• Energy Research

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

The purpose is to study the geological resource planning and environmental impact assessments based on the geographic information system (GIS). In this study, the land resources of Yinan County in southeastern Shandong Province are taken as the research object. Based on a GIS, the current situation of land resource development is analyzed, land resource planning is carried out, and environmental impact mitigation measures are evaluated and analyzed through the environmental impact. The results obtained depict the distribution of cultivated land; the development area is 1617.31 hm2, of which 577.32 hm2 is cultivated land, 30.43 hm2 is garden land, 399.66 hm2 is forest land, 40.87 hm2 is urban and rural construction land, 10.11 hm2 is traffic water conservancy and other construction land, and 558.92 hm2 is natural reserve land. In the layout of the construction land, the development area is 841.94 hm2, of which 175.44 hm2 is cultivated land, 47.88 hm2 is garden land, 100.54 hm2 is forest land, 0.1 hm2 is other agricultural land, 90.45 hm2 is urban and rural construction land, 3.66 hm2 is traffic water conservancy and other construction land, 11.33 hm2 is water area, and 412.54 hm2 is natural reserve land. The impact of the implementation of planning on most indicators is positive and beneficial, while the impact of negative indicators is relatively small. It is revealed that the implementation of the plan has little impact on most of the ecological environment indicators. Construction and cultivated land development further improve the level of urbanization. In the process of planning implementation, corresponding measures should be taken to slow down or eliminate the negative development of the ecological environment.

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

A single technological advancement in the business sector tremendously changed customers’ lifestyles and consumption behavior. Drone technology is one of the main revolutions that increase business efficiency at a lower cost. However, the acceptance of emerging technologies is not rapid in developing markets. Therefore, this study aims to evaluate customers’ adoption of drone technology in the context of food delivery services. This study has used an extended technology acceptance model (TAM) to assess customers’ behavior. Product processing innovativeness, information processing innovativeness, and subjective norms have been added as additional constructs into TAM. The data of 354 customers from five different cities of Pakistan have been collected and analyzed through partial least square structural equation modeling (PLS-SEM). The results of the study revealed that all proposed hypotheses, except the positive influence of perceived ease of use on perceived usefulness, were accepted. Further, the results depict that perceived usefulness, subjective norms, and attitude were the major predictors of customers’ adoption of drone food delivery services. In addition to this, customers’ word of mouth has a greater influence and reach than other forms of marketing communication. Therefore, practitioners and marketers may consider hosting competition programs to experiment with drone food delivery systems to enhance the acceptance of this technology among the masses.

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.

Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with s -norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.

As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

Increasing the number of threats against software vulnerabilities and rapidly growing data breaches have become a key concern for both the IT industry and stakeholders. Developing secure software systems when there is a high demand for software products from individuals as well as the organizations is in itself a big challenge for the designers and developers. Meanwhile, adopting traditional and informal learnings to address security issues of software products has made it easier for cyber-criminals to expose software vulnerabilities. Hence, it is imperative for the security practitioners to employ a symmetric mechanism so as to achieve the desired level of software security. In this context, a decision-making approach is the most symmetrical technique to assess the security of software in security tactics perspective. Since the security tactics directly address the quality attribute concerns, this symmetric approach will be highly effective in making the software systems more secure. In this study, the authors have selected three main attributes and fifteen sub-attributes at level 1 and level 2, respectively, with ten different software of an institute as alternatives. Furthermore, this study uses a fuzzy-based symmetrical decision-making approach to assess the security of software with respect to tactics. Fuzzy Analytic Network Process (F-ANP) is applied to evaluate the weights of criteria and fuzzy-Symmetrical technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to determine impact of alternatives. The proposed symmetrical assessment in this study will be beneficial for both the designers and developers to categorize and prioritize the security attributes and understand the importance of security tactics during software development life cycle.