Improving Efficiency of Passive RFID Tag Anti-Collision Protocol Using Dynamic Frame Adjustment and Optimal Splitting

Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

On the Security of Rotation Operation Based Ultra-Lightweight Authentication Protocols for RFID Systems

Passive Radio Frequency IDentification (RFID) tags are generally highly constrained and cannot support conventional encryption systems to meet the required security. Hence, designers of security protocols may try to achieve the desired security only using limited ultra-lightweight operations. In this paper, we show that the security of such protocols is not provided by using rotation functions. In the following, for an example, we investigate the security of an RFID authentication protocol that has been recently developed using rotation function named ULRAS, which stands for an Ultra-Lightweight RFID Authentication Scheme and show its security weaknesses. More precisely, we show that the ULRAS protocol is vulnerable against de-synchronization attack. The given attack has the success probability of almost ‘1’, with the complexity of only one session of the protocol. In addition, we show that the given attack can be used as a traceability attack against the protocol if the parameters’ lengths are an integer power of 2, e.g., 128. Moreover, we propose a new authentication protocol named UEAP, which stands for an Ultra-lightweight Encryption based Authentication Protocol, and then informally and formally, using Scyther tool, prove that the UEAP protocol is secure against all known active and passive attacks.