Globally, the number of falls among the elderly is rising, particularly among those 60 and older. An important contributing element to these falls is the fact that elderly people who live alone are not regularly supervised. A significant number of claims are filed for injuries caused by falls in the elderly, and at times these falls result in fatalities. Therefore, well-founded, and practical e-health technologies are critical for elder care, particularly for individuals who live alone. One of the emerging and rapid-growing technologies like Artificial Intelligence would be an excellent companion for them to continuously monitor their health condition and prevent falls. This review paper compares various research, surveys, studies, and experiments conducted on elderly fall prevention utilizing Artificial Intelligence and other technologies such as Internet of Things (IoT), Sensor, Radio Detection and Ranging (RADAR), Infrared Radiation (IR) and Hardware technologies. It has been identified that in real time and long-term monitoring without human intervention, AI-IoT technology will be the best solution for fall prevention in older adults.

Unmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

In the era of digital advancement, the integration of Deep Learning (DL) algorithms is revolutionizing performance monitoring in football. Due to restrictions on monitoring devices during games to prevent unfair advantages, coaches are tasked to analyze players’ movements and performance visually. As a result, Computer Vision (CV) technology has emerged as a vital non-contact tool for performance analysis, offering numerous opportunities to enhance the clarity, accuracy, and intelligence of sports event observations. However, existing CV studies in football face critical challenges, including low-resolution imagery of distant players and balls, severe occlusion in crowded scenes, motion blur during rapid movements, and the lack of large-scale annotated datasets tailored for dynamic football scenarios. This review paper fills this gap by comprehensively analyzing advancements in CV, particularly in four key areas: player/ball detection and tracking, motion prediction, tactical analysis, and event detection in football. By exploring these areas, this review offers valuable insights for future research on using CV technology to improve sports performance. Future directions should prioritize super-resolution techniques to enhance video quality and improve small-object detection performance, collaborative efforts to build diverse and richly annotated datasets, and the integration of contextual game information (e.g., score differentials and time remaining) to improve predictive models. The in-depth analysis of current State-Of-The-Art (SOTA) CV techniques provides researchers with a detailed reference to further develop robust and intelligent CV systems in football.

Vehicular network structures present a range of challenges and opportunities for efficiently managing awareness of road dynamics and network connectivity. An enhanced manageable organization can offer a better reaction to safety-related road events, facilitate dynamic topological flexibility, relate to road layout, and interact with unpredictable distribution of the vehicles. Vehicular grouping is one of the suggested structural techniques that offers a great benefit in grouping vehicles and modelling data routing, giving importance to road structure and the occurrence of a dynamic event within the associated group of vehicles. The approach discussed in this paper is based on a dynamic grouping through phases of self-formation, self-joining, self-leaving and self-healing as key components of the protocol operational cycle. Both vehicular physical connected resources and the remote computational cloud could be used for data processing and monitoring of road dynamics. This, in effect, encourages an Internet of Things (IoT) environment that enhances the dynamic performance through direct interaction between the virtualized network of vehicles and the physical network on the road leading to Internet of Vehicles (IoV). The objective of this paper is to develop a concept of network self-formation algorithm based on vehicle grouping strategy wherein the node can flexibly switch its function, be it an IoT gateway or a router node, based on the proposed fitness election model to be elected as group head. Testing using Contiki-Cooja simulator has been implemented on various road condition scenarios reflects the operational ability of the algorithm taking into consideration the network performance based on the ultimate capacity of the road.

The topological structure in vehicular communication networks presents challenges for sustaining network connectivity on the road. Highway dynamics, for example, encourage the need for an adaptive and flexible structure to handle the rapid events of vehicles joining and leaving the road. Such demand aligns with the advancement made in software-defined networks and related dynamic network re-orchestration. This paper discusses the development of a virtual model that represents the operation of an autonomous vehicular network. It also investigates the ability to re-orchestrate the topology through software definition while running the various operational phases. Network self-formation, network expansion, retraction via vehicular members joining and leaving, and network self-healing when a topological rupture occurs as a result of a key member leaving the network are the key grouping phases. The communication approach is analyzed based on the status of network members and their ability to assume the various network roles. The concept is tested using both a Contiki–Cooja network simulator and a MATLAB analytical modeling tool to reflect the operation and performance of the grouping approach under various road scenarios. The outcome of the analysis reflects the ability of the group to be formulated within a measured latency considering the various network parameters such as communication message rate. The approach offers tools for managing the dynamic connectivity of vehicular groups and may also be extended to assume the function of an on-road network digital twin during the lifetime of a given group.