A unified framework for constrained linearization of 2D/3D sensor networks with arbitrary shapes

Author(s):  
Yufu Jia ◽  
Wenping Liu ◽  
Hongbo Jiang ◽  
Yamin Li ◽  
Guoyin Jiang ◽  
...  
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 112777-112791
Author(s):  
Yufu Jia ◽  
Wenping Liu ◽  
Guoyin Jiang ◽  
Hongbo Jiang ◽  
Yamin Li ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Alexandros Karagiannis ◽  
Demosthenes Vouyioukas

Body sensor networks and implantable and ingestible medical devices energy efficiency is a substantial key factor in network lifetime and functionality. This work confronts the nodes’ energy problem by establishing a unified energy consumption framework comprised of theoretical model, energy simulator model, and electronic metering modules that can be attached to the nodes. A theoretical analysis, a simulation procedure, and the design and development of three prototype electronic metering modules are presented in this paper. We discuss the accuracy of the proposed techniques, towards a unified framework for the aprioriestimation of the energy consumption in commercial sensor nodes, taking into account the application functionality and the energy properties of the incorporated electronics. Moreover, body network nodes are considered for the application and the measurements of the proposed framework.


2019 ◽  
Vol 28 (05) ◽  
pp. 1930005 ◽  
Author(s):  
Sergio Diaz ◽  
Diego Mendez ◽  
Rolf Kraemer

We present the state-of-the-art related to self-organizing and self-healing techniques. On the one hand, self-organization is the nodes’ ability to construct a network topology without any human intervention and any previous topology knowledge. On the other hand, self-healing is the network’s ability to recover from failures by using hardware and software redundancies. By using both techniques, Wireless Sensor Networks (WSNs) can be deployed in unattended and harsh environments where on-site technical service is unfeasible. In the last few years, a large amount of work has been done in these two research areas, but these different techniques occur at different layers and with no general classification or effort to consolidate them. One of the contributions of this paper is the consolidation of the most significant and relevant mechanisms in these two areas, and additionally, we made an effort to organize and classify them. In this review, we explain in detail the two stages of self-organization, namely topology construction and management. Moreover, we present a comprehensive study of the four steps in a self-healing technique, namely, information collection, fault detection, fault classification and fault recovery. By introducing relevant work, comparative tables, and future trends, we provide the reader with a complete picture of the state-of-the-art. Another contribution is the proposal of a unified framework that employs self-organizing and self-healing mechanisms to achieve a fault-tolerant network.


2015 ◽  
Vol 64 (5) ◽  
pp. 1323-1335 ◽  
Author(s):  
Wenping Liu ◽  
Hongbo Jiang ◽  
Yang Yang ◽  
Xiaofei Liao ◽  
Hongzhi Lin ◽  
...  

2018 ◽  
Vol 32 (24) ◽  
pp. 1850283 ◽  
Author(s):  
Bharti Saneja ◽  
Rinkle Rani

Wireless sensor networks (WSNs) are ubiquitous nowadays and have applications in variety of domains such as machine surveillance, precision agriculture, intelligent buildings, healthcare etc. Detection of anomalous activities in such domains has always been a subject undergoing intense study. As the sensor networks are generating tons of data every second, it becomes a challenging task to detect anomalous events accurately from this large amount of data. Most of the existing techniques for anomaly detection are not scalable to big data. Also, sometimes accuracy might get compromised while dealing with such a large amount of data. To address these issues in this paper, a unified framework for anomaly detection in big sensor data has been proposed. The proposed framework is based on data compression and Hadoop MapReduce-based parallel fuzzy clustering. The clusters are further refined for better classification accuracy. The modules of the proposed framework are compared with various existing state-of-art algorithms. For experimental analysis, real sensor data of ICU patients has been taken from the physionet library. It is revealed from the comparative analysis that the proposed framework is more time efficient and shows better classification accuracy.


Sign in / Sign up

Export Citation Format

Share Document