IET Wireless Sensor Systems最新文献

Communication architectures based on the Internet of Things (IoT) are increasingly frequent. Commonly, these solutions are used to carry out control and monitoring activities. It is easy to find cases for manufacturing, prediction maintenance, Smart Cities, etc., where sensors are deployed to capture data that is sent to the cloud through edge devices or gateways. Then that data is processed to provide useful information and perform additional actions if required. As crucial as deploying these monitoring solutions is to verify their operation. In this article, we propose a novel warning method to monitor the performance of IoT-based systems. The proposal is based on a holistic quality model called Quality of X (QoX). QoX refers to the use of a variety of metrics to measure system performance at different quality dimensions. These quality dimensions are data (Quality of Data, QoD), information (Quality of Information, QoI), users' experience (Quality of user Experience, QoE), and cost (Quality Cost, QC). In addition to showing the IoT system performance in terms of QoX in real-time, our proposal includes (i) a forecasting model for independent estimation of QoX applying Deep Learning (DL), specifically using a Long Short-Term Memory (LSTM) and time series, and (ii) the warning system. In light of our results, our proposal shows a better capacity to forecast quality drops in the IoT-based monitoring system than other solutions from the related literature.

The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device-free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine-grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real-time location-tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location-independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real-world environments.

An intelligent video surveillance system is crucial to enhance public safety, crime prevention, traffic, and crowd management in a smart city milieu. Situational awareness is an essential aspect of these surveillance systems and it is inferred through underlying context aware frameworks. However, these systems may not possess the ability to proactively disseminate the real-time context among its sensor nodes. Moreover, in the specific conditions of occurrence of related or repeated events, these systems may also perform inefficiently through afresh context processing and disseminate cycles, without learning from the relevant context that has already been occurred and processed by the system. It leads to deteriorated performance, especially delay in reaction, overwhelmed processing, and energy expenditures. Therefore, to counter such issues, this research work proposes an energy efficient situational aware framework deployed in visual sensors network that is incorporated with context associative learning. System observes currently occurring context at each instance of an event. Overtime, context is refined and stored in context database. Such mechanism empowers the system to learn from previous experiences and develop relationship among the subsequent events that is embedded through this associative (adaptive) learning. Eventually, each event is processed through intelligent resource allocation, supported through mechanism of context learning that further illustrates the independent functions of reduced processing and improved (rapid) decision making resulting in evolution of energy efficient computing paradigm. Ultimately, the capability of learned reflex-action is induced through introspectively evolved context of the system in entirety and against specific condition of recurred situation depicting minimum energy expenditure.

Low-power localisation systems are crucial for machine-to-machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range-based technique to estimate the distance of a target node from a LoRa gateway using machine-learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range-based distance mapping with trilateration and fingerprint-based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF-based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration-based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint-based direct location estimation approaches.

Wireless multimedia sensor networks (WMSNs) has the capability of collecting scalar information, as well as multi-media events like moving object tracking and traffic fatalities. Because energy in WMSNs is a constrained resource, several researchers offered cluster routing approaches, and most protocols generally do not offer an average resource usage of the network that is balanced and minimised. To address this issue, this paper proposes an Energy-Efficient Multipath Clustering with Load Balancing Routing Protocol for WMSNs (EEMCL) to prolong the network lifetime. The suggested protocol, which segments the network into layers of clusters, would be implemented using multi-hop. Sensing data from the sensor is transmitted to the sink by the main cluster heads in each layer, cooperating with the cluster heads in the upper layers. The proposed protocol improves energy dissipation, network life-times, and network stability according to simulation results when compared with SEP, SEP-E, and SEPFL protocols. The last node dead for the proposed protocol is at round 5833, 4027 for SEPFL, 2828 for SEP-E, and 2325 for SEP.

Retraction: “Wideband circularly polarised antenna ‘multi-input-multi-output’ for wireless UWB applications,” by Pillalamarri Laxman, Anuj Jain, Volume 11, Issue 6, IET Wireless Sensor Systems, pages: 259–274, First Published online: December 01, 2021: The above article published online on December 01, 2021 in Wiley Online Library (https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/wss2.12032), has been retracted by agreement between the journal Editor-in-Chief, Professor Sherali Zeadally, The Institution of Engineering and Technology and John Wiley & Sons, Ltd.

The retraction has been agreed due to unattributed overlap between this article and the following article published in IEEE Access, ‘Wideband Circularly Polarized Textile MIMO Antenna for Wearable Applications’ by Sanjeev Kumar, Durgesh Nandan, Kunal Srivastava, Sachin Kumar, Hari Singh, Mohamed Marey, Hala Mostafa, Binod Kumar Kanaujia, Volume 9, 2021, doi:10.1109/ACCESS.2021.3101441.

Bridges are a kind of infrastructure constructed to save urban space, to relieve traffic congestion, and to show urban modernisation. Therefore, bridge safety has become the focus of social attention. To know the structural condition of the bridge in real-time, it is necessary to develop an intelligent and efficient bridge health monitoring system. However, the traditional bridge health monitoring system has poor real-time performance, low efficiency, and a low degree of automation. The development of LoRa technology is expected to improve this situation. In this study, we designed a bridge-distributed monitoring system based on LoRa technology, which has end nodes, relay nodes, a collect node, and a monitoring centre. In detail, the STM32H743XI processor is combined with the radio frequency SX1268 chip to form the above nodes. Furthermore, the end node contains the pulse vibration excitation circuit, and pick-up signal conditioning circuit of the vibrating string crack metre, and the signal conditioning circuit of the temperature sensor AD590 and the humidity sensor HM1500LF. Finally, the data preprocessing of sensors and the communication performance of the two-stage relay network of the LoRa module, which represent the key technologies to effectively and efficiently meet the real-time monitoring of bridge conditions, are tested and discussed. This distributed monitoring system for the bridge can provide feasible solutions for the application field of bridge monitoring, which is helpful for the daily maintenance management and risk warning decision-making of bridges.

Localization is one of the essential problems in wireless sensor applications (WSNs). Most range-free localization schemes for mobile WSNs are based on the Sequential Monte Carlo (SMC) algorithm. Multiple iterations, sample impoverishment and less sample diversity, leading to low localizing efficiency, are the most usual problems demanding to be solved in these SMC-based methods. An improved localization scheme for mobile aquaculture WSNs based on the Improving Dynamic Population Monte Carlo Localization (I-DPMCL) method is proposed. A population of probability density functions is proposed to approximate the unknown location distribution based on a set of observations through an iterative mixture importance sampling procedure, accompanied by node dynamic behaviours being analysed quantitatively or definitely. Threefold constrain rules are put forward in the I-DPMCL scheme to decrease the iteration number and trade off iteration number and enough valid samples to obtain the optimum iteration number. Then, these localization behaviours, especial delay, are predicted based on the statistical point of view. Moreover, performance comparisons of I-DPMCL with other SMC-based schemes are also proposed. Simulation results show that delay of I-DPMCL has some superiority to those of other schemes, and accuracy and energy consumption are improved in some cases of lower mobile velocity.

User behaviour, human mistakes, and underperforming equipment contribute to wasted energy in buildings and industries. Identifying anomalous consumption power behaviour can help to reduce peak energy usage and change undesirable user behaviour. Furthermore, decreasing energy consumption in buildings is difficult because usage patterns vary from one building to the next. So, the main contribution in this manuscript is to propose a lightweight architecture for smart meter to identify abnormalities in power consumption for each building individually using machine learning (ML) models and implement on a Single Board Computer. To detect daily and periodic pattern anomalies, two models of anomaly detection based on supervised and unsupervised ML algorithms are built and trained where numerous algorithms were utilised to select the best algorithm for each model. Also, the proposed approach enables iterative procedure modifications by retraining the two anomaly detection models on data aggregator server based on the received data meter from the specific smart meter to give better power service to clients while minimising provider losses. The effectiveness and efficiency of the suggested approach have been proven through extensive analysis.