Progression in Computer networks and emerging of new technologies in this field helps to find out new protocols and frameworks that provides new computer network-based services. E-government services, a modernized version of conventional government, are created through the steady evolution of technology in addition to the growing need of societies for numerous services. Government services are deeply related to citizens’ daily lives; therefore, it is important to evolve with technological developments—it is necessary to move from the traditional methods of managing government work to cutting-edge technical approaches that improve the effectiveness of government systems for providing services to citizens. Blockchain technology is among the modern technologies highly suitable for developing digital governance services, and its technological ability to sustain information stability is vital in digital governance systems since it improves integrity and transparency measures while preventing corruption. In this study, computer networking protocols are built to form a peer-to-peer network framework for managing official documents. using blockchain technology was built to illustrate how any element of government work may be developed using it. The suggested framework comprises the addition of a new official document, and the verification of an existing document. The system was created in socket programming using Java and tested the response times for many simultaneous requests. The system was tested using transactions per second (throughput) measurement. The result showed that the proposed system processed 200 document verification transactions within 50 seconds. In addition, the test of the proposed system presented the time required for document retrieval—about three seconds to answer 100 document retrieval transactions. Furthermore, the results of throughput were compared to the results of the same measurement of some popular applications such as bitcoin. And the result of the proposed system was within the average value of output throughput of the other compared applications.

Content Delivery Networks (CDNs) have evolved to meet surging data demands and stringent low-latency requirements driven by emerging applications like high-definition video streaming, virtual reality, and IoT. This paper proposes a hybrid CDN architecture that synergistically combines edge caching, Multi-access Edge Computing (MEC) offloading, and reinforcement learning (Q-learning) for adaptive routing. In the proposed system, popular content is cached at radio access network edges (e.g., base stations) and computation-intensive tasks are offloaded to MEC servers, while a Q-learning agent dynamically routes user requests to the optimal service node (cache, MEC server, or origin) based on the network state. The study presented detailed system design and provided comprehensive simulation-based evaluation. The results demonstrate that the proposed hybrid approach significantly improves cache hit ratios and reduces end-to-end latency compared to traditional CDNs and simpler edge architectures. The Q-learning-enabled routing adapts to changing load and content popularity, converging to efficient policies that outperform static baselines. The proposed hybrid model has been tested against variants lacking MEC, edge caching, or the RL-based controller to isolate each component’s contributions. The paper concludes with a discussion on practical considerations, limitations, and future directions for intelligent CDN networking at the edge.

Road traffic accidents are one of the leading causes of mortality globally. Reducing the number of traffic-related incidents has become a serious socio-economic and public health problem, given the ever-increasing number of cars on the road. As a result, this paper proposes an intelligent vehicle prediction communication mechanism that alerts drivers to any autos that may be overtaking or bypassing the targeted vehicle. The primary goal of this paper is to leverage modern Internet of Things (IoT) and wireless sensor technologies to predict any potential accident that may occur as a result of car accidents. This paper proposes the Collision Prediction of a Moving Vehicle (CPMV) system. The information acquired by CPMV will alert the driver to divert the vehicle in a reasonable amount of time before any harm occurs. It redirects the inbound object that emitted the Ultrasound signal which was received by the vehicle, to a safe location. The proposed system predicts collision between vehicles through Wi-Fi and Bluetooth, using a set of sensors with a precision of 360 degrees and a distance of collision prediction of one meter and at a speed of 200-300 revolutions per minute. The python programming language was utilized to code the programs that control the vehicle during the implementation of this project. The Raspberry Pi 4 is utilized as the controller to examine the vehicle’s spatial data. The test results showed that using this application to deal with an approaching object can be a successful strategy in the three proposed scenarios at different angles and directions. Index Terms— collision prediction, collision avoidance, Internet of Things (IoT), Raspberry Pi, vehicle communication.

Network function virtualization (NFV) has emerged as a transformative paradigm in which traditional hardware appliances are replaced with virtual network functions (VNFs) running on commodity hardware. While NFV offers scalability and flexibility, it faces major challenges in sustaining high throughput and minimizing resource overhead under dynamic traffic conditions. In particular, flooding algorithm-based request propagation often leads to excessive redundancy, congestion, and resource waste. To address this limitation, this study applies Manta ray foraging optimization (MRFO), a swarm intelligence algorithm inspired by natural foraging behaviors, to optimize packet routing and resource allocation in NFV environments. The research employs a Barabási–Albert (BA) scale-free topology model to simulate realistic NFV infrastructures. The performance is evaluated by comparing the conventional flooding algorithm with MRFO-based routing under varying network sizes and time-to-live (TTL) values. The key metrics include throughput, packet delay, CPU and memory utilization, and the success rate. The simulation results demonstrate that MRFO consistently outperforms flooding in medium- and large-scale networks, achieving up to 53.6% improvement in throughput, reduced average delay (2.6 s → 1.7 s), and more balanced resource utilization across NFVs. However, in small-scale networks with limited routing paths, MRFO introduces computational overhead that reduces performance compared with the flooding algorithm. These findings highlight the significance of swarm intelligence for NFV optimization, showing that MRFO is best suited for scalable, dynamic infrastructures such as the cloud, Internet of Things (IoT), and 5G edge networks. This study contributes a novel integration of MRFO with flooding algorithm-based propagation, offering new insights into adaptive and resource-aware NFV optimization strategies.