Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agentbased control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

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Summary The leader node in a distributed computing system is responsible to establish coordination among all other nodes that are situated apart geographically. Selection of a suitable leader is one of the major and challenging problems. In this paper, a novel leader election algorithm is proposed based on resources of each node in a ring network. All the nodes compute resource strength values by considering available resources like CPU, memory capacity, and residual energy. A node with the highest resource strength over the network is elected as the leader. The proposed algorithm has also considered sudden failure of the nodes particularly when it is the leader node. Moreover, addition of new nodes is also considered. The proposed algorithm shows improvement on message complexity over the network and resource‐based priority generation, which helps in efficient election of the leader. To validate, the proposed algorithm is extensively simulated as well as real‐life hardware experiment is also done. In the experiment, 2 cases are considered with different weight of the resources, and consequent effects are shown. The results are also compared with the existing algorithms, and it is observed that the proposed work comparably performs better that the existing related algorithms.

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