In the era of the Internet of Things (IoT), smart connected devices have the ability to generate data that could be of interest to the public. This paves the way for an emerging market for monetized data exchanges, where IoT device owners can sell access to live data generated by their connected devices to interested users. Implementing a trusted, cost-efficient, automatic monetization solution of IoT data can be a challenging problem and usually involves intermediaries and centralised governance and management. Blockchain and smart contracts introduce a secure and trusted platform to carry out transactions in a highly trusted, secure, decentralised manner. In this study, they present a blockchain-solution and implementation using Ethereum smart contracts for monetizing IoT data with automated payment involving no intermediary. The authors discuss key aspects related to architectural design, entity relations, interactions among participants, logic flow, implementation and testing of the overall system functionality.
With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.
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