An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

Naz

Computational Intelligence and Neuroscience

et al. 2022

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: Introductionmentioning

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

“…The numerical content of the measured dataset was scaled to a new scale in this step, allowing it to be easily fed into the network as input. In Figure 4(c), the minimum and maximum normalization with values of 0.0,+0.5 was used in intel berkeley research lab dataset as given by (1).…”

Section: Normalizationmentioning

confidence: 99%

“…The new technological development has allowed more sensors to be associated with the internet of things (IoT). The number of devices connected to the IoT will exceed 20 billion by 2021, and the data generated from these devices will reach more than 2.5 quintillion bytes per day [1]- [3]. Moreover, people's desire to sense and collect data has increased exceedingly due to the effect of artificial intelligence (AI) technology on their ability to use data.…”

Section: Introductionmentioning

confidence: 99%

Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks

Abdalkafor

Aliesawi

2022

Bulletin EEI

Wireless sensor network (WSN) is one of the most promising technologies due to its size, cost-effective nature, and its ability to easily deploy in the target environment, as well as for its entry into many sensitive applications. However, making the most of the potential of this network is very difficult due to many issues, including the data received from the sensor nodes contains a huge amount of data redundant that negatively affects the overall network performance. Recent years have witnessed an increasing interest in data aggregation technology intending to eliminate redundant data from neighboring sensor nodes before transferring to the base station, thus improve performance efficiency and increasing the wireless sensor networks lifespan. This paper focused on applying three intelligent algorithms (SOM, HAC, and RBF) and describing the impact of data aggregation strategy on WSNs through the results obtained. As well as, an accurate description of the literature that applied these algorithms. A Competitive classification accuracy has been achieved when the proposed work is implemented and tested via the intel berkeley research lab dataset.

JOIV : Int. J. Inform. Visualization

“…Transmission urgency u is packet urgency (Cc) according to the importance of the application layer, transmission hops (Hc) indicating the retransmission cost, residual energy (Ec), and the proportional load of the queue (queue's) proportional load), and the relation is as follows. [13,14] = × + + +…”

Section: B Proposed Mac Protocolmentioning

confidence: 99%

Data Fairness Transmission and Adaptive Duty Cycle through Machine Learning in wireless Sensor Networks

Jeon

Park

2022

In this paper, we propose the data fairness transmission and adaptive duty cycle through machine learning in wireless sensor networks. The mechanism of this paper is mainly composed of two parts. The proposed mechanism is based on the sleep-wake structure, which is one of the methods to increase the lifespan of the entire network by efficiently using the energy of the nodes. The first is a mechanism to support priority and data fairness. To this end, data input to the node is divided into priority classes according to transmission urgency and stored. Introduces the concept of cross-layer to rearrange data destined for the same destination. In addition, we propose a fair data transmission mechanism that allows even low-priority data to participate in transmission after a certain period. The second is an adaptive duty cycle mechanism through machine learning. For this purpose, public data related to forest fires are collected. The collected data is refined into data for each forest fire location and data for each forest fire time. For the refined data, an SVM (Support Vector Machine) model of supervised learning is used for machine learning, and a mechanism for adaptively adjusting the duty cycle of each node through the trained model is proposed. The computer language used for machine learning is Python language, and Google's Psychic Learn is used for the machine learning library. It was compared with the existing MAC protocol for evaluation, and it was confirmed that excellent energy efficiency results were obtained.