown to generate solutions within 86-97% of the optimal on sparse graphs, in the best case, whilst providing a very good estimate (within 1%) of the global solution at each agent

Plataforma per a la simulació de xarxes de sensors

Aquest projecte descriu una plataforma de simulació per a xarxes de sensors des de la perspectiva dels sistemes multi-agents. La plataforma s'ha dissenyat per facilitar la simulació de diferents aplicacions concretes de xarxes de sensors. A més, s'ha entregat com a artefacte del projecte IEA (Institucions Electròniques Autònomes, TIN2006-15662-C02-0) de l'IIIACSIC. Dins l'entorn de l'IEA, aquesta és l'eina que aporta les capacitats de simulació per donar suport al disseny d'algorismes adaptatius per a xarxes de sensors.Este proyecto describe una plataforma de simulación para redes de sensores desde la perspectiva de los sistemas multi-agentes. La plataforma se ha diseñado para facilitar la simulación de diferentes aplicaciones concretas de redes de sensores. Además, se ha entregado como artefacto del proyecto EA (Institucions Electròniques Autònomes, TIN2006-15662-C02-0) del IIIA-CSIC. Dentro del entorno del IEA, ésta es la herramienta que aporta las capacidades de simulación para dar soporte al diseño de algoritmos daptativos para redes de sensores.This project describes a multi-agent based simulation framework for sensor networks. It has been conceived to ease the simulation of a wide range of sensor network applications. Furthermore, the framework has been delivered s a result of the ongoing IEA (Institucions Electròniques Autònomes, IN2006-15662-C02-0) project at IIIA-CSIC. In the realm of IEA, this is the tool that provides simulation facilities to support the design of self-organising algorithms for sensor networks.Nota: Aquest document conté originàriament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia

Putting residential flexibility management into action with pilot sites in Europe: From Mas2tering to DRIvE projects

The Mas2tering and DRIvE European projects develop a software platform to manage the residential and tertiary energy flexibility in local communities of prosumers. This platform includes forecasting, optimization, cybersecurity and fast-response capabilities modules. Business models and use cases were developed, taking into account the regulatory challenges. The platform will be implemented in five pilot sites across three countries

Distributed Constraint Handling and Optimization

Constraints pervade our everyday lives and are usually perceived as elements that limit solutions to the problems that we face (e.g., the choices we make everyday are typically constrained by limited money or time). However, from a computational point of view, constraints are key components for efficiently solving hard problems. In fact, constraints encode knowledge about the problem at hand, and so restrict the space of possible solutions that must be considered. By doing so, they greatly reduce the computational effort required to solve a problem. Here we will focus on how constraint processing can be used to address optimization problems in Multi-Agent Systems. Specifically, we will consider Dis tributed Constraint Optimization Problems (DCOPs) where a set of agents must come to some agreement, typically via some form of negotiation, about which action each agent should take in order to jointly obtain the best solution for the whole system. In more detail, this chapter aims to provide the reader with a broad knowledge of the main DCOP solution approaches describing both exact algorithms, approximate approaches and quality guarantees that can be provided in the DCOP framework

Efficient buyer groups for prediction-of-use electricity tariffs

Copyright © 2014, Association for the Advancement of Artificial Intelligence.Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customers consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work (Vinyals et al. 2014) studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK

Scalable multi-agent local energy trading — Meeting regulatory compliance and validation in the Cardiff grid

International audienceWith the recent approval of energy directives (e.g. the EU Clean Energy Package) that state the rights and obligations of Local Energy Communities (LECs), time had come for smart grid technologies to show that they can comply with the complexity of the new regulatory environment when optimising LEC energy exchanges. This paper meets this challenge by modelling LECs operation by means of a novel energy coordination network that satisfies all stated requirements, such as the right of each community member of selecting their own supplier and the subsequent need of independent metering. The optimisation of the community is decentralised among a set of autonomous computational entities (a.k.a. agents), achieving scalability, and orchestrated via an agent interaction protocol based on the well-known Alternative Direction Method of Multipliers (ADMM) that keeps the preferences and cost structures of prosumers private. The approach is validated via extensive simulations using a dataset based on real data related to an existing energy grid in Cardiff (UK). Empirical results show that by optimising the use of prosumers' flexible resources to maximise the share of PV self-generated energy at community level our approach achieves higher community self-consumption ratios (up to 59% increment) and significantly reduces prosumer energy bills (589£/month additional reduction expected on summer months) w.r.t. optimising houses individually