Continuous advancements in digital image transmission technology within network environments have heightened the necessity for secure, convenient, and well-suited image encryption systems. Base64 encoding possesses the ability to convert raw data into printable ASCII characters, facilitating excellent stability transmission across various communication protocols. In this paper, base64 encoding is first used in image encryption to pursue high application value in network transmission. First, a novel one-dimensional discrete chaotic system (1D-LSCM) with complex chaotic behavior is introduced and extensively tested. Second, a new multi-image encryption algorithm based on the proposed 1D-LSCM and base64 encoding is presented. Technically, three original grayscale images are constructed as a color image and encoded in base64 characters. To purse high plaintext sensitivity, the original image is input to the SHA-256 hash function and its output is used to influence the generated keystream employed in the permutation and diffusion process. After scramble and diffusion operations, the base64 ciphertext is obtained. Finally, test results derived from comprehensive tests prove that our proposed algorithm has remarkable security and encryption efficiency.

Recently, RFID technology has come into end-user applications for monitoring, tracking, and so forth. In RFID system, a reader identifies a set of tags over a shared wireless channel. When multiple tags communicate with the same reader simultaneously, all packages will be lost and no tag can be recognized, which is known as tag collision. Tag collision is a significantly important issue for fast tag identification. We firstly make a thorough analysis among a variety of traditional RFID anticollision algorithms. Then a novel hybrid anticollision algorithm called T-GDFSA is proposed. Tags are assigned to different groups based on the initial tag estimation and then experience several dynamic read frames for identification. When a collision occurs in current slot, a tree-based recursive process will be deployed immediately. T-GDFSA combines the advantages of ALOHA-based and tree-based together and acquires higher system throughput by reducing unnecessary idle and collision slots and lower communication complexity by decreasing the data transmitted, which makes it identify tags faster with less power consumption. Simulations show that the theoretical values match well the simulation results. Moreover, T-GDFSA also has a good tolerance for the inaccuracy of initial tag estimation and the length variation of tag's ID.

Mitigating tag collisions is paramount for enhancing throughput in Radio Frequency Identification (RFID) systems. However, traditional algorithms encounter challenges like slot wastage and inefficient frame length adjustments. To tackle these challenges, the Slot Prediction Q (SPQ) algorithm was introduced, integrating the Vogt-II prediction algorithm and slot grouping to improve the initial Q value by predicting the first frame. This method quickly estimates the number of tags based on slot utilization, accelerating Q value adjustments when slot utilization is low. Furthermore, a Markov decision chain is used to optimize the relationship between the number of slot groupings (x) and the Q value. The Whale Optimization Algorithm (WOA) is applied to fine-tune the learning rate (C) and Q value in the traditional Q algorithm. Simulation results demonstrate that SPQ significantly reduces the total slots used during the reading process and improves RFID system throughput compared to traditional Q, FastQ, Subset Enhanced Performance-Q (SUBEP-Q), and Threshold Grouping Dynamic Q (TGDQ) algorithms. Specifically, compared to the traditional Q algorithm, SPQ increases the average Identification Speed by 7.20%, System Efficiency by 11.08%, and Time Efficiency by 5.69%.

High throughput is crucial for Electronic Toll Collection(ETC) systems to effectively reduce traffic congestion. However, traditional algorithms face challenges such as slot waste and slow adjustments in frame length. To tackle these issues, a Slot Prediction Q algorithm(SPQ) has been introduced, which combines the Vogt-Ⅱ Prediction algorithm and slot grouping concept to improve the initial Q-value by predicting the first frame. This algorithm can quickly predict the number of tags to be identified based on slot utilization, speeding up the Q-value adjustment process when slot utilization is low. Additionally, the Markov decision chain is used to determine the optimal relationship between the number of slot groupings x and Q-value. The Whale Optimization Algorithm(WOA) is employed to optimize the relationship between the learning rate C and Q-value in the traditional Q algorithm. Simulation results show that SPQ significantly reduces the total number of slots used during the reading process, thereby enhancing the ETC throughput.