International Journal of Reconfigurable and Embedded Systems (IJRES)
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IoT-enabled smart cities towards green energy systems: a review
Get PDFIntegration of internet of things (IoT) in smart city management to improve various functions and living standards due to increasing population growth has dramatically evolved ubiquitous and essential services at various stages of urbanization. Hence, smart cities need to be eco-friendly by improving various sectors like education, health, and transport to provide an urban and sustainable quality of life through solving complicated green energy networks, controlling toxic pollution risks, and public safety. Linking optimized green energy systems with the production and automation of advanced applications is crucial to compose implementation strategies for smart city services. This paper aims to conduct a review on eco-friendly plans and infrastructure of IoT-enabled smart cities by exploiting green energy approaches. This study performs critical observations, ideas, and analyses of recent research in the context of our mentioned research theme. This paper points out the technical and functional challenges of an optimal performance-based green IoT-enabled smart city infrastructure. In this sense, this study organizes observations of relevant initiatives, technologies, and experiences in IoT-enabled smart cities, as well as how to embed it with green energy. Moreover, it can provide significant directions to intellectuals and authorities to develop IoT-enabled smart city applications for prospective research
Dual step hybrid routing protocol for network lifetime enhancement in WSN-IoT environment
Get PDFRecent development in internet of things (IoT) has been generating huge data due to the large number of nodes deployed and utilized for different applications. In addition, these applications utilize big data and require a more efficient mechanism for data sensing and data transmission. This research work proposes dual step hybrid routing (DSHR) protocol for efficient cluster-based routing. It comprises a two-phase algorithm, which aims at finding the optimal path considering clustering. It further comprises several processes such as cluster head selection, optimal path construction, integrating of nodes to cluster head and sensing range optimization. DSHR is evaluated considering the network lifetime; thereafter model is compared with the existing low energy adaptive clustering hierarchy (LEACH) protocol to prove the efficiency
Kernel rootkit prevention model using multiclass
Get PDFMalicious individuals can access a computer network or application thanks to a series of programmes known as rootkit malware. These kernel rootkits use covert methods to conceal the kernel components, various control frameworks, and system activities, making identifying or prohibiting their presence in the target machine challenging. The bulk of rootkit detection and prevention techniques used today are particular to a system and dependent on recognized sources, making them ineffective for growing, evolving, concealed, or unnamed rootkits. This study proposes using the kernel rootkit prevention model using multiclass (KRPMM) system to identify hash values and detect/prevent such rootkits. The file downloaded by the client, who is availing of the service, is not permitted into the node used by the client in the cloud. But, it is redirected to the node wherein the file that has been downloaded and has entered the node anew is examined by a program which is specially coded to test the presence of rootkit in the file by some mechanisms and then comes to a conclusion of either the file being malicious or the file being clean and is free of rootkits. KRPMM tested only 64 rootkits
An active two-stage class-J power amplifier design for smart grid’s 5G wireless networks
Get PDFThe wireless communication networks in the smart grid’s advanced metering infrastructure (AMI) applications need 5G technology to support large data transmission efficiently. As the 5G wireless communication network’s overall bandwidth (BW) and efficiency depend on its power amplifier (PA), in this work, a two-stage class-J power amplifier’s design methodology that operates at 3.5 GHz centre frequency by utilizing the CGH40010F model gallium nitride (GaN) transistor is presented. The proposed design methodology involves proper designing of input, output, and interstage matching networks to achieve class-J operation with improved power gain over desired BW using the advanced design system (ADS) electronic design automation (EDA) tool and estimating its integration feasibility through active element-based design approach using the Mentor Graphics EDA tool. The proposed PA provides 54% drain efficiency (D.E), 53% power added efficiency (PAE) with a small signal gain of 27 dB at 3.5 GHz and 41 dBm power output with 21 dB of improved power gain across a BW of around 400 MHz using 28 V power supply into 50 Ω load. By replacing the two-stage PA's passive elements with active elements, its layout size is estimated to be (15.5×29.2) μm2 . The results of the proposed PA exhibit its integration feasibility and suitability for the smart grid’s 5G wireless networks
Hyperelliptic curve based authentication for the internet of drones
Get PDFDrones provide an alternative progression in protection submissions since they are capable of conducting autonomous seismic investigations. Recent advancement in unmanned aerial vehicle (UAV) communication is an internet of a drone combined with 5G networks. Because of the quick utilization of rapidly progressed registering frameworks besides 5G officialdoms, the information from the user is consistently refreshed and pooled. Thus, safety or confidentiality is vital among clients, and a proficient substantiation methodology utilizing a vigorous sanctuary key. Conventional procedures ensure a few restrictions however taking care of the assault arrangements in information transmission over the internet of drones (IOD) environmental frameworks. A unique hyper elliptical curve (HEC) cryptographically based validation system is proposed to provide protected data facilities among drones. The proposed method has been compared with the existing methods in terms of packet loss rate, computational cost, and delay and thereby provides better insight into efficient and secure communication. Finally, the simulation results show that our strategy is efficient in both computation and communication
Machine learning classifiers for fall detection leveraging LoRa communication network
Get PDFToday, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology
Design of access control framework for big data as a service platform
Get PDFBig data as a service (BDaaS) platform is widely used by various organizations for handling and processing the high volume of data generated from different internet of things (IoT) devices. Data generated from these IoT devices are kept in the form of big data with the help of cloud computing technology. Researchers are putting efforts into providing a more secure and protected access environment for the data available on the cloud. In order to create a safe, distributed, and decentralised environment in the cloud, blockchain technology has emerged as a useful tool. In this research paper, we have proposed a system that uses blockchain technology as a tool to regulate data access that is provided by BDaaS platforms. We are securing the access policy of data by using a modified form of ciphertext policy-attribute based encryption (CP-ABE) technique with the help of blockchain technology. For secure data access in BDaaS, algorithms have been created using a mix of CP-ABE with blockchain technology. Proposed smart contract algorithms are implemented using Eclipse 7.0 IDE and the cloud environment has been simulated on CloudSim tool. Results of key generation time, encryption time, and decryption time has been calculated and compared with access control mechanism without blockchain technology
Smart farming based on IoT to predict conditions using machine learning
Get PDFSmart farming is a type of technology that utilizes the internet of things (IoT) to provide information on agricultural and environmental conditions as well as perform automation. Some of these ecological conditions can be used and analyzed in machine learning (ML) data management. This study focuses on utilizing ML algorithms to find the best prediction; typically used methods include linear regression, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). In the application of smart farming, research on IoT and artificial intelligence (AI) is still uncommon since most IoT cannot make predictions like AI. Because basically, some IoT can't make predictions as AI does. In this Study, predictions were made by looking at the regression results in the form of root mean square error (RMSE) and absolute error. The results show a strong and weak correlation between features (positive or negative). The best prediction results are obtained by XGBoost when predicting temperature (RMSE 6.656 and absolute error 3.948) and (soil moisture 17.151 and absolute error 11.269). However, using different parameters (RMSE RF and absolute error DT) on RF and DT resulted in good and distinct results. Linear regression, on the other hand, produced unsatisfactory and poor result
A novel smart irrigation framework with timing allocation using solenoid valves and Arduino microcontroller
Get PDFIrrigation in agriculture is the most common way of providing water to agricultural land or fields at normal stretches through channels and embedded platforms with the internet of things (IoT), to upgrade rural development. In this paper, the arrangement of the various types of irrigation systems and embedded platforms for agriculture was studied. The embedded platform can be designed in a suitable framework that can assist the irrigation system in growing more water-required crops. In this work, three relay switches, two solenoid valves, and one water pump source were connected to Arduino ESP32. The free version of Sinric Google Cloud was utilized significantly to control three devices namely, two solenoid valves using two relay switches and a water pump source using one relay switch. The experiment was executed in a prototype manner with timing allocation by considering two agricultural fields where water was supplied either in one field at a time and showed more prominent results to save time, replacement of manual valves, man intervention, power, and suitable quantity of water for more water-required crops namely, arecanut and coconut
Video saliency detection using modified high efficiency video coding and background modelling
Get PDFVideo saliency has a profound effect on our lives with its compression efficiency and precision. There have been several types of research done on image saliency but not on video saliency. This paper proposes a modified high efficiency video coding (HEVC) algorithm with background modelling and the implication of classification into coding blocks. This solution first employs the G-picture in the fourth frame as a long-term reference and then it is quantized based on the algorithm that segregates using the background features of the image. Then coding blocks are introduced to decrease the complexity of the HEVC code, reduce time consumption and overall speed up the process of saliency. The solution is experimented upon with the dynamic human fixation 1K (DHF1K) dataset and compared with several other state-of-the-art saliency methods to showcase the reliability and efficiency of the proposed solution