Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks

Naz

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Section: Related Workmentioning

confidence: 99%

A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

Naz

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

“…As a result, a lot of redundant or raw data products that cause various types of traffic are generated in WSNs such as packet losses, high energy consumption, data representation, data accuracy, data redundancy, network overload, high transmission cost rate, data latency, congestion, data delivery ratio, etc. In recent years, various studies have been proposed to tackle these challenges and issues [86][87][88][89].…”

Section: Time-driven Modelmentioning

confidence: 99%

“…In WSNs, data redundancy is a challenging issue that arises due to temporal and spatial correlation data collection from sensor nodes. Roohullah Jan et al [86] propose two new lightweight fold aggregated techniques such as exact matching-based weight-enabled local data aggregation EMWA, and marginal weight-enabled local data aggregation MWA at the node level. For exact matching and to remove marginally aggregated data, EMWA is used to reduce redundancy by similar and average functions.…”

Section: Time-driven Modelmentioning

confidence: 99%

“…High energy cost has a harmful impact on the sensors lifetime due to redundant data, data congestion, high traffic data, and network overload. For less energy consumption, data aggregation, clustering, data redundancy reduction, and routing protocols are used in WSNs [86][87][88][89]. Previous studies used a combination of time-driven and event-driven (hybrid) models.…”

Section: Hybrid-driven Modelmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey

et al. 2021

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.

Internet of things-enabled real-time health monitoring system using deep learning

Liu

Wang

et al. 2021

Neural Comput & Applic

Smart healthcare monitoring systems are proliferating due to the Internet of Things (IoT)-enabled portable medical devices. The IoT and deep learning in the healthcare sector prevent diseases by evolving healthcare from face-to-face consultation to telemedicine. To protect athletes’ life from life-threatening severe conditions and injuries in training and competitions, real-time monitoring of physiological indicators is critical. In this research work, we present a deep learning-based IoT-enabled real-time health monitoring system. The proposed system uses wearable medical devices to measure vital signs and apply various deep learning algorithms to extract valuable information. For this purpose, we have taken Sanda athletes as our case study. The deep learning algorithms help physicians properly analyze these athletes’ conditions and offer the proper medications to them, even if the doctors are away. The performance of the proposed system is extensively evaluated using a cross-validation test by considering various statistical-based performance measurement metrics. The proposed system is considered an effective tool that diagnoses dreadful diseases among the athletes, such as brain tumors, heart disease, cancer, etc. The performance results of the proposed system are evaluated in terms of precision, recall, AUC, and F1, respectively.