The energy consumption and coverage range of unmanned aerial vehicles (UAVs) are major challenges in UAV-based postdisaster communications. To address these challenges, energy harvesting is employed to power communication devices and prolong the lifetime of the wireless communication network during a disaster. In addition, clustering techniques and device-to-device (D2D) communication are needed to increase the overall network coverage and provide sustainable connectivity during the disaster and postdisaster phases. We have proposed a novel emergency communication system (ECS) using the optimal cluster head (CH) technique to improve the energy transfer efficiency for sustainable network connectivity. We have developed a UAV deployment model assisted by the clustering technique and D2D links that is capable of harvesting energy to increase the network lifetime. This new approach is expected to enhance the reliability of the network in disaster situations. The proposed methods have been evaluated by measuring the energy efficiency performance and the network outage probability. The simulation results demonstrate improved performance with the deployment of optimal CHs, while the outage probability has been effectively reduced. Moreover, the proposed approach has been proven to reduce the computational complexity. In conclusion, UAV deployment with the optimal CH algorithm is a suitable network design to recover from natural disasters and potentially save many lives.

In this paper, we present a novel power allocation scheme for multicarrier cognitive radio networks. The proposed scheme performs subchannel power allocation by incorporating primary users activity in adjacent cells. Therefore, we first define the aggregated subchannel activity index (ASAI) as an average indicator which characterizes the collective networkwide primary users' communication activity level. The optimal transmit power allocation is then obtained with the objective of maximizing a total utility function at the secondary base station (SBS), subject to the maximum SBS transmit power, and collision probability constraint at the primary receivers. Utilizing ASAI, we further obtain an energy efficient power allocation for the secondary system. Optimal energy efficiency (EE) and spectral efficiency (SE) are contradicting objectives, and thus, there is a tradeoff between these two performance metrics. We also propose a design approach to handle this tradeoff as a function of the ASAI, which provides quantitative insights into efficient system design. In addition to a lower signaling overhead, the simulation results confirm that the proposed scheme achieves a significantly higher achievable rate. Simulation results further indicate that using ASAI enables obtaining an optimal operating point based on the tradeoff between EE and SE. The optimal operating point can be further adjusted by relaxing/restricting the sensing parameters depending on the system requirements.

The cultural shift of work from on-premises to remote home offices allows hackers to access corporate data by compromising devices attached to home-based broadband routers. Currently, home devices are growing at an exponential rate, e.g., in the form of smartphones, Internet of Things (IoT) devices, and corporate laptops. Presently, there is a lack of cost-effective, practical, and lightweight Unified Threat Management (UTM) systems designed to protect home users from the wide range of existing cyber threats. This applied research article introduces a sophisticated Smart UTM System (SUTMS) that is designed to tackle the contemporary security issues encountered in home networks while running on a Raspberry Pi single-board computer. The proposed integrated SUTMS design consists of flow detection, intrusion detection, and firewall core engines, as well as optional routing and log collection engines. The flowdetection engine in SUTMS discovers anomalies and detects the active protocols, which are a basis for signature optimization in the SUTMS intrusion detection system (IDS). By ingesting Indicator of Compromise (IoC) feeds, the SUTMS firewall engine provides dynamic anti-bot protection. Evaluations indicate that SUTMS with IDS signature optimization can provide 99% accuracy with approx. 55% memory utilization reduction compared to traditional signature-based IDS.

In this paper, we introduce a novel, self-hosted Syslog collection platform designed specifically to address the challenges that small and medium enterprises (SMEs) face in implementing comprehensive syslog monitoring solutions. Our analysis begins with an assessment of current network observability practices, evaluating enterprise solutions, on-premises systems, and Software as a Service (SaaS) offerings to identify features crucial for SME environments. The proposed platform represents an advancement in the field through the incorporation of modern practices, including GitOps and continuous integration and continuous delivery/deployment (CI/CD), and its implementation onto a self-managed Kubernetes platform, which is an approach not commonly explored in SME-focused solutions. We will explore its scalability by leveraging dynamic templates, which allow us to select the number and type of nodes when deploying networks of various sizes. This architecture ensures organisations can deploy a pre-designed, scalable network monitoring solution without extensive external support. The resilience of the proposed platform is assessed by providing empirical evidence of the scaling performance and reliability under various failure scenarios, including node failure and high network throughput stress.