Smart LED streetlight system is one of the enabling technologies for a smart city, giving low-cost, low power outdoor lighting also with benefits for vehicle users as well as pedestrians. Integration of sensors and ZigBee-based wireless sensor modules can furnish an optimal platform for an innovative LED streetlight application. Psychological studies suggest that a different level of color temperature can significantly affect human circadian rhythm. For this reason, correlated color temperature (CCT)-based illumination gives a significant lighting performance both in terms of energy efficiency and in overcoming traffic accidents in low visibility areas. Previous works usually assume only specific platform and did not consider CCT-based illumination toward smart LED streetlight system. In this paper, we consider the importance of CCT-based illumination and propose a novel integration of public weather data awareness, ZigBee-based wireless communication, and dynamic web-based management system for the state-of-art of smart LED streetlight system applicable to smart city. In particular, we design a central web server that can receive weather information and real-time sensor data from each LED streetlights and provides a dynamic and flexible web interface for authorized users. Furthermore, real-time implementation of the proposed system shows perfect transmission-reception parameters, such as throughput and signal strength among the different LED streetlights, which fulfills the wireless communication range and signal quality between each LED streetlights.

The paper presents a modified hybrid particle swarm optimization with bat algorithm parameter inspired acceleration coefficients (MHPSO-BAAC) without and with the constriction factor to find the optimal solution of the economic dispatch problems (EDPs) incorporating conventional as well as hybrid and renewable energy sources (RESs) based plants. The algorithm is designed by modifying the recently presented hybrid PSO and BA (HPSOBA) algorithm applied for the achievement of the optimal solution of the EDPs. The modified algorithm is implemented to solve EDPs of all RESs-based power systems for three scenarios, without constraints, with time-varying demand, and with the consideration of regional load sharing dispatch (RLSD). The performance of the algorithm is also verified through the implementation of various combinations of hybrid as well as thermal power plants (TPPs). The case of TPPs consists of three different scenarios: 1) a small-scale system with constraints like ramp-rate limits (RRLs), prohibited operating zones (POZs), and power losses; 2) a medium-scale power system with consideration of emission-economic dispatch (EED); 3) a large-scale power system with valve-point loading (VPL) effect. The results of the designed MHPSO-BAAC algorithm are compared with the various metaheuristic algorithms available in the literature and the comparative analysis shows the superior performance of the developed algorithm in terms of fuel cost reduction, fast convergence, and computational time.

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.

Irregular repetition slotted ALOHA (IRSA) based non-orthogonal multiple access (NOMA) with priority adaptive traffic load (ATL) (PATL-IRSA-NOMA) is proposed to prioritize the performance of emergency devices in 6G enabled massive Internet of Things. In PATL-IRSA-NOMA, emergency devices (ED) and regular devices (RD) are coexisting and differentiated by access probability before sending packets, where RD performing traffic load estimation and applying ATL to access the channel. Different from conventional ATL that is performed by all users/devices, priority ATL in PATL-IRSA-NOMA is only performed by RD and reduces the contention of RD in IRSA-NOMA channel. The superior of PATL-IRSA-NOMA over conventional IRSA-NOMA is shown in terms of packet loss rate and throughput for both ED and RD groups.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of $256\times 256$ pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.