Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT.

The passengers traveling in aircrafts demand for internet connectivity to effectively utilize their precious inflight time. Provision of internet services to airborne users at the same cost and of the same speed, it is being provided to the home users, is a critical challenge for the research community. In this regard, various solutions have been recently proposed in the literature, which involve satellite based solutions, direct Ground Station (GS) link based solutions, and mesh network based solutions etc. In this paper, inspired from land-mobile radio cellular networks, a new architecture for Airborne Internet Access (AIA) is proposed. Computer simulations are performed to evaluate the proposed architecture, while various routing algorithms are implemented and a thorough performance evaluation is conducted. Mean End-to-End (E2E) packet delay is used as performance metric in the presented analysis. Routing algorithms from two diverse classes of routing algorithms are selected for the analysis, viz: topology based and position based routing algorithms. In a scenario, when transmit message length is 1000 bytes, transmitter bit rate is 10 Mbps, communication range of nodes restricted to 10 km, velocity of aircrafts' mobility up to 250 m/s, and direction of motion of the aircrafts drawn from uniform distribution; the Ad-hoc On-demand Distance Vector Routing (AODV) algorithm is observed to outperform the Greedy Perimeter Stateless Routing (GPSR) algorithm in terms of mean E2E delay. Moreover, effect of various network and physical parameters on the network performance is observed and various useful conclusions are drawn. The conducted analysis is useful in selection of appropriate routing algorithm for appropriate network conditions.

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.