The aims of this survey article are to elaborate on cross-layer optimization, Software-Defined Networking (SDN) and Software-Defined Radio (SDR) as separate domains of wireless network design for which a unified view has not been adequately considered to date and present lessons learned, with a view towards the challenges associated with SDN-SDR interaction that would facilitate benefits in cross-layer optimization of mobile ad hoc networks (MANETs). We focus on MANETs because ( ${i}$ ) they are still at the forefront of technology, and in some scenarios they are the only meaningful option for establishing communication; ( ii ) they expose the full potential and benefits of coexistence and interaction of SDN and SDR, in terms of optimizing key performance indicators. While SDN and SDR are mature technologies, their interaction and joint consideration have been largely overlooked. Current SDN approaches do not span the physical (PHY) and medium-access control (MAC) layers, but they rather concentrate on network-level routing and traffic flow optimization. As a result, PHY- and MAC-layer related parameters which notoriously affect key network performance metrics remain static or at best are adapted based on some heuristic or local approaches. On the other hand, the reach of SDR architectures is restricted to the PHY and MAC layers. We discuss the state of the art of cross-layer optimization, SDN and SDR, and current challenges associated with coexistence and interaction of SDN and SDR. Such an interaction would extend the span of SDN to PHY and MAC layers and lead to realizations of centralized approaches across all layers so as to control and optimize parameters, towards global network objectives. It would also create a bridge between centralized network control that is inherent in SDN and the distributed nature of MANETs, with the add-on features of flexible and fast PHY and MAC layer adaptation offered by SDR, for solid, autonomous and ultimately better network control implementations that span all layers, towards realizing and implementing the holy grail of real cross-layer optimization.

We are concerned with the personalized student course plan (PSCP) problem of optimizing the plan of courses students at the American College of Greece will need to take to complete their studies. We model the constraints set forth by the institution so that we guarantee the validity of all produced plans. We formulate several different objectives to optimize the resulting plan, including the fastest completion time, course difficulty balance, and maximization of the expected student grade point average given the student’s performance in past courses. All resulting problems are mixed-integer linear programming problems with a number of binary variables, that is, the max number of terms times the number of courses available for the student to take. The resulting mathematical programming problem is solvable in less than 10 seconds on a modern commercial off-the-shelf PC, whereas the manual process used to take more than one hour of advising time for every student and, as measured by the objectives set forth, resulted in suboptimal schedules. History: This paper was refereed.

Cybersecurity is essential for organizations of all sizes, as cyber threats can jeopardize their data, operations, reputation, and competitiveness. Consequently, a one-size-fits-all cybersecurity framework, especially one based solely on maturity levels, is neither practical nor effective, as acknowledged in recent literature critiquing universal maturity models (Liyanage et al. in Sok: identifying limitations and bridging gaps of cybersecurity capability maturity models (ccmms). arXiv preprint arXiv:240816140, https://arxiv.org/abs/2408.16140, 2024). Instead, organizations require a scalable and adaptive framework that allows them to assess their current cybersecurity posture, identify gaps and risks, and implement targeted measures to enhance their cybersecurity maturity in a timely and practical manner. Maintaining continuous awareness of their cybersecurity status and understanding the degree of necessary improvement is critical. In response to these challenges and to address the limitations of existing capability models and frameworks, this paper proposes a novel framework, the Cyber-Resistivity Maturity and Scoring Framework (CRMSF). It emphasizes distinct capability evaluation attributes, including the use of weight factors, criticality and complexity analysis, benchmarking, and maturity levels.

In the realm of military operations, this research paper investigates the complex implications surrounding the integration of fifth-generation (5G) wireless technology, aiming to serve the enhanced and diverse military user requirements for established Quality of Service (QoS) provisioning in modern-era military environments. This work presents a testing campaign conducted within a controlled laboratory environment, aimed at facilitating the interconnection of two independent 5G networks (5G Bubbles), through the utilization of a conventional military Internet Protocol (IP) radio. The testing campaign was executed as part of the 5G COMPAD Project, co-funded by the European Union through the European Defence Fund (EDF). The core objective of the campaign was to demonstrate that text, voice and video services can be efficiently provisioned and sustained between 5G User Equipments (UEs) situated within these independent 5G Bubbles, interconnected solely via a standard military IP radio. Various performance metrics were defined, measured and evaluated during the testing campaign, while the results indicate that legacy military radios and 5G systems can be successfully integrated, thereby offering robust support for the diverse communication needs of tactical units, despite the performance constraints set by military links. The main contribution of this study is the practical integration of commercial 5G components with existing military communication assets, marking the first European experimental campaign in this domain. This effort provides critical insights into the feasibility and performance of linking independent 5G Bubbles over legacy military infrastructure in realistic defense-oriented scenarios.

We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.