Wireless Body Area Network is an emerging technology that is used primarily in the area of healthcare applications. It is a low-cost network having the capability of transportability and adaptability. It can be used in location independent and long-term remote monitoring of people without disturbing their daily activities. In a typical WBAN system, sensing devices are either implanted or etched into the human body that continuously monitors his physiological parameters or vital signs. In such a network, trusts among the stakeholders (healthcare providers, users, and medical staff, etc.) are found of high importance and regarded as the critical success factor for the reliability of information exchange among them. In remote patient monitoring, the implementation of trust and privacy preservation is crucial, as vital parameters are being communicated to remote locations. Nonetheless, its widespread use, WBAN, has severe trust and privacy risks, limiting its adaptation in healthcare applications. To address trust and privacy-related issues, reliable communication solutions are widely used in WBANs. Given the motivation, in this paper, we have proposed a trust-based communication scheme to ensure the reliability and privacy of WBAN. To ensure reliability, a cooperative communication approach is used, while for privacy preservation, a cryptography mechanism is used. The performance of the proposed scheme is evaluated using MATLAB simulator. The output results demonstrated that the proposed scheme increases service delivery ratio, reliability, and trust with reduced average delay. Furthermore, a fuzzy-logic method used for ranking benchmark schemes, that has been concluded that the proposed scheme has on top using comparative performance ranking.

A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant's hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6% with 70% of training (33488 samples), 30% of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

Reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) are anticipated as talented technologies to extend the range of millimeter wave (mmWave) communications. In this letter, a UAV equipped with RIS (UAV-RIS) is used to assist mmWave base station (BS) in covering users in hotspot areas. In this context, UAV should cover several high-capacity hotspots while minimizing its flying/hovering energy consumptions. Energy-aware multi-armed bandit (EA-MAB) algorithm is proposed as an effective online learning tool to handle this problem efficiently. By which, the UAV acts as the player trying to maximize its achievable rate, i.e., the reward, over selecting different hotspots in its trajectory, i.e., the arms of the bandit game. This is done while minimizing the energy/cost of the UAV flight from one hotspot to another over the time span of its battery life. Numerical analysis confirms the superior performance of the proposed EA-MAB algorithm over benchmarks.

In this letter, a novel millimeter wave (mmWave) multi-level beamforming (BF) is proposed. User positioning is used for roughly defining the area within which the mobile station (MS) is properly located. Based on this, a multi-level beam search is conducted using compressive sensing (CS) based channel estimation to find out the transmit (TX)/receive (RX) beams for establishing the mmWave link. The estimated MS location-uncertainty area is used to determine the number of beams along with the beamwidth required for constructing the sensing matrix used in each beam searching level. Mathematical and simulation analysis confirm the superiority of the proposed BF scheme over the conventional ones in both BF complexity and performance.

In this paper, the problem of joint unmanned aerial vehicle (UAV) trajectory planning and low-orbit satellites (LEO-Sats) selection in space-air-ground integrated networks (SAGIN) will be investigated. This problem is of utmost importance when SAGIN is exploited for post-disaster relief services, where ground base stations (GBSs) within the post-disaster area are completely damaged or malfunctioned. In this scenario, UAV will provide wireless connectivity for the victims, while LEO-Sats will relay the UAV data to the nearest survival GBS. UAV trajectory should be optimized to maximize the collected data from the victims subject to its limited battery capacity, while it should jointly select the best LEO-Sat in each visited location within its optimized trajectory. The selected LEO-Sat should maximize UAV’s achievable data rate while maintaining a long remining visible time to minimize frequent LEO-Sats’ handovers. In this paper, an online learning approach using multi-armed bandit (MAB) models will be proposed to address this highly dynamic problem. As LEO-Sat selection should be performed after UAV arrives at a dedicated location in its optimized trajectory, the problem is divided into two MAB stages. In the first stage, the battery constraint UAV trajectory optimization is modeled as budget constraint MAB (BC-MAB) game using BC-upper confidence bound (BC-UCB) algorithm. In the second stage, LEO-Sat selection in each visited location is modeled as contextual MAB with variable arms (CMAB-VA) game using LinUCB-VA algorithm. Numerical analysis confirms the superior performance of the proposed approach over candidate benchmarks.

Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV’s flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints.