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Internet‐of‐things is an enabling technology in the fourth‐generation industrial revolution. The freshness of the data sent by a sensor node (SN) is an important parameter in the Internet‐of‐things, unlike the throughput in cellular communications. A relatively new performance metric named age of information (AoI) is used in this paper to quantify the freshness of the data. The SN, located in the transport infrastructure, harvests energy from radio frequency signals transmitted by the FD‐UAV. This is used to transmit real‐time sensor observations to the data sink via FD‐UAV. The SN generates an update after replenishing the battery and transmits it by using the harvested energy. A closed‐form expression for average AoI is derived as a function of time allocated for energy harvesting. The optimal time allocation for energy harvesting that maximizes the freshness of data update is identified. A deep learning technique namely long‐short term memory is used to predict the average AoI. Simulation results demonstrate the usefulness of the performance bounds in terms of the freshness of data updates. [ABSTRACT FROM AUTHOR]

In this study, the authors present an energy-efficient technique for the fog-networks, where a node can communicate to other users using limited network infrastructure support. The authors' contribution is central to node mobility and lower energy consumption rates. They propose an energy-efficient cellular communication model for fog-networks. To establish the connection between the source and the destination node, the next generation involves node base station imports required information from nearby fog nodes and performs the network management with the help of data analytic units. A system-level optimisation for the energy consumption model is also proposed, which supports a high degree of node mobility. They compared their results with 4G/LTE and observed that the proposed work provides the least energy consumption as compared to the legacy of the networks. [ABSTRACT FROM AUTHOR]