Proactive Blockage Prediction for UAV assisted Handover in Future Wireless Network

The future wireless communication applications demand seamless connectivity, higher throughput, and low latency, for which the millimeter-wave (mmWave) band is considered a potential technology. Nevertheless, line-of-sight (LoS) is often mandatory for mmWave band communication, and it renders these waves sensitive to sudden changes in the environment. Therefore, it is necessary to maintain the LoS link for a reliable connection. One such technique to maintain LoS is using proactive handover (HO). However, proactive HO is challenging, requiring continuous information about the surrounding wireless network to anticipate potential blockage. This paper presents a proactive blockage prediction mechanism where an unmanned aerial vehicle (UAV) is used as the base station for HO. The proposed scheme uses computer vision (CV) to obtain potential blocking objects, user speed, and location. To assess the effectiveness of the proposed scheme, the system is evaluated using a publicly available dataset for blockage prediction. The study integrates scenarios from Vision-based Wireless (ViWi) and UAV channel modeling, generating wireless data samples relevant to UAVs. The antenna modeling on the UAV end incorporates a polarization-matched scenario to optimize signal reception. The results demonstrate that UAV-assisted Handover not only ensures seamless connectivity but also enhances overall network performance by 20%. This research contributes to the advancement of proactive blockage mitigation strategies in wireless networks, showcasing the potential of UAVs as dynamic and adaptable base stations.

Machine learning for accelerating the discovery of high performance low-cost solar cells: a systematic review

Solar photovoltaic (PV) technology has merged as an efficient and versatile method for converting the Sun's vast energy into electricity. Innovation in developing new materials and solar cell architectures is required to ensure lightweight, portable, and flexible miniaturized electronic devices operate for long periods with reduced battery demand. Recent advances in biomedical implantable and wearable devices have coincided with a growing interest in efficient energy-harvesting solutions. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques are touted as game changers in energy harvesting, especially in solar energy materials. In this article, we systematically review a range of ML techniques for optimizing the performance of low-cost solar cells for miniaturized electronic devices. Our systematic review reveals that these ML techniques can expedite the discovery of new solar cell materials and architectures. In particular, this review covers a broad range of ML techniques targeted at producing low-cost solar cells. Moreover, we present a new method of classifying the literature according to data synthesis, ML algorithms, optimization, and fabrication process. In addition, our review reveals that the Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) enables the design of the most promising low-solar cell architecture. Therefore, our review is a critical evaluation of existing ML techniques and is presented to guide researchers in discovering the next generation of low-cost solar cells using ML techniques.

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm, and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multi-objective optimisation criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.