IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in such networks suffer from the perturbing concern of limited energy resources, restricting their potential in the successful detection of wildfire. To extenuate this concern, we propose an intelligent framework, Sleep scheduling-based Energy Optimized Framework (SEOF), that works in two folds. Firstly, we propose an energy-efficient Cluster Head (CH) selection employing a recently developed meta-heuristic method, Tunicate Swarm Algorithm (TSA), that optimizes the five novel fitness parameters by integrating them into its weighted fitness function. Secondly, we perform a sleep scheduling of closely-located sensor nodes based on the distance threshold calculated through a set of experiments. Sleep scheduling methodology plays a pivotal role in abating the number of data transmissions in SEOF. Finally, we simulate SEOF in MATLAB under different scenarios to examine its efficacy for the various performance metrics and scalability features. Our empirical results prove that SEOF has ameliorated the network stability period for two different scenarios of network parameters by 35.3% and 216% vis-à-vis CIRP.

Both unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) have gradually become promising technologies for the fifth generation (5G) driven green Internet-of-Things (IoT) networks on account of their unique advantages of massive connections, higher spectral efficiency and flexibility. Motivated by this, we propose a 3-hop NOMA UAV-aided green communication network framework, where UAVs serve as aerial relays to support two groups of ground users. A stochastic geometry approach is invoked to model the spatial positions of the two group users. Under the realistic assumption, imperfect successive interference cancelation (ipSIC) is considered. To evaluate the performance of the proposed framework, theoretical expressions are derived to facilitate the outage performance evaluation of the far user (FU) and the near user (NU). Moreover, the asymptotic behaviors for the outage probability (OP) of both the FU and the NU in the high signal-to-noise ratio (SNR) regime are explored by obtaining diversity orders. Finally, the system throughputs under the delay-limited transmission mode are investigated. Numerical results confirm that: 1) For uplink transmission, there exist outage floors for the OP of both ipSIC and perfect SIC (pSIC) due to interference from the NU; 2) For downlink transmission, an outage floor exists for the OP of the NU under the condition of ipSIC; 3) For uplink NOMA/orthogonal multiple access (OMA) transmission, the outage performances of both the FU and the NU with NOMA outperform OMA in the low SNRs, while OMA has better performance in the high SNR regime; 4) For downlink NOMA/OMA, the outage performances of both the FU and the NU under pSIC outperform OMA.

Vehicles play a vital role in modern-day intelligent transportation systems (ITS). Plate characters provide a standard means of identification for any vehicle. To serve this purpose, an automatic license plate recognition system is studied. In this paper, we intend to create an optimized algorithm for implementing the scheme. Firstly, we undertake several challenging stages. The first step is introduced as the determination of plate location. Then, in the second phase, we apply an initial improvement to decline the likely noises using the Gaussian function to provide an appropriate filter for this target. Next, the rest of the project is organized as follows, finding the edge of images, enhancing modified pictures, and selecting the exact place of the plate. Afterward, tilting and plate rotation improvement and plate characters' extraction are considered two essential steps in this regard. Eventually, the final step of this project consists of several stages, such as employing a neural network to extract the plate characters automatically.

The initiative for solar-driven hydrogen and oxygen production for cooking and medical applications is designed to revolutionize our approach to energy-intensive activities. This project integrates three crucial components: solar power generation, hydrogen and oxygen gas separation and storage, and the application of these gases for cooking and medical purposes. Through the use of solar panels, the project captures sunlight and converts it into electrical energy, fundamentally departing from conventional energy sources with its clean and renewable approach. The electrolyzer, powered by the harvested solar energy, then facilitates the separation of water into hydrogen and oxygen gasses, both stored meticulously in separate containers with stringent safety measures in place. The innovative aspect lies in the utilization of these clean gasses as a sustainable fuel source for cooking and medical applications. When these gasses combust, they recombine to form water vapour, releasing energy without the emission of harmful by-products. By emphasizing clean energy practices, this project significantly diminishes the carbon footprint associated with traditional cooking methods, offering a promising and ecofriendly alternative for households and communities. The success of this solar-driven hydrogen and oxygen production initiative holds the potential to transform our energy landscape, making strides towards a more sustainable and environmentally conscious future.