5G technology enhances industries with highspeed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasing complexity of applications on User Equipment (UE), offloading resource-intensive tasks to robust servers is essential for improving latency and speed. The 3GPP's Multi-access Edge Computing (MEC) framework addresses this challenge by processing tasks closer to the user, highlighting the need for an intelligent controller to optimize task offloading and resource allocation. This paper introduces a novel methodology to efficiently allocate both communication and computational resources among individual UEs. Our approach integrates two critical 5G service imperatives: Ultra-Reliable Low Latency Communication (URLLC) and Massive Machine Type Communication (mMTC), embedding them into the decision-making framework. Central to this approach is the utilization of Proximal Policy Optimization, providing a robust and efficient solution to the challenges posed by the evolving landscape of 5 G technology. The proposed model is evaluated in a simulated 5G MEC environment. The model significantly reduces processing time by$\mathbf{4 \%}$for URLLC users under strict latency constraints and decreases power consumption by$\mathbf{2 6 \%}$for mMTC users, compared to existing baseline models based on the reported simulation results. These improvements showcases the model's adaptability and superior performance in meeting diverse$\mathbf{Q o S}$requirements in 5G networks.

The Distributed Sensing and Computing Over Sparse Environments (DISCOVER) testbed is a pioneering cyberinfrastructure initiative designed to advance research in distributed computing and networking tailored to rural, remote, and sparsely populated regions. Supported by the National Science Foundation (NSF), DISCOVER integrates a network of configurable Internet-of-Things (IoT) nodes—including stationary sensors, drones, and terrestrial rovers—across three key sites: Northern Arizona University (NAU), Clemson University, and Navajo Technical University (NTU). This collaboration offers a unique platform to explore innovative algorithms and methodologies addressing the technical challenges of under-served areas, with an emphasis on environmental and civil disaster response. The testbed enables a wide range of experiments, such as regional-scale data collection, heterogeneous networked services, distributed artificial intelligence (AI), distributed multi-robot control, and communication-aware software for resource-constrained networks. An online portal enhances accessibility, allowing researchers to request resources, upload experimental code, and retrieve data, with pre-integrated deep learning models for applications like human posture detection, object detection, and wildfire detection. This paper outlines the testbed’s architecture, operational sites, supported experiment types, ongoing research efforts, and its educational and outreach impacts, highlighting its role in fostering scientific innovation.

The COVID-19 pandemic has become a challenge to our lives and affects us differently under certain circumstances. This project invites and assists participants in developing their awareness systems for COVID-19. The system comes with a set up consisting of standard information and the necessary coding explained to the participant. The findings are open and shared through an honor system. The awareness system consists of four embedded subsystems known as diagnostics systems, simulation, tracing, and pattern-matching systems. The diagnostic system uses inference rules to generate an outcome by diseases with the same symptoms related to COVID-19. The skeleton for the simulation system works as a game, depicting how the virus enters and interacts with the body's cells with different four scenarios such as involved in defeating the body's cell, defeating the virus, neutral coexistence of the virus and enclosure, and at best, turning into a positive virus. At that stage of defeating the body's cell, the virus will replicate itself by an assigned degree with a recursive behavior. The tracing system traces the COVID19 and the participant's health using red, green, and blue (RGB) colors. The combination of RGB creates more than 16 million using 24 bits in binary or hexadecimal. The red color reserves for COVID-19, with 16 shades of red for a symptom with a 16 degree of severity. The pattern-matching system's skeleton provides four databases for asymptomatic, mild, severe, and fatal cases. The digitized information will quickly identify the pattern. One database may compare with another database for similarities or differences. The transfer of learning from one system can flow into another, ultimately resulting in a solution pattern. This project's implication will guide others to initiate their participant system to find a way and formulate a solution.