Achieving a balance between security and retrieval accuracy presents a significant challenge in secure content-based image retrieval (SCBIR), particularly in untrusted cloud environments. Conventional SCBIR schemes often struggle with trade-offs, either compromising data privacy, reducing retrieval performance, or introducing significant computational overhead, making them unsuitable for large-scale real-world applications. To overcome these challenges, we propose SCBIR-PE, a novel SCBIR scheme that integrates block-based perceptual encryption with an efficient histogram-based feature extraction method. The encryption incorporates geometric and color transformations at block and sub-block levels across three distinct extensions, confirming security robustness while preserving image features necessary for an image retrieval application. Thus, feature extraction is performed using the Color and Edge Directivity Descriptor (CEDD), enabling direct feature computation in the encrypted domain. Additionally, Euclidean distance is utilized as a similarity measurement technique, ensuring efficient image retrieval while avoiding computationally intensive tasks like clustering, index generation, or feature mapping, which are at the core of existing SCBIR schemes. Experimental evaluations demonstrate that SCBIR-PE scheme outperforms conventional privacy preserving image retrieval schemes in terms of retrieval accuracy (up to 4% better mean average precision score), security robustness, and computational efficiency. By ensuring strong privacy protection with minimal computational overhead, our proposed scheme offers a scalable and efficient solution for cloud-assisted secure image retrieval applications.

Cloud-edge collaboration provides an efficient way to promote the development and application of the Artificial Intelligence of Things (AIoT) by addressing the limited computing, communication, and storage capabilities of the Internet of Things (IoT) devices. The compute-intensive task of developing an Artificial Intelligence (AI) model is performed on the cloud-server over a public dataset while it is deployed on the edge-server to analyze the IoT data. The merit in processing data locally is the privacy preservation. However, to keep the model relevant to the ever-growing IoT data, it is necessary to share it with the cloud-server, which can raise certain privacy concerns. Also, it is challenging to adapt the model to the IoT data characteristics. Therefore, we propose a privacy-preserving cloud-edge paradigm incorporating personalized context learning and incremental learning strategies to adapt the trained model to the heterogeneous and dynamic IoT environment. To avoid any privacy issues, we propose a lightweight image cryptosystem parameterized to protect image contents according to an application requirement. The simulation results show that our proposed image cryptosystem has favorable encryption properties and the cipher-images are learnable by an AI model. In addition, our incremental learning strategy efficiently kept the model up-to-date, and with proposed personalized context learning strategy, the cloud-server model’s performance improved up to 13% for plain-images and 18% for cipher-images.

Channel correction plays an important role in performance of wireless communication systems. In conventional systems, channel estimation is one of the blocks at receiver side to compute channel impulse response. Various algorithms have been proposed to make them efficient and improve their performance. However, precise channel estimation incurs additional computational cost and increases complexity of the overall system. In this study, we have considered the otherwise to bypass channel estimation of an Orthogonal Frequency Division Multiplexing (OFDM) based image communication system designed to enable cloud-based deep learning (DL) computation. The simulations present performance analysis of OFDM system with and without channel correction in-terms of bit error rate (BER), and two image quality measures. Recovered image quality difference between the two systems significantly increases with higher Eb/N0. For inferencing analysis, in higher Eb/N0regions, model performance on images recovered with correction is same as on the original images while lags behind by 6% on images without correction. In lower Eb/N0regions, the model accuracy reduces by 10% on average for both systems. In addition, the model accuracy shows overlapping pattern in that region and for 3 dB, it has performed better on images recovered without correction.

Maximising the tag reading rate of a reader is one of the most important design objectives in radio frequency identification (RFID) systems as it is inversely proportional to the time required to completely read all the tags within the reader's radio field. To this end, numerous techniques have been independently suggested so far and they can be broadly categorised into pure advancements in the link‐layer tag anti‐collision protocols and pure advancements in the physical‐layer RF signal reception model. This study shows by rigorous mathematical analysis and Monte‐Carlo simulations that how those two independent approaches can be coupled to maximise the tag reading efficiency in an RFID system, considering a slotted Aloha‐based dynamic link‐layer anti‐collision protocol at tags and a multi‐packet reception capable RF reception model at the reader.

A hidden-node problem is one of the major challenges that cause severe performance degradation in wireless ad-hoc networks. Even though RTS/CTS (Request To Send/Clear To Send) exchange can remove the hidden-node problem, the inefficiency of exchanging the RTS/CTS has brought attentions to many researchers. While most of these efforts have been focused on the theoretical analysis, very little efforts were given to the verification of proposed models through real-world experiments. Random-bit error is another important factor that deteriorates the network performance in wireless environments. Through rigorous real-world experiments, this paper demonstrates a feasible methodology to enhance network throughput in the existence of hidden nodes by using optimal fragments. Our previous analytical model that incorporates the impact by random-bit error and hidden nodes on the throughput is validated under various real-world environments. These experimental results will provide a solid foundation to the research communities in solving the hidden-node problem in noisy MANET environments.

Vehicular Ad-hoc Network(VANET) enables a wireless communication between vehicles to improve driver's safety. For the safety, a message including safety-related information is broadcast between vehicles and forwarded for farther vehicles. VANET poses challenges that broadcast attempts from many vehicles cause collisions between packets and result in packet drops. Many researches have concentrated on removing collisions in those excessive broadcasts. These approaches lead one vehicle to be selected as a rebroadcast vehicle. However, it makes difficult to forward the safety message to all directions at intersections. In this paper, we suggest an efficient way to broadcast a safety message to all directions at an intersection with a short delay and without a collision. Our simulation results show a coverage improvement of as much as 70% and a delay reduction of as much as 100msec.

This paper proposes a novel contention window control scheme for backoff based CRA (Collision Resolution Algorithm). The proposed scheme demonstrates a distributed way to control random slot selection over the contention window in each contention stage with reference to normalized backoff value in the previous contention stage. Such control results an intelligent backoff time distribution over the contention window for each contention stage. Backoff time chosen for every access from such distribution potentially mitigates much collisions along with reduced channel ideal time. Analysis results show that, compared to the performance of legacy scheme incorporating DSSS (Direct Sequence Spread Spectrum) 802.11b system parameters, there is at least 13% normalized throughput enhancement with heavily reduced packet delay in an IBSS (Independent Basic Service Set) with more than 30 stations with saturated traffic.

In this paper, a contention resolution backoff algorithm termed Utility Based Backoff (UBB) is proposed for initial ranging procedure in IEEE 802.16e based WiBro (Wireless Broadband) system. In UBB algorithm, whenever an active Subscriber Station (SS) experiences a collision during ranging request transmission, it accomplishes its new contention window size based on the utility (satisfaction) upon its randomly selected backoff value on the previous transmission attempt. Higher the deferred backoff value lower will be the utility and vice versa. The contention window will be larger for higher utility and vice versa. The process is repeated unless the request is transmitted successfully, or until the maximum retransmission attempt is reached. UBB algorithm applied to WiBro reduces the average medium access delay for the successful request transmission compared to Binary Exponential Backoff (BEB). Even more, throughput for lower offered load is higher in UBB than BEB.

In this paper, we investigate two packet scheduling schemes over the downlink of DS/CDMA systems to support packet data services. These schemes are the code division multiplexing (CDX)-based packet scheduling and the time division multiplexing (TDX)-based packet scheduling. To evaluate each scheme more effectively, we derive the throughput gain of the downlink as a performance measure based on the power constraint equation. As the downlink capacity is quite related to the orthogonality factor, our analysis shows from the results that the throughput gain is highly dependent on this factor. Furthermore, it is shown that the TDX scheduling scheme in CDMA downlink affords better performances than the CDX scheduling scheme, except when the orthogonality factor is around 1.

Since the interference is related to the capacity and performance of DS/CDMA systems, it is necessary to investigate other-cell-interference characteristics according to the location of a user. In this paper, we specifically analyze the ratio of the intercell interference to the total power received from the own cell in downlink, defined as the other-cell-interference factor, as a function of normalized distance. Additionally, the maximum transmission rate under uniform user distribution throughout the cell is simulated when TDX mode packet scheduling is applied. The results show that a maximum transmission rate up to 7.6 Mbit/s for the privileged user could be supported when the bandwidth is 1.2288 MHz, orthogonality factor is 0.9 and the normalized distance is 0.1.