In the era of smart devices and connected neighborhoods, the ubiquitous monitoring and care of patients are possible with the Internet of Medical Things (IoMT). Smart healthcare devices may serve their purpose well when they are able to share patient’s data with each other. However, data formats vary widely across vendors, rendering these devices not interoperable. Recent solutions mostly rely on cloud services where a source device uploads the data, and the sink devices download it conforming to their own native formats. However, the quality of service is expected to deteriorate in a cloud processing regime with inherent network delays and traffic congestion, and the real-time data acquisition and manipulation is, therefore, not possible. This article presents MeDIC, a framework of Medical Data Interoperability through Collaboration of healthcare devices. MeDIC improves over a cloud-based IoMT by utilizing translation resources at the network edge, with its probing and translating agents. The probing agents maintain a capability list of MeDIC devices within a local network and enable one MeDIC device to request data conversion from another device when the former is not capable of this conversion by itself. The translating agent of the later then converts the data into the required format and returns it to the former. These novel agents allow IoMT devices to share their redundant computing resources for data translations in order to minimize cloud accesses. Legacy devices are supported through MeDIC-enabled, fog resource managers. We evaluate MeDIC in four use cases with rigorous simulations, which prove that this collaborative framework not only reduces the uplink traffic but also improves the response time, which is critical in real-time medical applications.

The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer’s. Therefore, the detection of such mild cognitive impairment (MCI) becomes important. Usually, this is detected when it is converted into Alzheimer’s disease (AD). AD is irreversible and cannot be cured whereas mild cognitive impairment (MCI) can be cured. The goal of this research is to diagnose Alzheimer’s patients for timely treatment. For this purpose, functional MRI images from the publicly available dataset are used. Various deep‐learning models have been used by the scientific community for the automatic detection of Alzheimer’s subjects. These include the binary classification of scans of patients into MCI and AD stages, and limited work is carried out for multiclass classification of Alzheimer’s disease up to six different stages. This study is divided into two steps. In the first step, a binary classification of the subject’s scan is performed using Custom CNN. The second step involves the use of different deep learning models along with Custom CNN for multiclass classification of a subject’s scan into one of the six stages of Alzheimer’s disease. The models are evaluated based on different evaluation metrics, and the overall result of the models is improved using the max‐voting ensembling technique. The experimental results show that an overall average accuracy of 98.8% is achieved for Alzheimer’s stages classification.

With growing security threats, many online and offine frameworks have been proposed for anomaly detection in video sequences. However, existing online anomaly detection techniques are either computationally very expensive or lack desirable accuracy. This research work proposes a novel particle filtering based framework for online anomaly detection which detects video frames with anomalous activities based upon the posterior probability of activities in a video sequence. The proposed method also detects anomalous regions in anomalous video frames. We propose novel prediction and measurement models to accurately detect anomalous video frames and anomalous regions in video frames. Novel prediction model for particle prediction and likelihood model for assigning weights to these particles are proposed. These models efficiently utilise variable sized cell structure which creates variable sized sub-regions of scenes in video frames. Furthermore, they efficiently extract and utilise information from the video frame in the form of size, motion and location features. The proposed framework is tested on UCSD and LIVE datasets and compared with the existing state-of-the-art algorithms in the literature. The proposed anomaly detection algorithm outperforms the state-of-the art algorithms in terms of reduced Equal Error Rate (EER) with comparatively lesser processing time.

In conventional healthcare, real‐time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients’ death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients’ medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)‐based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID‐19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer’s. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud‐based environment using benchmark and real‐world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases.

The advancements in Edge computing have paved the way for deep learning in real-time systems. One of the beneficiaries is an adaptive traffic control system that responds to real-time traffic observations by governing the signal phase and timings. Reinforcement Learning (RL) is extensively utilized in the literature in order to decrease traffic congestion in a road network. However, most of the previous works leverage centralized and cloud-based RL due to the computational complexity of underlying deep neural networks (DNN). Therefore, a persistent challenge towards adopting Edge learning is in devising a Multi-Agent RL in which agents are simplified, and their state spaces are localized but they perform comparable to the centralized RL. This article presents a Collaborative and Adaptive Signaling on the Edge (CASE), a novel Multi-Agent RL approach to control the traffic signals’ phase and timing. Each signalized intersection in the road network is provided with an Edge Learning Platform which hosts an RL-Agent that observes local traffic states and learns an optimum signal policy. Moreover, CASE allows collaboration among RL-Agents by sharing their signal phase and timings to achieve convergence and performance. This collaboration is limited to one’s direct neighbours only to minimize the computational complexity. We performed rigorous evaluations in terms of the choice of RL methods and their state space/reward and found that our collaborative state-space has resulted in a performance comparable to a centralized RL yet with a cost similar to the decentralized RL. Finally, a performance comparison of the CASE controller ported to the state-of-the-art Edge learning platforms is presented in this article. The results show that the proposed CASE controller can achieve real-time performance when ported to a general-purpose GPU-based platform. This arrangement achieves more than 8 times improvement in computational time over conventional embedded platforms.

Introducing coding in early education improves the logical and computational thinking in kids. However, cognitive skills are not sufficient for a successful life. Understanding and managing the emotions of oneself is another crucial factor in success. The current state of the art teaching methods educates the kids about programming and emotional intelligence independently. In our opinion, it is advantageous to teach kids emotional intelligence, along with the programming concepts. However, the literature lacks the studies that make students emotionally aware while teaching them programming. This research aims to prepare students to be cognitively healthy as well as emotionally intelligent with the hypothesis that a kid’s emotional intelligence can be enhanced while teaching them cognitive skills. We proposed a computational model that teaches programming and emotional intelligence side by side to students. The model provides a curriculum and related tools. For evaluations, five hundred students of a public school were involved in different activities to find the effectiveness of the proposed model. These students were divided into five groups (A, B, C, D, and E), each having a mean age of 4, 5, 6, 7, and 8 years, respectively. Students performed multiple adaptive scenarios of path-finding that were based on self-awareness, social-awareness, sharing, and empathy emotions. Students provide the programming instructions such as sequencing, conditional statements, and looping to a robot. The children have successfully improved in both fundamental programming constructs and emotional intelligence skills. The research also successfully reduced screen time problem by providing a screen-free student interface.