2690-6791ISSN (Online)
2769-786XISSN (Print)
The Malaysian government’s support for offshore wind power production has led to an increase in a few proposals. An important factor in the overall efficiency of any offshore wind farm is the site selection process, which is a multi-criteria decision-making (MCDM) task. However, classical MCDM techniques often fail to choose a suitable site because of three main challenges. First, compensation is regarded as a problem in the processing of information. Second, data usage and data leakage are often ignored in the decision-making process. Third, interaction difficulty in fuzzy environments is easily ignored. This study provides a framework for making site selection decisions for offshore wind farms while addressing the constraints. Fuzzy VIKOR is used in the second stage of the AHP process to analyze the site’s results with respect to evaluation criteria for offshore wind farms. A comprehensive index system, which incorporates the veto criteria and evaluation criteria for selecting offshore wind power station sites, is devised. Then, the system is used to transmit imprecise information to decision makers by using a triangular fuzzy set. Likelihood-based valued comparisons indicate that imprecise choice information can be correctly used, and issues of information loss can be logically avoided. A case study of Malaysia is used to demonstrate the validity and practicality of the site selection technique. This research offers a theoretical basis for accurate offshore wind power evaluation in Malaysia.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060201
Vol. 6 Issue. 2 PP. 08-21, (2022)
Futuristic sustainable computing solutions in e-healthcare applications were depends on the Internet of Things (IoT) and cloud computing (CC), has provided several features and realistic services. IoT-related medical devices gather the necessary data like recurrent transmissions in health limitations and upgrade the exactness of health limitations all inside a standard period. These data can be generated from different types of sensors in different formats. As a result, the data fusion is a big challenge to handle these IoT-based data. Moreover, IoT gadgets and medical parameters based on sensor readings are deployed for detecting diseases at the correct time beforehand attaining the rigorous state. Machine learning (ML) methods play a very significant task in determining decisions and managing a large volume of data. This manuscript offers a new Hyperparameter Tuned Deep learning Enabled Clustered IoT Based Smart Healthcare System (HPTDLEC-SHS) model. The presented HPTDLEC-SHS technique mainly focuses on the clustering of IoT devices using weighted clustering scheme and enables disease diagnosis process. At the beginning level, the HPTDLEC-SHS technique exploits min-max data normalization technique to convert the input data into compatible format. Besides, the gated recurrent unit (GRU) model is utilized to carry out the classification process. Finally, Jaya optimization algorithm (JOA) is exploited to fine tune the hyperparameters related to the GRU model. To demonstrate the enhanced performance of the HPTDLEC-SHS technique, an extensive comparative outcome highlighted its supremacy over other models.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060202
Vol. 6 Issue. 2 PP. 22-31, (2022)
The application of industrialization and urbanization strategies results in the proliferation of waste products in water resources which is a serious public challenge. They have resulted in calls for advanced technologies of water quality mitigation and monitoring, as emphasized in the sustainable development objectives. Now, environmental engineering researcher is looking for a more complex process of implementing practical assessments and of monitoring the quality of ground and surface water that is quantifiable to human beings over different locations. Many current techniques use the Internet of Things (IoT) for water quality assessment and monitoring. This paper explores the proposal of African Buffalo Optimization with Deep Belief Network for Water Quality Prediction (ABODBN-WQPR) model in an IoT environment. The presented proposed model majorly concentrates on the identification of water quality.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060203
Vol. 6 Issue. 2 PP. 32-44, (2022)
The Internet of Things (IoT) is a concept that has the potential to attract new audiences in fields as diverse as manufacturing, healthcare, and more. IoT devices included into the sensor were the primary drivers of the massive data collection. To successfully combine, assess, and comprehend all programme objects, thus, self-adaptive algorithms based on AI are necessary. The proliferation of both massive datasets and resource-intensive IoT devices makes stringent power management essential. The proliferation of both massive datasets and resource-intensive Internet of Things devices makes stringent energy management essential. Combining IoT with AI-based techniques is crucial for equitable power distribution to compact mobile devices. To this end, we offer an efficient way to communicate between power utilities and end users by forecasting future power usage over short periods of time. Innovations include a revolutionary convolutional recurrent model for lightweight prediction method with low duration intricacy and minimum margins of error, as well as massive energy administration for edge devices via a centralised cloud-based data supervisory server. To maintain the power consumption and supply paradox efficiently, the suggested scheme has mobile nodes interact with a central remote server via an IoT network and then on to the corresponding power grid. We use a number of preparation methods to accommodate the varied electrical data, and then we construct a powerful decision-making engine for quick prediction on devices with limited resources.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060204
Vol. 6 Issue. 2 PP. 45-55, (2022)
Federated learning (FL) is a recently evolved distributed learning paradigm that gains increased research attention. To alleviate privacy concerns, FL fundamentally suggests that many entities can cooperatively train the machine/deep learning model by exchanging the learning parameters instead of raw data. Nevertheless, FL still exhibits inherent privacy problems caused by exposing the users’ data based on the training gradients. Besides, the unnoticeable adjustments on inputs done by adversarial attacks pose a critical security threat leading to damaging consequences on FL. To tackle this problem, this study proposes an innovative Federated Deep Resistance (FDR) framework, to provide collaborative resistance against adversarial attacks from various sources in a Fog-assisted IIoT environment. The FDR is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures that contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The FDR mainly emphasizes convolutional networks for image recognition from the Food-101 and CIFAR-100 datasets. The empirical results have revealed that FDR outperformed the state-of-the-art adversarial attacks resistance approaches with 5% of accuracy improvements.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060205
Vol. 6 Issue. 2 PP. 56-66, (2022)
The Internet of Things (IoT) is pervasive in today's world and may be located almost throughout. It is employed in smart cities for things like highways and clinics, as well as in smart buildings for things like regulating doors and air conditioner units, avoiding fires, and many other things. The Internet of Things (IoT) refers to a set of interconnected computing devices that may communicate with one another by exchanging data over the internet. This provides the opportunity for the attacker to penetrate the IoT technologies and get the important data they contain. The restricted measure performance of IoT systems is the source of the issue, as they make it impossible to implement the conventional security mechanism on these devices. As a result of this constraint, it is necessary to propose lightweight algorithms that are capable of supporting IoT devices. However, Internet of Things (IoT) safety and confidentiality are important challenges that might impede the technology's long-term growth. In this study, we have addressed the security of the internet of things from two primary vantage points, namely, IoT design and protocols. We cover the many levels that make up the architecture of the Internet of Things (IoT), as well as the security problems that are connected with those layers and the possible alternatives to those concerns. We went through a variety of protocols that are used in the layered evolution of the Internet of Things, as well as the security mechanisms that were built for every protocol
Read MoreDoi: https://doi.org/10.54216/JISIoT.060206
Vol. 6 Issue. 2 PP. 67-78, (2022)