Murat Yuksel

Md Asif Ishrak Sarder, Murat Yuksel, Elizabeth Bentley

Directional antenna systems are gaining substantial traction for aerial networks due to their higher gain, extended transmission range, and enhanced security. However, the requirement of beam alignment makes the task of finding and reaching neighbors challenging, particularly in a mobile setting. For wireless networks, privacy concerns play an equally critical role. However, the problem of ensuring network-wide connectivity while maintaining limited exposure when probing around is still unexplored. We address this trade-off by proposing an adaptive transceiver selection protocol based on the Deep Q-Network (DQN) framework. Each node acts as an independent DQN agent and interacts with the environment to learn how to balance the trade-off. Since the directional nodes operate only based on local observations, we adopt a weighted mechanism that guides them in prioritizing either high reachability or privacy by adaptively tuning the probing patterns. Results show that DQN framework surpasses the Random and Q-Learning baselines. Weights favoring discovery provide higher probing efficiency and reachability, while weights prioritizing privacy ensure limited exposure at the cost of low reachability, eventually attaining higher objective value.

Md Ibrahim Ibne Alam, Ankur Senapati, Anindo Mahmood et al.

Internet service providers (ISPs) need to connect with other ISPs to provide global connectivity services to their users. To ensure global connectivity, ISPs can either use transit service(s) or establish direct peering relationships between themselves via Internet exchange points (IXPs). Peering offers more room for ISP-specific optimizations and is preferred, but it often involves a lengthy and complex process. Automating peering partner selection can enhance efficiency in the global Internet ecosystem. We explore the use of publicly available data on ISPs to develop a machine learning (ML) model that can predict whether an ISP pair should peer or not. At first, we explore public databases, e.g., PeeringDB, CAIDA, etc., to gather data on ISPs. Then, we evaluate the performance of three broad types of ML models for predicting peering relationships: tree-based, neural network-based, and transformer-based. Among these, we observe that tree-based models achieve the highest accuracy and efficiency in our experiments. The XGBoost model trained with publicly available data showed promising performance, with a 98% accuracy rate in predicting peering partners. In addition, the model demonstrated great resilience to variations in time, space, and missing data. We envision that ISPs can adopt our method to fully automate the peering partner selection process, thus transitioning to a more efficient and optimized Internet ecosystem.