Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI.
Clustering is the key for energy constrained wireless sensor networks (WSNs). Energy optimization and communication reliability are the most important consideration in designing efficient clustered WSN. In lossy environment, channel coding is mandatory to ensure reliable and efficient communication. This reliability is compromised by additional energy of coding and decoding in cluster heads. In this paper, we investigated the trade-off between reliability and energy efficiency and proposed adaptive FEC/FWD and FEC/ARQ coding frameworks for clustered WSNs. The proposed schemes consider channel condition and inter-node distance to decide the adequate channel coding usage. Simulation results show that both the proposed frameworks are energy efficient compared to ARQ schemes and FEC schemes, and suitable to prolong the clustered network lifespan as well as improve the reliability.
In this paper, we have proposed a hybrid adaptive coding and decoding scheme for multi-hop wireless sensor networks (WSNs). Energy consumption and transmission reliability are used as performance metrics for multi-hop communications in WSNs. The presented scheme takes into account distance, channel conditions and correction codes performance to decide coding and decoding procedure, and considers Reed Solomon (RS) code and Low Density Parity Check (LDPC) code to provide error protection on the transmitted data. The proposed approach aims to reduce the decoding power consumption and to prolong the lifetime of the network as well as improve the reliability of the transmission. Simulation results show that our performed scheme enhances both energy efficiency and communication reliability of multi-hop sensor networks.
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