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The recent advances in unmanned aerial vehicles (UAVs) enormously improve their utility and expand their application scope. The UAV and swarm implementation further prevail in Smart City practices with the aid of edge computing and urban Internet of Things. The lead–follow formation in UAV swarm is an important organization means and has been adopted in diverse exercises, for its efficiency and ease of control. However, the reliability of centralization makes the entire swarm system in risk of collapse and instability, if a fatal fault incident happens in the leader. The motivation is to build a mechanism helping the distributed swarm recover from possible failures. Existing ways include assigning definite backups, temporary clustering and traversing to select a new leader are traditional ways that lack flexibility and adaptability. In this article, we propose a voting-based leader election scheme inspired by the Raft method in distributed computation consensus to solve the problem. We further discuss the impact of communication conditions imposed on the decentralized voting process by implementing a network resource pool. To dynamically evaluate UAV individuals, we outline measurement design principles and provide a realizable calculation example. Lastly but not least, empirical simulation results manifest better performance than the Raft-based method. Our voting-based approach exhibits advantages and is a promising way for quick regrouping and fault recovery in lead–follow swarms. [ABSTRACT FROM AUTHOR]

The application of the Internet of Things has produced large amounts of data in different scenarios, which are accompanied with problems, such as consistency and integrity violations. Existing research on dealing with data availability violations is insufficient. In this work, the detection and repair of data availability violations (DRAV) framework is proposed to detect and repair data violations in Internet of Things with a distributed parallel computing environment. DRAV uses algorithms in the MapReduce programming framework, and these include detection and repair algorithms based on enhanced conditional function dependency for data consistency violation, MapJoin, and ReduceJoin algorithms based on master data for k -nearest neighbor–based integrity violation detection, and repair algorithms. Experiments are conducted to determine the effect of the algorithms. Results show that DRAV improves data availability in Internet of Things compared with existing methods by detecting and repairing violations. [ABSTRACT FROM AUTHOR]