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The security of key and critical infrastructures is crucial for uninterrupted industrial process flow needed in strategic management as these facilities are major targets of invaders. The emergence of non-military use of drones especially for logistics comes with the challenge of redefining the anti-drone approach in determining a drone’s harmful status in the airspace based on certain metrics before countering it. In this work, a vision-based multi-tasking anti-drone framework is proposed to detect drones, identifies the airborne objects, determines its harmful status through perceived threat analysis, and checks its proximity in real-time prior to taking an action. The model is validated using manually generated 5460 drone samples from six (6) drone models under sunny, cloudy, and evening scenarios and 1709 airborne objects samples of seven (7) classes under different environments, scenarios (blur, scales, low illumination), and heights. The proposed model was compared with seven (7) other object detection models in terms of accuracy, sensitivity, F1-score, latency, throughput, reliability, and efficiency. The simulation result reveals that, overall, the proposed model achieved superior multi-drone detection accuracy of 99.6%, attached object identification of sensitivity of 99.80%, and F1-score of 99.69%, with minimal error, low latency, and less computational complexity needed for effective industrial facility aerial surveillance. A benchmark dataset is also provided for subsequent performance evaluation of other object detection models.

The future of healthcare relies heavily on the connection of humans to intelligent devices via communication networks for rapid medical response. Hence, the evaluation of the performance of smart wearable devices as veritable tools for prompt, pervasive, and proactive healthcare delivery to end-users in response to socio-economic dynamics is imperative especially as 5G unwinds and B5G emerges. Despite the boom in the wearable market and significant improvement in communication technologies, the translation of wearable data from clinical trials to valuable assets for practical medical application is burdened with varying challenges. This review provides an introspective analysis of the performance of unobtrusive wearable devices based on identified key performance indicators (KPIs) in relation to evolving generation networks in achieving innovative health care delivery. A total of 2751 articles pooled from 5 digital libraries were screened and 16 were selected for this review using PRISMA. The identified E-DISC wearable KPIs; energy efficiency, discretization, intelligence, secured network, and customizable standards are currently engrossed with both reliability and real-time issues that undermine its performance, perceptibility, and acceptability by end-users. The transformation of smart wearable devices' data from clinical trials into intangible resources for medical application is the fulcrum of innovative healthcare actualization. Further insight on how the identified challenges can be streamlined for smooth device alignment and transition to the emerging B5G network and its eco-friendly environment is also discussed. It is hoped that this will serve as a rallying point for research direction in translating prospective wearable solutions into a valuable resource for actualizing p-health.

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