Lewis Nkenyereye

Sixth Generation (6G) communication networks are anticipated to advance the fifth generation technologies as well as introduce new revolutionary technologies to deliver aggressive data rates, end-to-end latency, reliability, energy efficiency, security, network capacity, and mobility requirements of the future innovative applications. To deliver these demands, ultra-fast and secure computing capability for processing a huge amount of data, application-related tasks, and resource management algorithms will be required. Many 6G applications will use advanced learning techniques that work on large data sets and their working can be made more efficient with the help of Quantum Computing (QC) instead of conventional classical computing. In this paper, we provide an overview of QC and 6G networks and highlight the benefit of integrating these two technologies. We briefly review the recent work and present a framework for QC-assisted 6G networks. We also discuss four use cases of QC in 6G networks, including quick task offloading, fast optimization algorithm results, efficient machine learning techniques, and improved security. Finally, we discuss several open challenges of using QC in the context of 6G network applications.

The limited resources available for Smart Meter (SM) devices on large-scale Smart Grid (SG) networks impose several constraints on SMs authentication. Currently, available authentication schemes are not suitable for this type of network. In particular, factors such as power and memory consumption impact the protocol efficiency and the device lifetime. Furthermore, high computational complexity leads to scalability issues in real-world scenarios, wherein large SGs need to handle a huge number of requests coming at a high rate. In this paper, we propose a lightweight authentication protocol for Smart Grids (LAP-SG), a novel scheme accounting for real resource-constrained SM providing reduced computation power, memory requirements, communication overhead, and electricity consumption. We prove the security of LAP-SG using both informal security analysis and a formal security model. We further prove the security of LAP-SG by testing it using AVISPA and ProVerif tools, showing its security against all known attacks. To assess LAP-SG performance in a real-world scenario, we measure its performance using the configuration of the Atmel family of SM devices. When compared to the state of the art, LAP-SG attains three times Smaller computation cost, reduced communication costs (up to 400 bits), and nearly four times lower storage cost.

An efficient traffic light control system (TLCS) is an integral part of monitoring the flow of autonomous vehicles (AVs) through road junctions. Existing TLCS systems have limitations, focusing on specific scenarios like emergency vehicles or considering only the queue length at intersections. Furthermore, the absence of privacy protection in these systems exposes vehicles to potential tracking risks. We propose an intuitive and privacy-preserving TLCS (IPTLCS) to improve the performance of the TLCS for multiple traffic scenarios and prevent tracking of vehicles at traffic signals. The proposed IPTLCS uses a deep Q-learning (DQN) algorithm based on the quantity versus priority concept, adaptive to all scenarios, including pedestrians and emergency vehicles, making the framework applicable to real-time situations. Further, we propose a novel anonymity preserving protocol (APP) to protect the privacy of vehicles using two-party computation (2PC) that can prevent the tracking of AVs in our environment. Extensive experimental studies of the IPTLCS reveal that the model can achieve a reduced waiting time of 3.45 s/vehicle, a queue length of 3.32 vehicles/lane, a run time of 0.002 s, and a communication overhead of 3 KB. The increase in the efficiency of the proposed IPTLCS model in comparison with existing models in terms of waiting time, queue length, run time, and communication overhead is 11.08%–43.25%, 2.35%–24.23%, 33.3%–65.5%, and 38.77%–54.38%, respectively. Impact Statement The infusion of deep learning (DL) methodologies into traffic light systems signifies a substantial leap in advancing the management of AVs on roadways. Confronting the limitations of current traffic light systems, especially in adapting to diverse scenarios with varying quantities and priorities of vehicles, this study introduces a framework for efficient AV navigation that minimizes traffic collisions with reduced delays. Incorporating the proposed privacy measures ensures vehicular data protection, enhancing the overall system’s safety. Results demonstrate that the proposed method addresses the secure management of vehicular data and achieves reduced processing times, paving the way for progressing more intelligent and secure urban mobility solutions and fostering a seamless coexistence between AVs and pedestrians.

Post COVID-19 and recent advancement in terms of language models, researchers have shown a lot of interest in analyzing social media posts for analyzing mental state of the users. Social media platforms are the epitome of sharing individual thoughts and feelings through textual posts and linguistic cues. Therefore, the textual modality from social media posts can be leveraged for detecting early signs of stress, depression or other mental health conditions, accordingly. Existing methods mainly focus on the feature engineering, shallow learning, and employing of deep learning architectures to improve the mental state recognition performance. Seldom the study uses an established knowledge-base that is available to model mentalization and emotional aspect to improving the depression and stress recognition. In this regard, we propose emotion aware contrastive networks (EAC-net) that leverages the existing knowledge-base and propose some new ones to model the emotional and mentalization aspect in order to improve the recognition of stress and depression state from textual posts. Furthermore, we propose a feature-level fusion and weighting mechanism using GRUs and self-attention layers to weight and select the important features. Lastly, the EAC-Net uses a supervised contrastive learning strategy to train the network. The proposed method is evaluated on four publicly available datasets. Experimental results reveal that the EAC-Net achieves state-of-the-art results by outperforming baselines and existing methods by atleast 1.86%, 0.72%, 3.43%, and 3.64% on four publicly available datasets using F1-measure as the evaluation metric.

Hardware implementation of new neuron models or improved conventional neuron models has made a significant contribution to neuromorphic development. One of the important factors considered to improve the conventional neuron models is to explore the impact of electromagnetic energy on neurons. In this work the efficient FPGA implementation of memristive Wilson (MW) neuron model using two approximate MW model is presented. For the first approximate MW (AMW1) model in a hybrid method, piecewise linear (PWL) and CORDIC functions have been used to provide a multiplierless and accurate model. The PWL approximation method is used to provide the second approximate MW (AMW2) model. Results of the FPGA implementation for both the MW and AMW models illustrate that, the AMW1 model with an overall saving of 79%, and the AMW2 model with an overall saving of 69% are appropriate options for large scale implementations. The average NRMSE for the AMW1 model is 0.57%, while for the AMW2 model it is 1.23%. The maximum frequency ofAMW2model is 91.5% better thanAMW1model and realizes high frequency implementation.

This study aims to present a comparative analysis of existing waveforms for integrated sensing and communication (ISAC) in vehicular environments. A novel multicarrier framework called generalized adaptive spreading modulation (GASM) is proposed for ISAC-enabled vehicular environments. The GASM waveform offers symbol spreading in both time and frequency domains with tunable spreading parameters, which allows the proposed GASM-based waveform to adapt according to the rapid time-frequency variations of the fading channel. This helps to combat the most common system impairments, such as carrier frequency offset (CFO) and symbol timing offset (STO). The GASM scheme is the generalization of various existing waveforms, such as orthogonal frequency-division multiplexing (OFDM), fractional Fourier transform-based OFDM (FrFT-based OFDM), and orthogonal chirp division multiplexing (OCDM). The proposed GASM-based ISAC system is evaluated in terms of average bit error rate (ABER) for the communication and ambiguity function (AF) for sensing capabilities. The performance of the GASM-based ISAC system is found superior as compared to the existing waveforms, i.e., OFDM, FrFT-based OFDM, OCDM, generalized frequency division multiplexing (GFDM), and orthogonal time frequency space (OTFS) modulation.

We investigate the incorporation of an intelligent reflecting surface (IRS) into cooperative spectrum-sharing cognitive radio networks (CRNs). The CRN consists of a primary user (PU) and multiple secondary users (SUs). There are two transmission phases. In the first phase, the primary transmitter is assisted by an IRS to serve the primary user (PU). This arrangement allows the primary network to allocate a part of its spectrum to the users within the secondary network. In the subsequent phase, the secondary transmitter (ST) employs a non-orthogonal multiple access (NOMA) transmission technique to simultaneously serve the PU and secondary users (SUs). By utilizing a semi-passive IRS, both data transmission to the PU and channel estimation of SUs are performed simultaneously during the first transmission phase. The main objective is to improve the weighted sum-rate of the CRN through a joint optimization of the NOMA power coefficients and IRS phase adjustments during the second transmission phase. We propose an effective algorithm that breaks down the primary sum-rate maximization problem into two sub-problems where IRS phase shifts are computed once at the beginning of the algorithm. Through simulations, we demonstrate that the proposed algorithm yields substantial gains in the sum-rate performance compared to existing methods.

The speedy proliferation of cloud storage choices has prompted consumers to choose the cloud as a preferred platform for storing their collections of images. However, this trend also invokes fundamental security issues regarding image privacy and data breach scenarios. To address these issues, consumers have recently used miscellaneous traditional image encryption techniques, primarily concentrated on preventing unauthorized access to cloud-stored images to defend the privacy of the image data. Moreover, these methods prioritize privacy preservation, they often neglect the importance of maintaining the usability of the encrypted images; hence, a Thumbnail-preserving encryption (TPE) was newly raised to preserve the thumbnail unchanged post-encryption, thereby improving the prevailing usability of the stored image. Hence, legitimate consumers can conveniently preview and control the cipher image without decryption. PwLMμ-TPE scheme greatly decreases encryption durations by approximately 17 times compared to existing methods, achieving an expansion ratio of around 2.1 for encrypted images and 1 for decrypted images, while showing SSIM and PSNR values close to 0.9 and infinity, respectively, along with an NPCR value of nearly 99%. The scheme is favourably adequate to resist cryptographic attacks and ensures usability and privacy, making it particularly suitable for cloud-based applications.