International Conference on Communication Systems and Networks : [proceedings]

Resource allocation is a fundamental problem in telecom and various other fields. In this work, we consider the issue of scheduling users for efficient radio resource management. Existing scheduling methods focus on maximizing the QoS of users. However, in future generation networks, it is imperative to schedule based on the requirements set forth by application owners. This paper presents a novel approach to time scheduling in 5G networks using model-free Reinforcement Learning (RL). The proposed framework leverages recent advancements in RL-based scheduling to dynamically allocate resources based on real-time network conditions and user-defined requirements (as defined by intents). To reduce the complexity of the action space, we introduce a novel greedy mapping function to convert the small action space of the RL agent into time-scheduling weights. Simulation results show that our approach effectively meets performance goals for throughput and latency, while reducing computational costs by 50% compared to existing methods.

In this article, we propose a Deep Q-Learning-based approach to improve the throughput of a Wireless Powered Communication Network (WPCN) by controlling the transmissions of energy harvesting devices through adaptive beam steering. We examine the dynamics of the slotted ALOHA protocol in conjunction with the charging process of energy harvesting devices to develop a framework for maximising throughput through beam steering, despite partial observability constraints, such as unknown device locations and current charge levels. We design a Deep Q-Network that learns the optimal beam steering policy only from the past observations of transmission attempts by the energy harvesting devices. Extensive simulations illustrate that our proposed approach offers as much as 26% improvement in throughput over deterministic round robin beam steering and 9% over random beam steering strategies for a dense network size of 50 devices. The performance is found to improve with increasing length of past observations.

The Internet of Things (IoT) demands lightweight and secure authentication schemes for resource-constrained multihop networks, where IoT nodes communicate with the gateway through multiple intermediary nodes. Present distributed schemes enable secure lightweight authentication for such networks, but overwhelm an IoT node with multiple authentication requests from its newly joined child nodes, leaving other nodes underutilized. This paper presents a decoupled distributed authentication scheme, where IoT nodes capable of authentication are selected by the gateway as an authentication server to authenticate newly joined nodes. The authentication server may be a parent or a non-parent node and thus separates the process of authentication and node joining, mitigating the skewed authentication load by newly joined child nodes to their parent node. After a successful authentication, the authentication server informs the gateway about the authenticated node and mediates the session key generation between the gateway and the node. It utilizes Physically Unclonable Function (PUF) responses to eliminate the need for long-term storage of the secret key (for the secure authentication execution) and prevents adversarial prediction by introducing session-based randomness. Lightweight hash-based accumulators and XOR operations are also introduced, producing reduced computational overhead on resource-constrained nodes. Security validation of the scheme is performed, both formally (using ProVerif) and informally. In addition, performance analysis is conducted through theoretical and simulation-based frameworks, focusing on energy consumption, CPU time, and communication overhead. Analysis demonstrates that the proposed scheme is secure while maintaining a lightweight structure over existing methods.

WiFi-based indoor localization provides a practical alternative to vision and sensor-based tracking, but its adoption is limited by high energy costs from repeated signal scans. To address this, we propose LowFI, a layout-aware, low-energy WiFi localization framework that jointly optimizes the number of scans and access point (AP) selection. LowFI leverages fading in RF channels to adaptively budget scans and rank APs, reducing redundancy while maintaining accuracy. Experiments with various WiFi-enabled devices show that localization error in nLoS conditions decreases from ≈8 m with a single scan to ≈2 m with much limited number of scans, after which accuracy gains saturate. By selecting minimal scans and a modest set of APs, LowFI achieves sub-meter accuracy while cutting energy cost by up to 65%. These results demonstrate that careful scan and AP orchestration can deliver reliable localization without compromising device battery.

In urban 5G Multi-access Edge Computing (MEC), overlapping server coverage and dense, heterogeneous user service demands often lead to uneven request distribution, causing resource underutilization at some edge servers and overloading at others. This imbalance degrades overall system efficiency, making load balancing essential for effective resource utilization and Quality of Service (QoS) assurance during multiple service placement and request allocation. We address this by formulating the Load-aware Service placement and Request allocation (LSR) problem as a multi-objective optimization model and proposing a scalable and efficient two-stage heuristic. The objective is to minimize server utilization variance while reducing service deployments and maximizing user coverage. In the heuristic, the first stage ensures maximum coverage through deterministic request allocation and greedy service placement, all within MEC constraints. The second stage iteratively refines service placement and request allocation to minimize utilization variance, offering reduced computational complexity compared to existing approaches. Experimental results demonstrate that our approach achieves significantly lower variance (65%) in optimal scenarios, reduces service deployments by 15%, increases user coverage by 15%, and establishes itself as an efficient centralized solution for quasi-static multi-service edge deployments in urban 5G-MEC environments.