B5G Applications and Emerging Services in Smart IoT Environments

Traditional approaches generally focus on the privacy of user’s identity in a smart IoT environment. Privacy of user’s behavior pattern is an important research issue to address smart technology towards improving user’s life. User’s behavior pattern consists of daily living activities in smart IoT environment. Sensor nodes directly interact with activities of user and forward sensing data to service provider server (SPS). While availing the services provided by a server, users may lose privacy since the untrusted devices have information about user’s behavior pattern and it may share data with adversary. In order to resolve this problem, we propose a multilevel privacy controlling scheme (MPCS) which is different from traditional approaches. MPCS is divided into two parts: (i) behavior pattern privacy degree (BehaviorPrivacyDeg), which works as follows: firstly, frequent pattern mining‐based time‐duration algorithm (FPMTA) finds the normal pattern of activity by adopting unsupervised learning. Secondly, patterns compact algorithm (PCA) is proposed to store and compact the mined pattern in each sensor device. Then, abnormal activity detection time‐duration algorithm (AADTA) is used by current triggered sensors, in order to compare the current activity with normal activity by computing similarity among them; (ii) multilevel privacy design model: we have divided privacy of users into four levels in smart IoT environment, and by using these levels, the server can configure privacy level for users according to their concern. Multilevel privacy design model consists of privacy‐level configuration protocol (PLCP) and activity design model. PLCP provides fine privacy controls to users while enabling users to set privacy level. In PLCP, we introduce level concern privacy algorithm (LCPA) and location privacy algorithm (LPA), so that adversary could not damage the data of user’s behavior pattern. Experiments are performed to evaluate the accuracy and feasibility of MPCS in both simulation and real‐case studies. Results show that our proposed scheme can significantly protect the user’s behavior pattern by detecting abnormality in real time.

Efficient data collection and communication are key tasks in smart IoT environment consisting of a large number of devices. Here imprecise data are generated due to the interferences between the devices and harsh operation condition, and therefore data fusion is needed to gather and extract useful data from multiple sources. A number of approaches for data fusion have been proposed which are based on probability, artificial intelligence, or evidence theory to efficiently aggregate the data. The techniques allow the system to be cognitive and intelligent in terms of decision-making under the uncertainty of data and limited resource. In this paper a comprehensive survey on the data fusion techniques for smart IoT system is presented. The challenges and opportunities with data fusion are also delineated. It will be useful for the researchers in developing the applications and services based on smart IoT environment, which require intelligent decision making.

Falls are the primary basis of autonomy loss, injuries, and deaths among disabled persons and the elderly. With the development of technologies, falls are extensively researched by scientists to diminish severe consequences and adverse effects. Reliable fall recognition is crucial in humanoid robotics and healthcare research, as it helps minimize damage. The detection methods for falls are classified into three categories: wearable sensors, ambient sensors, and vision-based sensors. Over the last few years, computer vision and deep learning (DL) have been widely applied to fall detection systems. The application of DL for fall activity recognition has led to major developments in detection accuracy by overcoming numerous obstacles met by conventional models. This study presents a Vision Transformer and Self-Attention Mechanism with Recurrent Neural Network-Based Fall Activity Recognition System (VTSAMRNN-FARS) method. The primary objective of the VTSAMRNN-FARS method is to improve the fall detection and classification method for individuals with disabilities in smart IoT environments. Initially, the bilateral filtering (BF) model is used for image pre-processing to remove the noise in input image data. Furthermore, the feature extraction process is performed by the Vision Transformer (ViT) model to convert raw data into a reduced set of relevant features, thereby enhancing model performance and efficiency. For detecting fall activities, a bidirectional gated recurrent unit with a self-attention mechanism (BiGRU-SAM) model is implemented. Finally, the enhanced wombat optimization algorithm (EWOA) model optimally adjusts the hyperparameter values of the BiGRU-SAM approach, resulting in improved classification results. The simulation analysis of the VTSAMRNN-FARS methodology is examined under the UR_Fall_Dataset_Subset dataset. The comparison study of the VTSAMRNN-FARS methodology is reviewed and found to be 99.67% more effective than existing models.

<p>Modern Industrial Control System (ICS) can provide vast functions as the introduction of IT technology is established along with the introduction of the IoT environment. Engineering Workstation (EWS) used by ICS is widely used to efficiently manage and control industrial devices including smart IoT devices. However, the DLL injection attack in ICS is not high in difficulty compared to the risk, but it can cause fatal malfunction. If an attack is carried out targeting the EWS, it may cause erroneous operation in many control devices, including IoT devices, cause fatal accidents throughout the Supervisory Control and Data Acquisition (SCADA) system. In this paper, we present a method to detect DLL injection attacks by specializing in EWS used in ICS in IoT environment and purpose a method to detect data changes due to DLL injection attacks by analyzing and utilizing PEB-LDR data. Also, we purpose a method to detect and block execution when a malicious DLL is suspected to be loaded by DLL injection.</p> <p> </p>

The indoor air temperature is one of the key factors to improve the performance of energy efficiency of buildings and quality of life in a very smart IoT environment. Therefore, a periodic and accurate prediction of the minimum and maximum indoor air temperature allows taking necessary precautions to handle the variations’ impact and tendencies. During this assessment, we developed minimum and maximum indoor air temperature prediction models using multiple statistical regression (MLR), multilayered perceptron (MLP), and random forest (RF, where Rf is achieved once with tree depth 10 (RFdepth10), and once with tree depth 50 (RFdepth50)). The study was conducted at a building located within the University of Applied Sciences, Stuttgart, in Germany. Sensors were accustomed to aggregate data, which were used because of the input variables for the prediction. The variables are outdoor air temperature, indoor air temperature, humidity, and heating temperature. Performance of the models was evaluated with the coefficient of determination \({R}^{2}\) and therefore the root means square error (RMSE). The simulation results showed that the prediction by the MLP algorithm, based on minimum indoor air temperature models and also maximum indoor air temperature models, provides better accuracy with the very best \({R}^{2}\) and lowest RMSE in the independent test dataset. This survey developed a straightforward and powerful MLP model to predict the minimum and therefore the maximum indoor air temperature, which may integrate into smart building management system technology in the future.

Development of information and communication technologies enabled more efficient development of assistive technologies and their application in visually impaired people everyday living. For that purpose, in this research, the user needs of visually impaired people during the purchasing process are defined. On the basis of the user needs conceptual architecture of the system is proposed for the service delivery of guidance and information of users during the use of smart shopping cart in shops. The key elements of the architecture are applicative solutions for smartphones and smart shopping carts, which interact which each other in smart IoT environment. The purpose of the mentioned service which uses the proposed architecture is improvement visually impaired persons’ quality of life and raising the degree of mobility in everyday activities. Mentioned service also presents model of assistive technology in Society 5.0 surrounding.

The Internet of Things is a wide-ranging concept that applies not only to objects but also in our everyday life. Home networks utilizing smart devices, automobile technology, and treatment at medical institutions are typical examples of implementations of the Internet of Things in everyday life. However, there are issues about fragile security that stand in stark contrast to the rapid popularization of the Internet of Things. There was a case of a hacked chip in a user device that spread malignant code and spam mail followed by a data leak. Besides, Internet of Things devices like the ones used as domestic smart home devices are found to be more prone to be the target of distributed denial of service attacks. Therefore, in this paper, we present a plan for the registration of a device, the formation of a session key, and user authentication to ensure secure communication. Additionally, we present a plan for a message communication protocol. The communication scheme proposed here is also analyzed for data stability and encryption efficiency.

Geo-sensor is the term used for the deployment of a wireless sensor network (WSN) in a real environment, which can be a hideous task due to many influential variables in a given environment. The spatial context of a sensor in a smart environment can be of huge significance and can also play an important role in improving the smart services provision. In this work, we propose a DIY geo-sensor framework and data composition toolbox for the deployment of sensors data in smart IoT environments along with geographical context. A geo-sensor framework is deployed, which enables the registration of multiple geo-sensor networks by providing multiple geo-sensor platforms. The framework’s logic is based on the combination of a geo-sensor service registry, geo-sensor composition toolbox, and geo-sensor platforms. A geo-sensor platform provides the content to the toolbox, enabling relaxed real-time geo-sensor data management. Our proposed work is two-fold. Firstly, we propose the design details for the geo-sensor framework and composition toolbox. The proposed design for the geo-sensor framework aims to provide a DIY platform for multiple geo-sensor networks and services deployment, giving access to multiple users resulting in reuse of resources and reduction in deployment costs by avoiding duplicate deployments. Secondly, we implement the proposed design based on RESTful web services and SOAP web services. Performance comparison analysis is then performed among the two web services to find the best suited implementation for given scenarios. The results of the performance analysis prove that RESTful web services are the better choice for ease of implementation, access, and light-weightiness.

COVID-19 special issue: Intelligent solutions for computer communication-assisted infectious disease diagnosis.

of Things (IoT) is raised as most adaptive technologies for the end users in past few years. Indeed of being popular, security in IoT turned out to be a crucial research challenge and a sensible topic which is discussed very often. Denial of Service (DoS) attack is encountered in IoT sensor networks by perpetrators with numerous compromised nodes to flood certain targeted IoT device and thus resulting in vulnerability or service unavailability. Features that are encountered from the malicious node can be utilized effectually to recognize recurring patterns or attack signature of network based or host based attacks. Henceforth, feature extraction using machine learning approaches for modelling of Intrusion detection system (IDS) have been cast off for identification of threats in IoT devices. In this investigation, Kaggle dataset is measured as benchmark dataset for detecting intrusion is considered initially. These dataset includes 41 essential attributes for intrusion identification. Next, selection of features for classifiers is done with an improved Weighted Random Forest Information extraction (IW-RFI). This proposed WRFI approach evaluates the mutual information amongst the attributes of features and select the optimal features for further computation. This work primarily concentrates on feature selection as effectual feature selection leads to effectual classification. Finally, performance metrics like accuracy, sensitivity, specificity is computed for determining enhanced feature selection. The anticipated model is simulated in MATLAB environment, which outperforms than the existing approaches. This model shows better trade off in contrary to prevailing approaches in terms of accurate detection of threats in IoT devices and offers better transmission over those networks.

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Static Multi Feature-Based Malware Detection Using Multi SPP-net in Smart IoT Environments

An Integrated Network Model with Sequential Approach to Elevate Irrigation in Smart IoT Environment

Automatic municipal solid waste management systems are integral to every smart city worldwide. They help to separate wastes into different categories for further recycling or effective disposal. This way, waste authorities could mitigate the effect of rapid urbanization, population growth, and the escalating consumption patterns associated with modern living. Deep learning models could play a critical role in the identification and classification of these wastes into their respective categories. Therefore, this study evaluates the performance of three deep learning models: MobileNet, InceptionV3, and VGG16 for waste classification. The evaluation was done under two separate model configurations while their classification accuracy, execution time, precision, recall, and F1-Score were computed across a range of 10 to 100 epochs. MobileNet consistently demonstrated the highest classification accuracy, reaching approximately 90% at 100 epochs, while also maintaining the shortest execution time, starting at 2.13 minutes for 10 epochs and increasing to about 14.34 minutes for 100 epochs. InceptionV3 exhibited a balanced performance, achieving around 83% accuracy at 100 epochs with execution times ranging from 3.57 minutes to 49.42 minutes. VGG16, although started with the lowest accuracy, improved significantly to about 88% at 100 epochs, but at the cost of the longest execution time, starting at 9.45 minutes and rising to 68.72 minutes. The results indicate that MobileNet is the most efficient model for applications requiring both high accuracy and low computational cost, while InceptionV3 and VGG16 are suitable for scenarios where accuracy is prioritized over execution time. This comparative analysis provides valuable insights for selecting appropriate deep-learning models based on specific task requirements and resource constraints.

IoT enabled smart environments typically include large number of simple sensors that are designed to detect specific events. In many environments, however, not one but combinations of sensor events represent activities of interest (such as activities of daily living of a patient in a smarthome). Detecting and monitoring these activities of interest result in both application-specific benefits and operational benefits. However, human activities often overlap, thereby making activity detection from the collected sensor events a challenging problem. In this paper, we first present the various benefits of such materialization of sensor events into activities, and then discuss the challenges in detecting diverse activities taken up by humans. More interestingly, the diversity of human activities and the time-variability of a given activity by the same human, makes reliable detection of activities even harder, and open up interesting avenues for future research.