IET Wireless Sensor Systems最新文献

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.

With the continuous progress of society, people's living consumption needs are also constantly changing. The traditional vehicle positioning technology can no longer meet the new positioning needs of travelers, so a more accurate positioning system is urgently needed. This paper aims to study a more accurate positioning system and proposes a vehicle positioning system based on radio frequency identification (RFID). After comparing several common positioning methods, the use of LANDMARC positioning algorithm makes the vehicle positioning more accurate. This paper has effectively integrated advanced computer and artificial intelligence technology and applies it to control and management and finally established a comprehensive traffic positioning system that can effectively alleviate traffic congestion, reduce traffic accidents and quickly deal with traffic emergencies. The experimental results have shown that when the vehicle runs at a speed of 40 km/h and the communication rate is 860 kpbs, the average positioning accuracy of the improved algorithm is doubled compared with the traditional algorithm, and the error is reduced by nearly one. Therefore, compared with the traditional positioning system and artificial intelligence, the RFID vehicle positioning system under cloud computing has more accurate positioning information and more timely and convenient services.

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.

The Wireless Sensor Network needs to become a dynamic and adaptive network to conserve energy stored in the wireless sensor network node battery. This dynamic and adaptive network sometimes are called SON (Self Organizing Network). Several SON concepts have been developed such as routing, clustering, intrusion detection, and other. Although several SON concepts already exist, however, there is no concept for SON in dynamic radio configuration. Therefore, the authors’ contribution to this field would be proposing a dynamic and adaptive Wireless Sensor Network node radio configuration. The significance of their work lies in the modelling of the SON network that builds based on our measurement in the real-world jungle environment. The authors propose input parameters such as SNR, the distance between the transmitter and receiver, and frequency as the static parameter. For adaptive parameters, we propose bandwidth, spreading factor, and its most important parameter such as power for data transmission. Using the Levenberg Marquardt Artificial Neural Network (LM-ANN) self-organise Network model, power reduction and optimisation from 20 dBm to 14.9 dBm for SNR 3, to 11.5 dBm for SNR 6, and to 12.9 dBm for SNR 9 all within a 100-m range can be achieved. With this result, the authors conclude that we can use LM-ANN for the wireless sensor network SON model in the jungle environment.

The 5G-enabled sensor network systems make it possible to connect cyber and real ‘things’ in many ways. Even so, the flow of data between 5G-enabled sensor devices brings big data environment problems, such as huge amounts of data, duplicated data, a lack of scalability etc. Some of these problems are as follows. It is hard to keep an eye on 5G-enabled sensor systems in a ‘big data’ environment. Cyberattacks that could put the safety of the sensor network systems at risk are hard to find, which make the situation even more complicated. The security challenges of 5G-enabled Sensor Network Systems are studied and analyzed due to some constraints associated with the sensor nodes. The proposed advanced algorithm for securing the 5G-enabled sensor systems is a Multidimensional big data environment using artificial intelligence/machine learning (AI/ML). Using a structure that depends on both geographical and temporal data, an improved clear point selection operation may get important information from multidimensional time series data that is spread across a wide range of sensor nodes. Therefore, the actions of the 5G-enabled sensor network can be shown accurately and a complete model of its underlying data structure is built to analysis attacking, pattern on 5G-enabled Sensor Network Systems using the AI/ML Algorithm.

Conventional infrastructures based on the cloud are not sufficient for the emerging Internet of Things (IoT) applications requirements. Many big problems are shortcomings, especially in terms of network bandwidth and latency. Throughout recent years, the idea to relieve fog computing and edge computing was suggested by bringing data processing capacities closer to these limits to the edge of the network. The authors assume that the full potential of IoT will, in many cases, only be activated by the combination of cloud, fog, and edge computing in a new computing paradigm, given IoT growth and development forecasts. This article discusses the possibility and need for such a paradigm by introducing steam computing as a new distributed type of computing using cloud, fog, and edge utilities to carry out data processing and storage. The authors treat steam computing through four planes: security and privacy plane, data analytics and fault tolerance plane and deployment and test beds plane. Finally, the authors focus on the open issues and future trends in steam computing.