Al-Yad: A Wearable Sensor Network over DDS Middleware for Industrial Application

Almadani, Basem; Aliyu, Farouq; Shakshuki, Elhadi M.

doi:10.1016/j.procs.2015.07.216

Cited by 8 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They contain a processor, main memory, secondary memory, network card, and other auxiliary resources found in a personal computer. Although they have a small footprint, SBCs are strong enough to compete with smartphones and tablets [17]. Eamples of some SBCs are Beaglebone Black [18], Raspberry Pi (RPi) [19], Orange Pi [20], and Intel Galileo [21].…”

Section: Contributions Of the Researchmentioning

confidence: 99%

Energy‐efficient multi‐biometric system for Internet of Things using trust management

et al. 2021

View full text Add to dashboard Cite

The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multibiometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multibiometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.

show abstract

Section: Contributions Of the Researchmentioning

confidence: 99%

Energy‐efficient multi‐biometric system for Internet of Things using trust management

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the field of industrial production (Almadani et al, 2015), the wearable wireless network can be applied when many wired detection devices fail to perform normal operations owing to the space environment and other factors, or when a wired connection is inconvenient because of multiple detection device requirements. The method will bring convenience, timely acquisition, improve the detection effect, data processing and transmission.…”

Section: Introductionmentioning

confidence: 99%

A quadratic fusion estimating model based on the clustering kernel for real-time data in web of things

LiChao

Zhang

ZhaoYingsi

et al. 2021

IJWGS

View full text Add to dashboard Cite

Real-time data processing is a very important part of data processing in the web of things (WoT). The devices in WoT collect data and provide real-time information. The accuracy of the collected data is critical to provide valid results. Many existing methods are devoted to modifying filter algorithms. However, little attention is devoted to the inner relationship of data and data accuracy. In the present study, a quadratic filter model based on the clustering kernel is presented. First, the common filter method is used. Second, the clustering algorithm is adopted to deliver the clustering result. The attractor of the class is gained to the clustering kernel. Finally, the quadratic filter is processed according to the clustering kernel. The simulations show that the proposed model can increase the data accuracy.

show abstract

“…These nodes are a type of portable device that can be worn directly or integrated into the user’s clothes. Meanwhile, using different uploaded applications, WBNs can realize many useful functions, such as monitoring blood glucose, blood pressure, and heart functions in the medical field; motion monitoring in nursing and sports; and data collection in industry and the military [3,4]. …”

Section: Introductionmentioning

confidence: 99%

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

Zhang

Chao

2017

Sensors

View full text Add to dashboard Cite

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

show abstract

Al-Yad: A Wearable Sensor Network over DDS Middleware for Industrial Application

Cited by 8 publications

References 11 publications

Energy‐efficient multi‐biometric system for Internet of Things using trust management

Energy‐efficient multi‐biometric system for Internet of Things using trust management

A quadratic fusion estimating model based on the clustering kernel for real-time data in web of things

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

Contact Info

Product

Resources

About