Driven by rapid advancements in interconnection, packaging, integration, and computing technologies, parallel and distributed systems have significantly evolved in recent years. These systems have become essential for addressing modern computational demands, offering enhanced processing power, scalability, and resource efficiency. This paper provides a comprehensive overview of parallel and distributed systems, exploring their interrelationships, their key distinctions, and the emerging trends shaping their evolution. We analyse four parallel computing paradigms—heterogeneous computing, quantum computing, neuromorphic computing, and optical computing—and examine emerging distributed systems such as blockchain, serverless computing, and cloud-native architectures. The associated challenges are highlighted, and potential future directions are outlined. This work serves as a valuable resource for researchers and practitioners aiming to stay informed about trends in parallel and distributed computing while understanding the challenges and future developments in the field.

Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market.

As the demand for high-speed data transmission grows with the expected emergence of 6G networks and the proliferation of wireless devices, more than traditional wireless infrastructure may be required. Small cell networks (ScNs) integrated with reconfigurable intelligent surfaces (RISs) and multiple-input-multiple-output (MIMO) have emerged as promising solutions to address this issue. However, ScNs have resource allocation limitations, and traditional RISs can only reflect signals in a limited propagation space of $180^{0}$ with fixed reflection properties. This paper proposes a novel approach to overcome these challenges by introducing actively simultaneously transmitting and reflecting (ASTAR)-RISs. Unlike conventional RIS, ASTAR-RISs actively amplify and transmit signals, effectively mitigating the limited propagation challenge and improving signal strength, especially in dense ScNs. This approach enhances the quality of service in complex channel environments by amplifying, on top of reflection, from the macro base station (mBS), improving the overall signal strength, and providing $360^{0}$ flexible propagation space. Furthermore, ASTAR-RIS enables dynamic beam management, significantly improving signal coverage and interference management, which are crucial in dense deployments. In this work, we propose a network architecture where distributed ASTAR-RIS units are deployed to assist small cell mBSs by optimizing signal coverage and enhancing communication performance. ASTAR-RISs dynamically control signal reflection and amplification, complementing the functionality of traditional small-cell BSs in dense network environments. Using the MIMO technique, we design phase shifts for ASTAR elements and develop optimal hybrid beamforming for users at the mBS. We dynamically control the ON/OFF status of the ASTAR-RIS based on active or idle status. We propose an efficient model that ensures fairness of signal-to-noise ratio (SNR) for all users and minimizes overall power consumption while meeting user SNR and phase shift constraints. To this end, we integrate robust beamforming and power allocation strategies, ensuring the system maintains reliable performance even under imperfect channel state information (CSI). We formulate a max-min optimization problem that optimizes the SNR and power consumption, subject to the ON/OFF status, phase shift, and power budget of the ASTAR-RIS. Our proposed method uses an alternating optimization algorithm to optimize the phase shift matrix at the ASTAR-RIS and the hybrid beamforming at the mBS. The approach includes two transmission schemes, and the phase optimization problem is solved using a successive convex approximation method that offers a closed-form solution at each step. Additionally, we use the dual method to determine the optimal ON/OFF status of the ASTAR-RIS. Comprehensive simulations validate the robustness and scalability of our proposed solution, particularly under varying network densities and CSI uncertainties. provides significant performance improvements over 170% compared to traditional RIS schemes.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms.