The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and employs machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and power consumption. The performance evaluations yield that the proposed Mobi-IoST framework has approximately 93% accuracy and reduced the delay and power by approximately 23-26% and 37-41% respectively than the existing mobility-aware task delegation system.

Recent advancements in artificial intelligence (AI) and IoT technology have revolutionized the healthcare industry by providing effective remote healthcare. Furthermore, with the aging of the world’s population, remote health monitoring and recommendations are becoming imperative to provide cost-effective healthcare solutions for improving the quality of life of our senior citizens. The explosive growth of wearable sensors (IoT sensors) and health bands has facilitated the interconnection among patients and caregivers to enable assisted living by leveraging AI techniques. This work proposes an end-to-end connected smart home healthcare system (FEEL) for elderly people. Our proposed framework addresses the main challenges of the Internet of Medical Things (IoMT) system namely, the scarcity of labeled data and user’s diverse needs. The major contributions of the work are: 1) few-shot learning-enabled novel federated learning (FL) framework for health data and context information analysis and recommendation; 2) user and context-based knowledge graph (UKG) to represent and model health parameters and environmental impacts on recommendations; 3) deep learning architecture for activity monitoring and location estimation of the users; and 4) edge-fog-IoMT collaborative framework to collect, store, and share medical recommendations while protecting the privacy of the users. FEEL is specifically beneficial for elderly homes where several aged people stay together and require constant care. We aim to develop a novel AI module where along with the health parameters, the social context of the home can be augmented to provide an accurate and improved healthcare service. FEEL has been evaluated for three tasks, namely: 1) activity monitoring and location estimation; 2) fall detection; and 3) medical recommendations for unusual health conditions. A customized wearable device has been used to collect, store, and send health-related parameters. The experimental evaluation demonstrates promising accuracy (F1 score 0.86–0.94 range) for the tasks and outperforms the baselines by a significant margin ( $\approx 10$ %–16%).

Exploring human movement pattern from raw GPS traces is an interesting and challenging task. This paper aims at analysing a large volume of GPS data in spatio-temporal context, clustering trajectories using geographic and semantic location information and identifying different categories of people. It tries to exploit the fact that human moves with an intent. The proposed framework yields encouraging results using a large scale GPS dataset of Microsoft GeoLife.