International Journal of Sensors Wireless Communications and Control - Volume 12, Issue 9, 2022

Background: With the advancements of ubiquitous computing, wireless sensor networks, and machine-to-machine communication, the Internet of Things (IoT) has become a constantly growing concept. The IoT is a new paradigm that interconnects all smart physical devices to provide smart services to users. It effectively delivers user-required services by utilising internet connectivity, sensors, and various technologies and protocols for the analysis and collection of data. IoT is predicted to permeate practically every facet of daily life, from smart cities to health care, smart agriculture, logistics and retail, and even smart living and smart ecosystems. Since IoT systems are comprised of heterogeneous hardware and networking technologies, integrating them to the software/ application level to extract information from massive amounts of data is a difficult task. Methods: In this survey, the definitions, elements, working, architecture, fundamental technologies, key challenges, and potential applications of IoT are systematically reviewed. Initially, the various definitions and elements of IoT are introduced, followed by an explanation of how an IoT works. Additionally, an outline of IoT in the context of the architecture is presented. The primary enabling technologies that will drive IoT research in the near future are examined in this paper. Furthermore, the major key challenges that the research community must address, as well as potential solutions, are investigated. Finally, the paper concludes with some potential IoT applications to demonstrate the concept's feasibility in real-world scenarios. Conclusion: The goal of this survey is to assist future researchers in identifying IoT-specific challenges and selecting appropriate technology based on application requirements.

Background: There has been an increasing interest in the research community regarding the development of new energy harvesting systems/architectures for sensor networks deployed at critical locations. Various types of energy harvesting techniques like solar, thermal, aquatic, and wind energy harvesting systems are popular in the research community. It has been found in a survey that a single energy harvesting technique is not enough for the wireless sensor network, especially when the nodes are deployed in critical areas, like volcanoes, underwater, ocean, rivers, etc. Objective: This study aimed to explore energy solutions for perpetual, battery-less, and critical places where human intervention is impossible. Methods: In this study, a hybrid energy harvesting solution using solar, pressure, and thermal has been proposed. An optimized framework has been proposed, implemented, and analyzed for the underwater sensor network application. Furthermore, mechanical and electrical schematic models have been designed, implemented, and realized. Results and Discussion: The physical parameters of solar, thermal, and piezoelectrical transducers have been analyzed along with mathematical equations to find the best possible solutions for the optimized framework. Conclusion: The model was theoretically implemented and investigated, and it was found that 22.3KJ of energy can be extracted in 24hrs from the proposed design, which guarantees a perpetual life of the sensor node.

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Methods: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Results & Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

Background and Objective: The deployment of a Wireless Sensor Network (WSN) provides a useful aid for monitoring greenhouse-like environments. WSN helps in achieving precision agriculture i.e. more yield can be produced with precise inputs. Before the deployment of a sensor network, it is necessary to explore the communication range of nodes. Communication signals are affected by losses due to stems, fruits, twigs, leaves, infrastructure material, etc. in a greenhouse. So as part of the deployment strategy, signal assessment is required in the greenhouse. Methods: This research work proposes a Machine Learning (ML) based signal assessment for the evaluation of WSN deployment in different structures of a tomato greenhouse. Signal strength is measured for a naturally ventilated greenhouse and a fan-pad ventilated greenhouse. Measurements for the naturally ventilated greenhouse are considered with two case scenarios i.e. with transmitter and receiver in the same lane and with transmitter and receiver in different lanes. Models are developed for measured values and evaluated in terms of correlation and error between measured and model formulated values. Results and Conclusion: For the naturally ventilated greenhouse case scenario 1, correlation increases from 91.83% to 95.42% as the degree increases from 2 to 7. Correlation for naturally ventilated greenhouse case scenario 2 rises from 72.51% at degree 2 to 90.09% at degree 10. For the fan-pad ventilated greenhouse, the model has a more complex fitting because of the spatial variability within the greenhouse. Correlation of the model increases from 79.39% to 84.06 % with an increase in degree from 2 to 11. For the naturally ventilated greenhouse, better correlation is achieved at lower degrees compared to the fan-pad ventilated greenhouse.