IET Wireless Sensor Systems最新文献_第8页

On optimal anchor placement for area-based localisation in wireless sensor networks 无线传感器网络中基于区域定位的最优锚点放置

IF 1.9

IET Wireless Sensor Systems Pub Date : 2021-02-07 DOI: 10.1049/wss2.12010

Abdelhakim Cheriet, Abdelmalik Bachir, Noureddine Lasla, Mohamed Abdallah

引用次数: 5

An optimal algorithm for better efficiency in WMSN for multimedia applications 为提高多媒体应用中WMSN的效率，提出了一种优化算法

IF 1.9

IET Wireless Sensor Systems Pub Date : 2021-01-01 DOI: 10.1049/wss2.12030

H. Al-Asadi

引用次数: 0

Anchor selection by geometric dilution of precision for an indoor positioning system using ultra-wide band technology 基于精度几何稀释的超宽带室内定位系统锚点选择

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-12-15 DOI: 10.1049/wss2.12006

Abbas Albaidhani, Ahlam Alsudani

{"title":"Anchor selection by geometric dilution of precision for an indoor positioning system using ultra-wide band technology","authors":"Abbas Albaidhani, Ahlam Alsudani","doi":"10.1049/wss2.12006","DOIUrl":"10.1049/wss2.12006","url":null,"abstract":"<p>In wireless localization systems, the performance enhancement of location estimation is an important goal. In recent years, different positioning systems using an ultra-wide band (UWB) technology have been created, and always an evaluation metric to test such systems is needed for ensuring a suitable system for a specified application. Also, a non-line-of-sight (NLOS) identification and mitigation method is needed usually when utilizing the UWB technology. The mean-square-error (MSE) and geometric dilution of precision (GDOP) evaluation metrics are widely implemented as standard for choosing a perfect system. In a harsh environment, a novel algorithm of indoor positioning (IP) system is presented using the UWB technology without implementing any NLOS identification and mitigation technique and the localization accuracy is evaluated online. The UWB is used to communicate a mobile station (M) with <i>n</i> anchor nodes distributed randomly and clustered by utilizing a combination method to create different groups, and then a conventional linearized least square (LLS) method is utilized by each group for locating <i>M</i>. A weighted GDOP metric is implemented online to assess the positioning accuracy of each group. Then, the group having the lowest positioning error among other groups is selected to relocate <i>M</i> using a proposed LLS, named modified LLS, for the selected group to enhance the positioning accuracy. The created system outperforms different IP systems in the market for the last decade in terms of time, complexity, and accuracy. The created IP system has a positioning error around 25 cm<sup>2</sup> of MSE in a hard environment, which is less than those of different IP systems created recently in the market.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82497785","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}

引用次数: 12

Modified threshold for cluster head selection in WSN using first and second order statistics 改进了WSN中使用一阶和二阶统计量选择簇头的阈值

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-12-01 DOI: 10.1049/iet-wss.2020.0048

Sefali Panda, Trupti Mayee Behera, Umesh Chandra Samal, Sushanta Kumar Mohapatra

{"title":"Modified threshold for cluster head selection in WSN using first and second order statistics","authors":"Sefali Panda, Trupti Mayee Behera, Umesh Chandra Samal, Sushanta Kumar Mohapatra","doi":"10.1049/iet-wss.2020.0048","DOIUrl":"10.1049/iet-wss.2020.0048","url":null,"abstract":"<div>\u0000 <p>Wireless sensor network (WSN) comprises of numerous sensors deployed either directly or randomly in the region of interest. Due to the limited power of the sensors, these networks are energy-constrained and thus need efficient power utilisation. Efficient clustering and cluster head (CH) selection ensures balanced energy distribution to the sensors within the WSN and hence prolong the network lifetime. This study proposes the method for evaluating the threshold for the CH selection after each round, which increases the network lifetime and throughput significantly. The threshold for CH selection is modified considering the normalised first-order and second-order statistical parameters, such as mean average low-energy adaptive clustering hierarchy (AvgLEACH) and variance (VarLEACH) of the overall network energy. These methods have been formulated after studying the effect of the number of working nodes in each round on the threshold value selection. Apart from including energy parameter to the threshold equation, the methods of VarLEACH and AvgLEACH are augmented with a residual energy parameter that is local to the nodes and named as VarRLEACH and AvgRLEACH. The simulation results comparing all the methods suggest that the proposed method AvgRLEACH outperforms LEACH by a factor of 1.5 in delivering data to the base station and outlives the network driven by LEACH protocol by 30–40%.</p>\u0000 </div>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2020.0048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86329762","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}

引用次数: 5

Localisation of wireless nodes with partial connectivity in wireless sensor systems 无线传感器系统中部分连接无线节点的定位

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-12-01 DOI: 10.1049/iet-wss.2019.0202

Muhammad Waqas Khan, Maryam Khan, Abdul Hafeez

{"title":"Localisation of wireless nodes with partial connectivity in wireless sensor systems","authors":"Muhammad Waqas Khan, Maryam Khan, Abdul Hafeez","doi":"10.1049/iet-wss.2019.0202","DOIUrl":"10.1049/iet-wss.2019.0202","url":null,"abstract":"<div>\u0000 <p>Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.</p>\u0000 </div>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2019.0202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78749222","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}

引用次数: 1

ZigBee wireless smart plug network with RSSI multi-lateration-based proximity estimation and parallelised machine learning capabilities for demand response ZigBee无线智能插头网络，具有RSSI基于多方的接近估计和并行机器学习功能，用于需求响应

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-12-01 DOI: 10.1049/iet-wss.2018.5047

Anthony S. Deese, Julian Daum

{"title":"ZigBee wireless smart plug network with RSSI multi-lateration-based proximity estimation and parallelised machine learning capabilities for demand response","authors":"Anthony S. Deese, Julian Daum","doi":"10.1049/iet-wss.2018.5047","DOIUrl":"10.1049/iet-wss.2018.5047","url":null,"abstract":"<div>\u0000 <p>This study explores how wireless ZigBee technology may be applied to automation of electric loads in residential and commercial spaces, allowing to participate in demand response initiatives. The authors discuss development of a custom smart plug with sensing, wireless communication, and electric load actuation capabilities along with several innovative upgrades. There are many commercially available smart plugs that contain multiple sensors and relays. However, very few provide the ability to effectively estimate the proximity between modules or the ability to perform robust system-wide optimisation. The authors propose two innovative smart plug eco-system improvements. One is the use of a received signal strength indicator (RSSI) multi-lateration-based method to estimate the relative proximities of modules. The RSSI values for almost all transmission paths within the ZigBee network are acquired via the authors' forced network reconfiguration algorithm, addressing the limitations of RSSI observation within a star structure. A second innovation is the development of a parallelised neural network training method for application to load automation. The authors use a <i>k</i>-means clustering algorithm to divide training data into subsets such that training may be parallelised.</p>\u0000 </div>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2018.5047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86048981","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}

引用次数: 5

Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks 无线传感器网络中故障传感器在线识别的混合检测算法

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-12-01 DOI: 10.1049/iet-wss.2020.0053

Walaa Ibrahim Gabr, Mona A. Ahmed, Omar M. Salim

{"title":"Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks","authors":"Walaa Ibrahim Gabr, Mona A. Ahmed, Omar M. Salim","doi":"10.1049/iet-wss.2020.0053","DOIUrl":"10.1049/iet-wss.2020.0053","url":null,"abstract":"<div>\u0000 <p>Wireless sensor network (WSN) is a developed wireless network consisting of some connected sensor nodes. The WSN is employed in many fields such as military, industrial, and environmental monitoring applications. These nodes are equipped with sensors for sensing the environmental variables such as temperature, humidity, wind speed, and so on. In most applications, WSN is positioned in remote places and harsh environments, where they are most probably exposed to faults. Hence, faulty sensor identification is one of the most fundamental tasks to be considered in WSN. This study suggests a hybrid methodology based on mutual information change (MIC) and wavelet transform (WT) for faulty sensor identification. The MIC method is suggested to study correlation among sensors, while the WT technique is proposed for self-sensor detection. WT is suitable for analysing non-stationary signals into approximation and detail coefficients. The suggested algorithm performance is investigated by applying a real case study at an arbitrary location close to Cairo, Egypt. The results of each method are compared using the true positive rate (TPR), false negative rate, and accuracy measures. Obtained results have shown that combining MIC and WT techniques can achieve a higher TPR and accuracy reach 100% in most fault types.</p>\u0000 </div>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2020.0053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83904161","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}

引用次数: 3

Comparative evaluation of six wireless sensor devices in a high ionizing radiation environment 高电离辐射环境下六种无线传感装置的比较评价

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-12-01 DOI: 10.1049/iet-wss.2020.0035

Qiang Huang, Jin Jiang, Yongqiang Deng

{"title":"Comparative evaluation of six wireless sensor devices in a high ionizing radiation environment","authors":"Qiang Huang, Jin Jiang, Yongqiang Deng","doi":"10.1049/iet-wss.2020.0035","DOIUrl":"10.1049/iet-wss.2020.0035","url":null,"abstract":"<div>\u0000 <p>This paper reports the results of experimental studies of six different wireless sensor nodes and networks under a radiation environment with a dose rate of 20 K Rad (Si)/h. The wireless nodes evaluated are ZigBee, WirelessHART, ISA 100.11a, LoRa, and 433/915 MHz point-to-point devices made from commercial off-the-shelf (COTS) components. The experiments were carried out using a <sup>60</sup>Co gamma source, while the devices are at on-power operating states, and their operating statuses have been continuously monitored to determine the first instance of failure and the rate of gradual degradation in terms of communication channel performance and quality of the wireless signals. Observations indicate that the different devices and networks exhibit varying levels of radiation tolerance. For example, some can only survive for less than one hour, but others are operating satisfactorily for several hours. Furthermore, before a device suffers a fatal hardware failure, the performance degradation progresses slowly. It is believed that this is the first time that such results are reported in the open literature. Their significance is that the results can provide some practical guidance to select the most suitable wireless devices for the design and construction of remote monitoring systems for high-level radiation environments.</p>\u0000 </div>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2020.0035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84074131","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}

引用次数: 0

Metaheuristics-based energy efficient clustering in WSNs: challenges and research contributions 基于元启发式的WSNs节能聚类:挑战与研究贡献

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-10-21 DOI: 10.1049/iet-wss.2020.0102

Richa Sharma, Vasudha Vashisht, Umang Singh

引用次数: 6

Machine learning-based edge-computing on a multi-level architecture of WSN and IoT for real-time fall detection 基于机器学习的边缘计算，基于WSN和IoT的多层次架构，用于实时跌倒检测

IF 1.9

IET Wireless Sensor Systems Pub Date : 2020-10-21 DOI: 10.1049/iet-wss.2020.0091

Amina El Attaoui, Salma Largo, Soufiane Kaissari, Achraf Benba, Abdelilah Jilbab, Abdennaser Bourouhou

{"title":"Machine learning-based edge-computing on a multi-level architecture of WSN and IoT for real-time fall detection","authors":"Amina El Attaoui, Salma Largo, Soufiane Kaissari, Achraf Benba, Abdelilah Jilbab, Abdennaser Bourouhou","doi":"10.1049/iet-wss.2020.0091","DOIUrl":"10.1049/iet-wss.2020.0091","url":null,"abstract":"<div>\u0000 <p>Health telemonitoring systems are constrained by the computational and data transmission load resulting from the large volumes of various measured signals, e.g. in the fall detection application. Nevertheless, the trend of movement and the implementation of computer intelligence in intelligent devices ensure an intelligent and convenient method for continuous real-time telemonitoring of health conditions. In this paper, fall detection is presented while leveraging edge computing integrated on a multi-level architecture combines the Wireless Sensors Network and the Internet of Things. Particularly, we present a complete study and implementation scenarios while investigating the performances of machine learning algorithms to distinguish between different fall patterns and activities of daily living using a set of significant extracted features from measured acceleration and angular velocity signals. For low computational requirements and to improve the classification performances, the Linear Discriminant Analysis is used to reduce the dimensionality of extracted features. The experimental results assess the performances of the proposed approach in fall detection that show the highest accuracy of 99.92% provided using the KNN classifier and accuracy of 97.5% for fall pattern recognition using the SVM classifier. Also, the online classification on the Fog device reached an accuracy of 94.42% using the SVM classifier.</p>\u0000 </div>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2020.0091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74229588","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}

引用次数: 11