• Energy Research

Because of the metaphysical shift in the technological era, the world is witnessing machine to machine (M2M) communication. Sensors are omnipresent, owing to which wireless sensor networks (WSNs) are developing as a key enabling technology for the Internet of Things (IoT). M2M interaction poses a severe threat to the lifetime of the network. Clustering plays an essential role in elevating the energy efficiency of the WSN. Particularly, uniform distribution of cluster heads (CHs) is crucial for achieving better network lifetime and uninterrupted sensing from the area under observation. For this, we propose an efficient two-fold clustering algorithm, namely second-fold clustering (SFC). In the first phase, the selection of CH is made based on zonal residual energy and zonal degree of connectivity. In the second phase, the leftover nodes known as isolated nodes are clumped. CH is selected based on the zonal degree of connectivity to ensure that energy is disbursed (almost) uniformly by all the nodes. This uniformity makes it suitable for integration with the IoT. To find out the efficacy of SFC, carried rigorous experimental study out where it is compared to two existing clustering algorithms. The simulation results show that SFC outperforms its counterparts. The results showed that SFC improved network lifetime by approximately 9.6% and 10.2% compared to regional energy-aware clustering scheme and Residual-Low Energy Adaptive Clustering Hierarchy (R-LEACH), respectively.

Nowadays, the usage of renewable energy resources (RER) is growing rapidly, but at the same time, the effective utilization of RER is also a challenging task. For the better usage of RER and the reduction of loss, the dual battery storage is proposed. The main aim of this work is to focus on the design and implementation of a reliable and renewable power generating system under a robust situation, along with a battery storage system. The perturb and observe (P&O) maximum power point tracking (MPPT) technique has been applied to improve the solar photovoltaic power production. In addition, the dual battery storage system is being introduced to improve the life cycle of the primary storage system. The proposed dual storage system is highly preferable for remote, location-based application systems, space applications and military operations. In the dual battery storage system, the batteries are working effectively with a good lifespan, when compared with the existing methods. To determine the state of charge (SOC) and depth of discharge (DOC), those batteries’ input charging and discharging levels were monitored closely. MATLAB Simulink (R2013) is used for simulation; finally, a real-time, three-phase inverter was designed and validated. Under this dual battery storage mode, the life time of battery is improved.

Water being one of the foremost needs for human survival, conservation, and management of the resource must be given ultimate significance. Water demand has increased tremendously all over the world from the past decade due to urbanization, climatic change, and ineffective management of water. The advancement in sensor and wireless communication technology encourages implementing the IoT in a wide range. In this study, an IoT-based architecture is proposed and implemented for monitoring the level and quality of water in a domestic water tank with customized hardware based on 2.4 GHz radiofrequency (RF) communication. Moreover, the ESP 8266 Wi-Fi module-based upper tank monitoring of the proposed architecture encourages provide real-time information about the tank through internet protocol (IP). The customized hardware is designed and evaluated in the Proteus simulation environment. The calibration of the pH sensor and ultrasonic value is carried out for setting the actual value in the prototype for obtaining the error-free value. The customized hardware that is developed for monitoring the level and quality of water is implemented. The real-time visualization and monitoring of the water tank are realized with the cloud-enabled Virtuino app.

Currently, a smart city is an emerging field in urban cities to improve the quality of life through information and communication technology (ICT). In general, the traditional solid waste management (SWM) approach taken by municipal authorities for waste collection in urban areas must be enhanced to achieve the green and smart city goals. This article is primarily focused on the progress of ICT technologies in solid waste management. With that aim, a thorough analysis is carried out in the article, and from the analysis, we have identified distinct ICT technologies that have been implemented in SWM. The function, application, and limitations of each technology are presented in the article. From the review, it is concluded that the implementation of the Internet of Things (IoT) plays a significant role in minimizing the negative impact of waste on the environment. It is also identified that selection of the appropriate wireless communication protocol is critical during the implementation of IoT-based system because the sensor node at the bins is battery-powered. In addition, it is analysed that blockchain technology plays an essential role in realizing the waste–money model, as this model includes transactions between users and recyclers. Finally, in this article, we propose that the waste-to-money model, local network and gateway architecture, vision node, and customized prototype improve solid waste management system in terms of communication, energy consumption, and real-time monitoring.

Abstract Load forecasting plays an essential role in effective energy planning and distribution in a smart grid. However, due to the unpredictable and non-linear structure of smart grids and large datasets' complex nature, accurate load forecasting is still challenging. Statistical techniques are being used for a long time for load forecasting, but it is inefficient. This paper tries to resolve challenges imposed by conventional methods like mean and mode by suggesting an ANN model for accurate load forecasting. Specifically, the LSTM and random forest approach has been used here. We compared our model to other models that use similar parameters and found that ours is more reliable and can be used for long-term forecasting. Our model has achieved an average overall accuracy of 96% and an average MSE of 4.486 with average CPU time consumption of 904.47 s, 872.43 s, and 908.32 s, respectively. Hence, the present model outperforms other existing methods.

The rapid development of technology has empowered us to achieve resilient infrastructure to establish a sustainable ecosystem. The construction site is one of the highest risk jobs for accident-related fatalities and injuries globally. From the previous studies, it is concluded that untrained or inexperienced workers were responsible for 40% of work-related accidents and the Health and Safety Executive (HSE) report concludes that inadequate working experience, knowledge, and safety awareness were the key causes of fatal accidents in the construction industry. Moreover, it is identified from previous studies that digital technology such as IoT with the assistance of wireless sensors can enhance the safety of construction sites. Based on this advantage, this study has implemented the hybrid architecture with the integration of the 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi communication protocol to monitor the health status of workers and construction sites and also to identify workers’ equipment wearing status in real-time scenarios. The proposed architecture is realized by implementing customized hardware, based on 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi. Furthermore, in the analysis of the evaluation metrics of LoRa, it is concluded that the lowest sensitivity is observed for SF 12 at BW 41.7 kHz and the highest is observed for SF 7 at BW 500 kHz; the maximum value data rate is observed at BW 500 kHz at CR 1 for SF 7, and the minimum data rate is observed at BW 41.7 at CR 4 for SF 12. In the future, the customized hardware will be implemented in different construction environments resolving possible challenges that empower to implementation of the proposed architecture in wide extensions.

AbstractFog computing is an emerging research domain to provide computational services such as data transmission, application processing and storage mechanism. Fog computing consists of a set of fog server machines used to communicate with the mobile user in the edge network. Fog is introduced in cloud computing to meet data and communication needs for Internet of Things (IoT) devices. However, the vital challenges in this system are job scheduling, which is solved by examining the makespan, minimizing energy depletion and proper resource allocation. In this paper, we introduced a reinforced strategy Dynamic Opposition Learning based Social Spider Optimization (DOLSSO) Algorithm to enhance individual superiority and schedule workflow in Fog computing. The extensive experiments were conducted using the FogSim simulator to generate the dataset and an energy-efficient open-source tool utilized to model and simulate resource management in fog computing. The performance of the formulated model is ratified using two test cases. The proposed algorithm attained the optimized schedule with minimized cost function concerning the CPU processing period and assigned memory. Our simulation outcomes show the efficacy of the introduced technique in handling job scheduling issues, and the results are contrasted with five existing metaheuristic techniques. The results show that the proposed method achieves 10% - 15% better CPU utilization and 5%-10% less energy consumption than the other techniques.

Vehicle identification and classification are some of the major tasks in the areas of toll management and traffic management, where these smart transportation systems are implemented by integrating various information communication technologies and multiple types of hardware. The currently shifting era toward artificial intelligence has also motivated the implementation of vehicle identification and classification using AI-based techniques, such as machine learning, artificial neural network and deep learning. In this research, we used the deep learning YOLOv3 algorithm and trained it on a custom dataset of vehicles that included different vehicle classes as per the Indian Government’s recommendation to implement the automatic vehicle identification and classification for use in the toll management system deployed at toll plazas. For faster processing of the test videos, the frames were saved at a certain interval and then the saved frames were passed through the algorithm. Apart from toll plazas, we also tested the algorithm for vehicle identification and classification on highways and urban areas. Implementing automatic vehicle identification and classification using traditional techniques is a highly proprietary endeavor. Since YOLOv3 is an open-standard-based algorithm, it paves the way to developing sustainable solutions in the area of smart transportation.