The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world of intelligent networks, and they essentially use a wireless sensor network (WSN) as a perception layer to collect the intended data. This data is processed as information and send to cloud servers through a base station, the challenge here is the consumption of minimum energy for processing and communication. The dynamic formation of cluster heads and energy aware clustering schemes help in improving the lifetime of WSNs. In recent years, grey wolf optimization (GWO) became the most popular feature selection optimizing, swarm intelligent, and robust metaheuristics algorithm that gives competitive results with impressive characteristics. In spite of several studies in the literature to enhance the performance of the GWO algorithm, there is a need for further improvements in terms of feature selection, accuracy, and execution time. In this paper, we have proposed an energy-efficient cluster head selection using an improved version of the GWO (EECHIGWO) algorithm to alleviate the imbalance between exploitation and exploration, lack of population diversity, and premature convergence of the basic GWO algorithm. The primary goal of this paper is to enhance the energy efficiency, average throughput, network stability, and the network lifetime in WSNs with an optimal selection of cluster heads using the EECHIGWO algorithm. It considers sink distance, residual energy, cluster head balancing factor, and average intra-cluster distance as the parameters in selecting the cluster head. The proposed EECHIGWO-based clustering protocol has been tested in terms of the number of dead nodes, energy consumption, number of operating rounds, and the average throughput. The simulation results have confirmed the optimal selection of cluster heads with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by using minimum energy levels in WSNs. Using the proposed algorithm, there is an improvement in network stability of 169.29%, 19.03%, 253.73%, 307.89%, and 333.51% compared to the SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, respectively.

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works.

The purpose of developing a Student Monitoring System that incorporates AI and Digital Image Processing is to automate the process of logging attendance, making it easier and more accurate to identify each student in the classroom or laboratory. The Student Monitoring System is built on a Raspberry Pi, which serves as the Central Processing Unit (CPU) of the system and stores current attendance data (logs) in a database created in conjunction with the RPI. An additional component of this system is an AI Vision Machine, which is used to identify each student's face when they attend class and is capable of providing audio announcements to students in class. Thus, the system will assist in eliminating the manual attendance logging method, resulting in a significant reduction of errors associated with this type of recording, saving time for both the student and the instructor. The Student Monitoring System allows the various components (AI algorithms, RPI, and database) used to identify students and the associated components of the system to work together with the latest data available for ongoing updates and monitoring. This solution was designed specifically for educational facilities to improve their attendance management processes and provide an enhanced student experience in a technology-driven campus environment.

Toward increasing the data rates further, Teraherk (THz) frequency bands (0.1 -10 THz) became the central aspect of sixth-generation (6G) networks that support massive connectivity, low-latency, highly secure communications, data rates [hundreds of gigabytes per second (Gbps)], and denser networks. THz transmission is a complementary wireless technology for high-speed wireless extension of the optical fibers for 6G. The availability of materials for antenna fabrication and feasibility in designing ultra-massive multi-input-multi-output (U-mMIMO) systems motivates one to explore its peak performance at THz frequency bands. In this paper, a multiuser U-mMIMO system was designed with hybrid beam-forming features and its performance was tested at 0.142 THz bands for different numbers of base station antennas and modulation orders. Each user had one or more number of independent data streams, and the total number of users considered in the multiuser U-mMIMO system were 4 and 8. The metrics we considered in evaluating the performance were error vector magnitude, antenna array radiation patterns, and equalized symbol constellations. The results of simulations strongly recommend exploring the THz frequency bands for building next-generation wireless systems that satisfy the needs of the near future.

With the continuous prevalence of wireless sensor network (WSN) applications in the recent days, localization of sensor nodes became an important aspect in research in terms of its accuracy, communication overhead and computational complexity. Localization plays an important role in location sensitive applications like object tracking, nuclear attacks, biological attacks, fire detection, traffic monitoring systems, intruder detections, and finding survivors in post-disasters, etc. The objective of localization is to identify the coordinates of target nodes using information provided by anchor nodes. Precision improvement of the sensor node positions is a key issue for an effective data transmission between sensor nodes and save the node’s energy as well as enhance the network lifetime. In this article, a cost-effective localization algorithm with minimal number of anchor nodes is proposed that uses nature inspired optimization techniques to enhance the localization accuracy compared to the state-of-the-art localization algorithms. The performance metrics considered for simulations and comparison with the existing algorithms include average localization accuracy, communication range, and the number of anchor nodes. The simulation results prove that the proposed gaussian-newton localization through multilateration algorithm (GNLMA) enhances the mean localization accuracy to 92.8% and the range measurement error is limited to 1.22meters. Depending on the communication range of sensor nodes, the average localization accuracy is achieved up to 94.4% using the proposed GNLMA.