With the removal of the barriers of time and distance, E-learning platforms have attracted millions of learners, but these platforms are experiencing a significant drop-out ratio. One of the primary reasons for this problem is the lack of motivation among the learners because of the similar learning experience provided to them despite their varying learning styles. Different researchers have introduced gamification as a solution for students’ engagement. The technique has improved engagement levels a bit, but it is not as useful as it was expected to be. One of the primary problems with gamification elements is their inability to induce intrinsic motivation among learners. To target this issue, we have proposed a framework that identifies the learning style of students based on their interactions with the system and provides an adaptive gamification experience according to their identified learning dimensions. The results of the experiments show that the motivation of learners increases by 25%, and the drop-out ratio is reduced by 26%.

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.

With advancements in educational technologies, e-learning platforms have evolved to provide learning environments to the privileged and under-privileged population so that they can learn at their own pace. The success of these systems relies on engaging experience and timely and accurate feedback to the students on their performance. Still, these systems suffer from high student dropouts, often due to a lack of personalization in student interactions. While students show different collaborative behavior, i.e., some students are social, and like discussions, while others are self-oriented and do not participate in any collaborative activity, the feedback and interactions with students are generally not customized based on their type of collaborative behavior. This research aims to develop a method that provides adaptive feedback to each student according to their type of collaborative behavior and preferred gamification elements. Two experiments were performed to evaluate the system, and the results show that the system, with adaptive feedback, significantly improved student performance.

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.

Due to exponential growth in the daily usage of vehicles, the traffic congestion and roadside accidents are increasing day by day. The communication among vehicles is critical to avoid the emergencies and to address the issue of congestion of vehicles. Internet of vehicles provides the communication channel between the vehicles, but existing solutions require a centralized communication system to distribute the message and to authenticate the source. This centralized infrastructure is subject to disturb the vehicular communication in case of server breakdown or due to any natural disaster where hardware stops working. Also, the centralized system proves to be costly as the communication of each vehicle necessitates access to the central server resulting in the more resource requirement. A secure distributed system is required to avoid the emergencies and reduce the traffic rate. To address the issues, we proposed a secure distributed message-passing framework that does not require a centralized server, and it rates the credibility of message source using blockchain technology. The messages in the proposed system are forwarded through dedicated short-range communication protocol. To validate the proposed system, we have performed different simulations using SUMO, OMNET, and VEINS. The results demonstrated an increase in the average speed of vehicles that showed a reduced congestion rate. Moreover, our system identified the malicious vehicles with 77.1% accuracy.