IET Wireless Sensor Systems最新文献

This paper introduces a kernel-based approach to enhance the security of distributed estimation in the presence of adversary links. Adversary links often degrade distributed recovery algorithm performance in distributed estimation. The authors propose secure distributed estimation algorithms employing an adaptive kernel and adaptive combination coefficients derived from it. The authors’ method includes a multiple kernel approach with varied widths and a heuristic formula for combination coefficients, improving performance in the presence of adversary links. Additionally, the approach is extended to single exponential kernels with fixed and adaptive widths, treating them as special cases. The multiple kernel method is used because it provides more degrees of freedom compared to a single kernel, leading to better results. Simulation results show that the proposed multiple kernel approach achieves performance close to the diffusion least mean square algorithm in the absence of attacks. The adaptive nature of the kernel and coefficients enhances algorithm robustness, making it promising for secure distributed estimation in the presence of adversary links.

Security problems loom big in the fast-growing world of Internet of Things (IoT) networks, which is characterised by unprecedented interconnectedness and data-driven innovation, due to the inherent susceptibility of wireless infrastructure. One of the most pressing concerns is user authentication, which was originally intended to prevent unwanted access to critical information but has since expanded to provide tailored service customisation. We suggest a Wi-Fi sensing-based physical layer authentication method for IoT networks to solve this problem. Our proposed method makes use of raw channel state information (CSI) data from Wi-Fi signals to create a hybrid deep-learning model that combines convolutional neural networks and long short-term memory networks. Rigorous testing yields an astonishing 99.97% accuracy rate, demonstrating the effectiveness of our CSI-based verification. This technology not only strengthens wireless network security but also prioritises efficiency and portability. The findings highlight the practicality of our proposed CSI-based physical layer authentication, which provides lightweight and precise protection for wireless networks in the IoT.

Recently, there has been an increasing interest in indoor localisation due to the demand for location-based services. Diverse techniques have been described in the literature to improve indoor localisation services, but their accuracy is significantly affected by the number and location of the anchors, which act as a reference point for localising tags in a given space. The authors focus on indoor area-based localisation. A set of anchors defines certain geographical areas, called residence areas, and the location of a tag is approximated by the residence area in which the tag is placed. Hence the position is not given by exact coordinates. In this approach, placing the anchors such that the resulting residence areas are small on average yields a high-quality localisation accuracy. The authors’ main contribution is the introduction of a discretisation method to calculate the residence areas for a given anchor placement more efficiently. This method reduces the runtime compared to the algorithms from the literature dramatically and hence allows us to search the solution space more efficiently. The authors propose APOTSA, a novel approach for discovering a high-quality placement of anchors to improve the accuracy of area-based indoor localisation systems while requiring a shorter execution time than existing approaches. The proposed algorithm is based on Tabu search and optimises the localisation accuracy by minimising the expected residence area. APOTSA's localisation accuracy and time of execution are evaluated by different indoor-localisation scenarios involving up to five anchors. The results indicate that the expected residence area and the time of execution can be reduced by up to 9.5% and 99% compared to the state-of-the-art local search anchors placement (LSAP) algorithm, respectively.

A network created in places inaccessible to humans is known as the wireless sensor network. A sensor must detect data/information before it sends this data to a base station. Data can be routed between just one node to a base station using a variety of routing protocols. The hierarchical routing method is one of the routing protocols that hierarchically distributes sensed data. Using clustering to arrange the network into an interconnected hierarchy has shown to be a successful strategy. Bio-inspired particle swarm optimisation is combined with the Enhanced LEACH protocol to overcome the shortcomings of conventional protocol like overall consumption of energy, the total number of survival nodes, and packets being delivered during the network's life. Metaheuristic approach of particle swarm optimisation which explores alternative paths during optimisation, leading to more adaptive and efficient energy dissipation. Enhanced LEACH with the bioinspired protocol makes it more efficient for real-time applications. Simulation results show that the proposed protocol has a greater advantage over the conventional and Enhanced LEACH.

The ubiquitous Internet of Things (IoT) and sensing technologies provide an interesting opportunity of remote health monitoring and disease risk categorisation of populations. An end-to-end architecture is proposed to facilitate real-time digital dashboards to visualise general anxiety risks of patients, especially during a pandemic, such as COVID-19. To collect physiological data related to anxiety (heart rate, blood pressure, and saturation of peripheral oxygen [SPO₂]) and communicate them to a centralised dashboard, dubbed ‘X-DASH’, a hardware prototype of the proposed architecture was developed using Node-MCU and diverse sensors. The dashboard presents a smart categorisation of users' data, assessing their anxiety risks, to provide medical professionals and state authorities a clear visualisation of health risks in populations belonging to different regions. We categorised the risk levels as Normal, Mild, Moderate, Elevated, Severe, and Extreme, based on the collected physiological data and pre-defined threshold values. The developed hardware prototype in this work was used to collect data from about 500 patients present at cardiac clinic of a leading general hospital in Karachi (Pakistan) and the anxiety risk levels were assigned based on pre-defined threshold values. To validate the reliability of the X-DASH, the personal physician of each patient was consulted and was requested to identify each of their anxiety risk levels. It was found that the risk levels suggested by X-DASH, (based on data of heart rate, blood pressure, and SPO₂ were more than 90% accurate when compared with diagnoses of physicians. Subsequently, packet loss, delay and network overhead for the platform was compared when using MQTT, CoAP and Modbus. Although MQTT has shown higher delays, but it is still recommended due to having a higher reliability.

Feeding electrical components without having a physical contact was always a goal in electrical engineering. Nowadays, Wireless Power Transfer (WPT) is becoming the main way to provide energy for wireless sensors. WPT can be categorised into two primary techniques: radiative and non-radiative methods. The authors uniquely delve into the utilisation of radiative methods, precisely the Radio Frequency (RF)-WPT method. The authors focus on the factors and considerations for designing this kind of systems highlighting the specific nuances and challenges associated with high power wireless energy transfer systems and will try to define an efficient design method. A comprehensive survey is offered encompassing the entire system. It explores both transmitter and receiver systems, dissecting their subsystems and elements and challenges related to high power application one by one, while also elucidating the essential principles and integration factors.

In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short-term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short-term SI predictions. To achieve accurate very short-term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low-cost, low-power, and long-range wireless control networks. The authors’ study focuses on SI forecasting using long short-term memory and bi-directional long short-term memory (Bi-LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root-mean-square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi-LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi-LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short-term predictions based on rapidly changing environmental factors.

A multi variable resource allocation problem is investigated in network environments, specifically focusing on the consideration of quality of service in open radio access network. The main objective is to minimise the combined latency of various servers while complying with network limitations. The delay of each server is represented by a non-linear function that has exponentially based. This characteristic inherently brings non-convexity into the objective function. In contrast, the constraints comprise various linear combinations of network variables, including resource block allocations, power consumption, and number of virtual machines. The purpose of these constraints is to guarantee that the allocation of resources adheres to practical limitations and upholds fairness among servers. Nevertheless, the inclusion of a non-convex objective function significantly adds complexity to the optimisation problem and non-convex behaviour, requiring specialised algorithms and techniques to identify solutions. Subsequently, the Lagrange multiplier method has been used to solve this problem mathematically. Numerically, three algorithms have been utilised and compared to solve the problem, these are active-set, interior point and sequential quadratic programming. Note that the total delay as an objective function is based on the total power consumption of the servers. Previous to optimising the total delay, a delay model is proposed and compared with two research works that are based on experimental and real time data. The proposed model shows data matching with the other works and permits for more adaptation/integration with any other works that uses different servers’ characteristics and network parameters.

In healthcare, the use of digital twin (DT) technology has been recognised as essential for enhancing patient care through real-time remote monitoring. However, concerns regarding risk prediction, task offloading, and data security have been raised due to the integration of the Internet of Things (IoT) in remote healthcare. In this study, a new method was introduced, combines edge computing with sophisticated cybersecurity solutions. A vast amount of environmental and physiological data has been gathered, allowing for thorough understanding of patients. The system included hybrid encryption, threat prediction, Merkle Tree verification, certificate-based authentication, and secure communication. The feasibility of the proposal was evaluated by using an ESP32-Azure IoT Kit and Azure Cloud to evaluate the system's capacity to securely send patient data to healthcare institutions and stakeholders, while simultaneously upholding data confidentiality. The system demonstrated a notable improvement in encryption speed, with 27.18%, represented as the improved efficiency and achieved storage efficiency ratio 0.673. Furthermore, the evidence from the simulations showed that the system's performance was not affected by encryption since encryption times continuously remained within a narrow range. Moreover, proactive alert of probable security risks would be detected from the predictive analytics, hence strong data integrity assurance. The results suggest the proposed system provided a proactive, personalised care approach for cybersecurity-protected DT healthcare (DTH) high-level modelling and simulation, enabled via IoT and cloud computing under improved threat prediction.

Cardiovascular disease is a leading cause of illness and death globally. The integration of Internet of Things (IoT) and deep learning technologies, including transfer learning, has transformed healthcare by improving the prediction and monitoring of conditions such as arrhythmias, which can be fatal if not detected and treated promptly. Traditional methods often lack real-time accuracy due to scattered data sources. A novel heart care approach utilising IoT technology and edge cloud computing is introduced to provide rapid, automated responses and support decision-making. The system connects smart devices, sensors, and healthcare providers to predict patient conditions and deliver accessible healthcare services. It consists of two main phases: data acquisition, where sensors measure heart rate, temperature, and blood pressure, and data processing, where the edge cloud processes the data using Haar Wavelet transform, Convolutional Neural Network (CNN), and transfer learning. Experimental results demonstrate that this smart cardio system achieves 99.3% accuracy with reduced network delay and response time, outperforming traditional methods, such as k-nearest neighbours, support vector machine, and discrete wavelet-based convolutional neural network.