Journal of Intelligent Systems and Internet of Things

The confidentiality of electronic medical records (E-Medical records) is of the utmost importance. Consequently, healthcare companies are responsible for ensuring their patients' medical records' privacy, security, and service management. Innovative agreements will ensure patient satisfaction for management. The study's primary goals are to enhance data security service management and reduce the amount of external involvement with healthcare data. This study explores a novel approach to improve the security and confidentiality of e-medical information by examining the feasibility of utilizing the blockchain system within the context of the IoMT (Internet of Medical Things). The medical care management platform uses blockchain technology to manage e-health records effectively. This paper presents a paradigm for e-medical record services based on IoMT resources, which integrates blockchain technology with Secure Federated Learning (BT-SFL-IoMT). The data is stored on blockchain, and predictions and analyses are made using secure federated learning. Hyper ledger Analyzer is used to assess the latency and speed of blockchain transactions and capture access activity and authorization events. As verified by the results, the functionality is resistant to unauthorized retrievals and fits the needs of real-world settings while securing e-medical records. Many metrics, including testing accuracy of federated learning, Convergence speed, and Performance analysis of the proposed model, demonstrate its efficient use in secure databases.

 Many data resources and network availability are needed for the smart city applications to execute at their highest efficiency level Many interconnected devices in a smart city produce vast quantities of data and will likely have new uses in the near future. In smart cities, the Internet of Things (IoT) and 5G beyond networks provide dependable, large-scale data exchange and communication. A new intelligent ecosystem is the goal of 5G, and the technology that will make it possible is the next-gen networking technologies. The drawback of smart devices is their limited computational capability. Adding in-network caching into information-centric edge networks allows them to overcome this obstacle. Hence, this study suggests an Adaptive Information-Centric Network based on Edge Computing Framework (AICN-ECF) to reduce data traffic and latency with high security in smart cities. Integrating EC and ICN allows content distribution to be handled quickly, improving user experience. This study provides an ICN-based edge caching system with four cache attributes for managing large multimedia data traffic in smart cities built on the Internet of Things. At the base station (BS) application layer, there is support for ICN and device-to-device (D2D) communication, which allows for caching of requested material at the network's edge. This layered design is the first step in the process. Secondly, to facilitate efficient caching, a selection has been offered to cache contents at network nodes in a layered network design, considering a variety of centrality indicators. Finally, this study provides a method for caching material close to the delivery path in ICN network layers, allowing for rapid content distribution by using near-path caching. The experimental findings demonstrate that the suggested AICN-ECF model increases the cache hit ratio of 98.7%, content retrieval time of 97.8%, data security ratio of 96.5%, data transmission ratio of 95.6% and delay ratio of 11.2% compared to other popular models.

Artificial Intelligence of Things (AIoT) is a term used to describe the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. AIoT combines the capabilities of AI algorithms with the data generated by IoT devices to enable real-time decision-making and automation of various processes. Smart buildings refers to a type of building that utilizes advanced technologies to improve its efficiency, performance, and functionality of indoor tasks in a way that provide a safe and comfortable environment for occupants. This paper provides an overview of the research literature on AIoT technologies that is contribute to the development of smart buildings and their functionality. We discuss the benefits of AIoT empowered smart buildings, which include reduced energy consumption and costs, improved occupant comfort and productivity, and increased safety and security. we also discusses the challenges associated with the deployment of AIoT in smart buildings, including data privacy and security concerns, interoperability issues, and the need for specialized expertise. Further,  we discuss the promising areas of future research that pave the way for further research on AIoT empowered smart buildings. We concludes our work with a discussion of the potential for AIoT empowered smart buildings to contribute to the sustainability of cities and improve the quality of life for their occupants.

As AI-based smart homes become increasingly popular, there is a need to better understand the benefits and challenges of this emerging technology. A survey on AI-based smart homes can provide valuable insights into user needs, adoption rates, user satisfaction, barriers to adoption, and opportunities for innovation. This research overviews the cutting-edge literature on smart home development with an emphasis on the utilization of artificial intelligence (AI) approaches in this application area. We begin with a review of AI technologies and the smart home necessities needed to implement AI. Then, we introduce several applications of AI for smart homes and describe the most popular approaches already present in literary works. The open Issues (e.g., security and privacy, data collection and sharing, data analytics, and latency) meeting the development of smart homes are also discussed in this work. Finally, the paper suggests some directions for future study that could be fruitful.

Smart Sensor Networks (SSNs) are an indispensable part of the Industrial Internet of Things (IIoT), which seeks to improve efficiency, productivity, and safety in different industrial applications. SSNs consist of a large number of sensors, regularly deployed in a wireless ad-hoc network, which communicates with each other and with other devices, such as gateways and servers. Nevertheless, the building of SSNs in IIoT environments encounters many challenges, such as trust management, data reliability, privacy, and security. These challenges necessitate proposing novel solutions and protocols, to provide a reliable, secure, and efficient SSN. To this end, this study presents a novel DL system that can effectively discriminate between normal traffics and malicious traffic in SSNs. A convolutional feature extractor is developed to learn important discriminative features necessary for the early detection of security threats in SSNs. Then, an improved LSTM (ILSTM) is presented to model the temporal dynamics of the SSNs flows, which helps model long interdependency between traffic samples. A focal loss function is applied to deal with the imbalance between class samples. Experimental analysis is performed on an open-source SSN security dataset, named WSN-DS, the findings demonstrated the competitive advantages of our system over the prevailing solutions.

Our ability to align with the trend of innovations in science and technology will not only emancipate ignorance but also unfold our ability to evaluate, understand and predict possibilities in our society, environment, and the world at large. Radar system technology gives us the privilege to achieve the above-mentioned fact. The word Radar is an acronym for Radio Detection and Ranging. It is a mean of getting information about a distant target, by sending electromagnetic waves to them and analyzing the echoes from the target to generate relevant reports about the target. In this paper, we will focus on some metrics and the effect of changes in them on the performance of the radar system using the MATLAB Radar Designer.

The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models.

The continuous improvements in the Internet of Things (IoTs) and machine learning (ML) make them the key enabling technologies for intelligent traffic management (ITM).The ability to accurately predict network traffic has been demonstrated as crucial for effective network management and strategic planning. Proactive management of future congestion incidents requires access to reliable long-term forecasting models. Conventional prediction methods often fail to completely capture the spatiotemporal features of the traffic flows because of the complexity of the interdependence between the flows. To this end, we proposed to improve the management of traffic with a novel framework for the predictive modeling of traffic flows. The proposed formwork introduces an improved graph network to capture the positional information in traffic follows. It is also capable of precisely capturing temporal dynamics using an improved bidirectional learning module. An attention mechanism is presented to capture the interactions among spatial and temporal patterns to further empower the predictive power of the model. Proof-of-concept experimentations are conducted on the PeMSD7 dataset, and the results (MAE: 0.197, MSE: 0.13, RMSE: 0.36, ) demonstrate the efficiency of our model over the state-of-the-art.