Modern wireless communication systems have become increasingly complex due to the proliferation of wireless devices, increasing performance standards, and growing security threats. Managing these networks is becoming more challenging, requiring the use of advanced network management methods and tools. AI-driven network management systems such as Self-Optimizing Networks (SONs) are gaining attention. On the other hand, Large Language Models (LLMs) have been demonstrating exceptional zero-shot learning and generalization capabilities across several domains. In this paper, we leverage the potential of LLMs with SONs to enhance future network management systems. Specifically, we benchmark the use of various LLMs such as GPT-4, Llama, and Falcon, in a zero-shot setting based on their real-time network configuration recommendations. Our results indicate promising prospects for integrating LLMs into future network management systems.
IoA (Internet of Agents), combining MAS (Multi-Agent Systems) and IoT (Internet of Things), aims to provide an environment where diverse services can be performed and evaluated through heterogeneous agents’ cooperative participation. Given this, IoA requires a mechanism monitoring agents’ real-time behaviors to prevent undesired activities caused by compromised ones. This paper proposed a trust model to detect agents’ misbehavior by taking their roles into consideration, namely SP (Service Provider), SR (Service Rater), and SR+SP. Moreover, we conducted a real MAS implementation by using ROS 2 (Robot Operating System) to verify the preliminary results of SR and SP agents’ trust evaluation.
The IPv6 over Bluetooth Low Energy (BLE) standard defines the transfer of IP data via BLE connections. This connection-oriented approach provides high reliability but increases packet delays and requires substantial overhead to manage BLE connections. To overcome these drawbacks we present the design and implementation of IPv6 over BLE advertisements, a standard-compliant connection-less approach. We deploy our proposal on low-power IoT hardware and comparatively measure key network performance metrics in a public testbed. Our results show that IP over BLE advertisements offers network performance characteristics complementary to IP over connection-based BLE, trading lower reliability for shorter~latency.
ler for many new technologies, ranging from autonomous vehicles to shared mobile devices. In order to ensure high precision for those applications, GNSS augmentation systems are needed to provide correction data to reach a precision that is in the order of centimeters. Those systems can be provided as paid services where correction data are broadcast over a satellite link. In order to protect those systems and restrict their access only to paid users, encryption mechanisms must be adopted. SPARTN is an open industry standard for GNSS augmentation that has been specifically designed for supporting encryption, while saving bandwidth on the satellite link. In this paper, we propose APBE (Anti-Piracy Broadcast Encryption), a method to enhance the SPARTN security by providing protection against pirate customers that is specifically tailored to minimize bandwidth and storage. The proposed approach is demonstrated to be feasible via a real proof-of-concept implementation based on an embedded system. APBE is a candidate mechanism to be included in the future versions of SPARTN.
