Intelligent Reward based Data Offloading in Next Generation Vehicular Networks

A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module

A DETAILED ANALYSIS AND OPTIMIZATION OF THE MODIFIED POLAR DECODING RNTI RECOVERY METHOD TO TRACK USER ACTIVITY IN 5G NETWORKS

In this thesis, we analyze and optimize the modified polar decoding and syndrome matching radio network temporary identifier (RNTI) recovery method to de-anonymize the physical downlink control channel (PDCCH) in 5G networks. We present the impact on RNTI recovery of payload length, codeword length, signal-to-noise ratio (SNR) and the Hamming and longest common substring (LCS) recovery methods. Further, we consider the full set of RNTIs and downlink control information (DCI) fields that can be examined for user activity data and propose methods to track user activity within radio networks from the recovered data. Finally, we optimize the RNTI recovery method for different attacker scenarios to demonstrate how an attacker can recover RNTIs, track UEs, and aggregate data about the UE usage patterns and/or metadata about the user.DOD Space, Chantilly, VA 20151Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited