8 research outputs found

    Decentralized and stable matching in Peer-to-Peer energy trading

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    In peer-to-peer (P2P) energy trading, a secured infrastructure is required to manage trade and record monetary transactions. A central server/authority can be used for this. But there is a risk of central authority influencing the energy price. So blockchain technology is being preferred as a secured infrastructure in P2P trading. Blockchain provides a distributed repository along with smart contracts for trade management. This reduces the influence of central authority in trading. However, these blockchain-based systems still rely on a central authority to pair/match sellers with consumers for trading energy. The central authority can interfere with the matching process to profit a selected set of users. Further, a centralized authority also charges for its services, thereby increasing the cost of energy. We propose two distributed mechanisms to match sellers with consumers. The first mechanism doesn't allow for price negotiations between sellers and consumers, whereas the second does. We also calculate the time complexity and the stability of the matching process for both mechanisms. Using simulation, we compare the influence of centralized control and energy prices between the proposed and the existing mechanisms. The overall work strives to promote the free market and reduce energy prices

    Group Formation Among Peer-to-Peer Agents: Learning Group Characteristics

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    This paper examines the decentralized formation of groups within a peer-to-peer multi-agent system. More specifically, it frames group formation as a clustering problem, and examines how to determine cluster characteristics such as area and density in the absence of information about the entire data set, such as the number of points, the number of clusters, or the maximum distance between points, that are available to centralized clustering algorithms. We develop a method in which agents individually search for other agents with similar characteristics in a peer-to-peer manner. These agents group into small centrally controlled clusters which learn cluster parameters by examining and improving their internal composition over time. We show through simulation that this method allows us to find clusters of a wide variety of sizes without adjusting agent parameters

    A Decentralized Latency-Aware Task Allocation and Group Formation Approach with Fault Tolerance for IoT Applications

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    © 2013 IEEE. Development of internet of things (IoT) and smart devices eased life by offering numerous applications targeting to provide real-time low latency services, but they also brought challenges in handling huge data generated from the powerful computations, to get a job done. Decentralized edge computing could help to achieve latency requirements of the applications by executing them closer to the user at edge of network, but most of the current studies actually deployed centralized approaches for cluster computing at edge, which put extra overhead of cluster formation and management. In this article, we propose to group heterogeneous edge nodes on task arrival with a more decentralized method and execute tasks in parallel to meet their deadline. On the other hand, to guarantee successful execution of critical IoT application running in an edge network, fault tolerance has to be significantly considered. For resource limited edge devices, there is a great need for efficient fault tolerance techniques, which can provide reliability based on device's local information, without worrying about overall network topology. In this article, our novel method is to increase task reliability being executed in distributed edge computing environment through finding reliability of an edge node locally, and by providing fault tolerance to increase overall application availability. Our proposed fault tolerance technique works in decentralized mode by executing new algorithms to handle above mentioned problems. Our experiment results show that our approach is effective as well as providing desired goals of achieving deadline for latency-aware IoT applications, with staggering decrease in overall network traffic along with ensuring reliability and availability

    Decentralized group formation

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    Imagine a network of entities, being it replica servers aiming to minimize the probability of data loss, players of online team-based games and tournaments, or companies that look into co-branding opportunities. The objective of each entity in any of these scenarios is to find a few suitable partners to help them achieve a shared goal: replication of the data for fault tolerance, winning the game, successful marketing campaign. All information attainable by the entities is limited to the profiles of other entities that can be used to assess the pairwise fitness. How can they create teams without help of any centralized component and without going into each other’s way? We propose a decentralized algorithm that helps nodes in the network to form groups of a specific size. The protocol works by finding an approximation of a weighted k-clique matching of the underlying graph of assessments. We discuss the basic version of the protocol, and explain how dissemination via gossiping helps in improving its scalability. We evaluate its performance through extensive simulations

    Traditional dwellings and settlements review : TDSR ; journal of the International Association for the Study of Traditional Environments

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    In this paper, a decentralized group formation algorithm for task allocation in complex adaptive systems is proposed. Compared with current related works, this decentralized algorithm takes system architectures into account and allows not only neighboring agents but also indirect linked agents in the system to help with a task. A system adaptation strategy is also developed for discovering effective system structures for task allocation. Moreover, a set of experiments was conducted to demonstrate the efficiency of our methods
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