IET Wireless Sensor Systems最新文献

{"title":"IoT and machine learning models for multivariate very short-term time series solar power forecasting","authors":"Su Kyi,&nbsp;Attaphongse Taparugssanagorn","doi":"10.1049/wss2.12088","DOIUrl":"https://doi.org/10.1049/wss2.12088","url":null,"abstract":"<p>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.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 6","pages":"381-395"},"PeriodicalIF":1.5,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delay aware resource allocation in ORAN through network optimization","authors":"Basit N. Khalaf,&nbsp;Wisam Hasan Ali,&nbsp;Raad S. Alhumaima,&nbsp;Haider Ali Jasim Alshamary","doi":"10.1049/wss2.12087","DOIUrl":"https://doi.org/10.1049/wss2.12087","url":null,"abstract":"<p>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.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 6","pages":"528-538"},"PeriodicalIF":1.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing offloading with cybersecurity in edge computing for digital twin-driven patient monitoring","authors":"Ahmed K. Jameil,&nbsp;Hamed Al-Raweshidy","doi":"10.1049/wss2.12086","DOIUrl":"10.1049/wss2.12086","url":null,"abstract":"<p>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.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 6","pages":"363-380"},"PeriodicalIF":1.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SmartCardio: Advancing cardiac risk prediction through Internet of Things and edge cloud intelligence","authors":"S. Durga,&nbsp;Esther Daniel,&nbsp;J. Andrew,&nbsp;Radhakrishna Bhat","doi":"10.1049/wss2.12085","DOIUrl":"10.1049/wss2.12085","url":null,"abstract":"<p>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.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 6","pages":"348-362"},"PeriodicalIF":1.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable micro-electro-mechanical systems pressure sensors in health care: Advancements and trends—A review","authors":"S. S. Kiran Kolluri,&nbsp;S. Ananiah Durai","doi":"10.1049/wss2.12084","DOIUrl":"10.1049/wss2.12084","url":null,"abstract":"<p>Wearable technologies offer a complementary approach to clinical diagnostics by utilising a variety of physical, chemical, and biological sensors to mine physiological (biophysical and/or biochemical) data in real time (preferably continuous), in a non-intrusive or minimally invasive manner. Micro-Electro-Mechanical Systems (MEMS) pressure sensors dominate the healthcare applications especially for vital parameter sensing, as they feature the non-invasive method of diagnosis and have comparatively high sensitivity leading to better accuracy. Among them, capacitive and piezoresistive type pressure sensors have gained substantial advantages compared to other transduction devices due to high linearity, low power consumption, and low thermal coefficient. The performance review of such MEMS sensors in research and as well as market-ready devices that can be seamlessly integrated into commercial wearable products is the primary focus in this work. Challenges in the system level integration of Microsensors with the associated interface electronics and the design mitigation of such MEMS microsystems are also discussed. Design insights of analog front-end circuitry in terms of gain, noise, power and area that are crucial for any wearable applications are also comprehensively reviewed.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 6","pages":"233-247"},"PeriodicalIF":1.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of shipborne cold chain monitoring system based on multi link compression transmission","authors":"Pei-xue Liu,&nbsp;Yu-jie Chen,&nbsp;Dong Yan","doi":"10.1049/wss2.12082","DOIUrl":"10.1049/wss2.12082","url":null,"abstract":"<p>Aiming at the problems of traditional ship cold chain monitoring systems being easily affected by environmental factors, difficult to achieve real-time monitoring in the open sea without network signals, and low efficiency in transmitting Beidou short message data, a multi-link compressed transmission ship cold chain monitoring system was designed by combining 5G technology, Beidou short message transmission technology, and multi-protocol transmission technology. The system can adaptively switch the strength of wireless signals to ensure that information transmission is not lost. At the same time, a Beidou short message compressed transmission method was proposed to improve transmission efficiency. Test results show that the system can accurately complete data collection and processing, with small system errors, effectively improving the reliability and efficiency of the monitoring system, and has high application value.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 5","pages":"223-231"},"PeriodicalIF":1.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising multi-user wireless networks through discrete Fourier transform-based channel estimation with cascaded intelligent reflecting surfaces","authors":"Sakhshra Monga,&nbsp;Nitin Saluja,&nbsp;Chander Prabha,&nbsp;Roopali Garg,&nbsp;Anupam Kumar Bairagi,&nbsp;Md. Mehedi Hassan","doi":"10.1049/wss2.12081","DOIUrl":"10.1049/wss2.12081","url":null,"abstract":"<p>Wireless communication systems are inherently challenged by factors such as fading, path loss, and shadowing, leading to potential errors in data transmission. Traditional methods to mitigate these issues include power control, diversification, variable beamforming, and modulation techniques. However, the unpredictable nature of the wireless medium often limits their effectiveness. A new approach to address these challenges is the implementation of cascaded intelligent reflecting surfaces (IRS). IRS systems consist of multiple passive elements that intelligently reflect electromagnetic waves, thereby enhancing signal quality. The Advanced Discrete Fourier Transform (ADFT) matrix scheme is explored in channel estimation, a novel method particularly suitable for wireless networks utilising cascaded IRS. The ADFT matrix scheme is significant for its efficiency in managing the common-link configuration of cascading channel coefficients, which effectively reduces pilot overhead. When compared to traditional channel estimation methods like the Least Square|least squares, Maximal a posteriori probability, and Linear Minimum Mean Square Error, the ADFT matrix scheme exhibits superior performance. It achieves a remarkable reduction in normalised mean squared error (NMSE) – 66% and 80% at ^20 dB and 15 dB Signal to-Noise Ratios (SNR), respectively. Furthermore, increasing the pilot length correlates with enhanced NMSE performance, with a noted 33% improvement as the base station distance increases. Simulations demonstrate that with an escalation in the number of IRS elements and SNR, the ADFT matrix scheme consistently surpasses conventional methods. This advancement represents a significant leap in the field of wireless communication technology.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 4","pages":"144-156"},"PeriodicalIF":1.5,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data collection in IoT networks: Architecture, solutions, protocols and challenges","authors":"Ado Adamou Abba Ari,&nbsp;Hamayadji Abdoul Aziz,&nbsp;Arouna Ndam Njoya,&nbsp;Moussa Aboubakar,&nbsp;Assidé Christian Djedouboum,&nbsp;Ousmane Thiare,&nbsp;Alidou Mohamadou","doi":"10.1049/wss2.12080","DOIUrl":"10.1049/wss2.12080","url":null,"abstract":"<p>The Internet of Things (IoT) is the recent technology intended to facilitate the daily life of humans by providing the power to connect, control and automate objects in the physical world. In this logic, the IoT helps to improve our way of producing and working in various areas (e.g. agriculture, industry, healthcare, transportation etc). Basically, an IoT network comprises physical devices, equipped with sensors and transmitters, that are interconnected with each other and/or connected to the Internet. Its main objective is to gather and transmit data to a storage system such as a server or cloud to enable processing and analysis, ultimately facilitating rapid decision-making or enhancements to the user experience. In the realm of Connected Objects, an effective IoT data collection system plays a vital role by providing several benefits, such as real-time data monitoring, enhanced decision-making, increased operational efficiency etc. However, because of the resource limitations linked to connected objects, such as low memory and battery, or even single-use devices etc. IoT data collecting presents several challenges including scalability, security, interoperability, flexibility etc. for both researchers and companies. The authors categorise current IoT data collection techniques and perform a comparative evaluation of these methods based on the topics analysed and elaborated by the authors. In addition, a comprehensive analysis of recent advances in IoT data collection is provided, highlighting different data types and sources, transmission protocols from connected sensors to a storage platform (server or cloud), the IoT data collection framework, and principles for streamlining the collection process. Finally, the most important research questions and future prospects for the effective collection of IoT data are summarised.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 4","pages":"85-110"},"PeriodicalIF":1.5,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing spectrum sensing efficiency in multi-channel cognitive device-to-device networks: Medium Access Control layer strategies and analysis","authors":"Irfan Latif Khan,&nbsp;Adeel Iqbal,&nbsp;Ali Nauman,&nbsp;Muhammad Ali Jamshed,&nbsp;Atif Shakeel,&nbsp;Riaz Hussain,&nbsp;Adnan Rashid,&nbsp;Tommaso Pecorella","doi":"10.1049/wss2.12079","DOIUrl":"10.1049/wss2.12079","url":null,"abstract":"<p>The detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device-to-device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference-free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi-channel Cognitive D2D Networks (cD2DNs).</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 4","pages":"132-143"},"PeriodicalIF":1.5,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced reliability in hazardous event detection: A resilient multipath routing protocol for wireless sensor networks","authors":"Bálint Áron Üveges,&nbsp;András Oláh","doi":"10.1049/wss2.12078","DOIUrl":"10.1049/wss2.12078","url":null,"abstract":"<p>With the advance of climate change and the local effects of human activity, it has become of utmost importance to sense spatially extended natural and artificial physical phenomena to predict, monitor, and mitigate hazardous events. Wireless sensor networks are suitable for observing such phenomena, for example, wildfires, floods or landslides, without human supervision. This is due to affordable devices, independent power sources, wireless communication, and a broad range of sensors. During normal operation a few, while during the occurrence of an event a multitude of devices can fail. This leads to further disconnected devices, degrading the network's sensing capabilities. The communication requirements of such applications are difficult to fulfil with general routing protocols. The monitored event is rare compared to the network's lifetime, while its occurrence results in multiple, gradual node failures, still demanding the network to perform reliably. Available routing protocols fail to address every aspect of such application, thus the authors propose the Reliable Resilient Multipath Routing Protocol, designed to construct multiple disjoint paths from each device to a distinguished one, called the sink. The protocol employs proactive and reactive network management techniques to increase connection redundancy and maintain connectivity during failures. To verify the proposed protocol end-to-end, we evaluated the supported parameters, performed comparative simulations with routing algorithms known from the literature, and provided estimates of a realistic deployment.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"14 4","pages":"111-131"},"PeriodicalIF":1.5,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}