Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

Souza, Paulo Silas Severo de; Rubin, Felipe; Hohemberger, Rumenigue; Ferreto, Tiago; Lorenzon, Arthur F.; Luizelli, Marcelo Caggiani; Rossi, Fábio Diniz

doi:10.1016/j.measurement.2020.108042

Cited by 30 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of data in transmission process also attracts the scholars' attentions. Therefore, in [30], in the field of precision agriculture, the authors developed a heuristic algorithm that helps to decide which anomaly detection should be selected according to the agricultural environments. It can identify faulty sensors to discard data collected by them.…”

Section: Related Workmentioning

confidence: 99%

A Balance Regulation Algorithm for Reliable Data in Hierarchical Private Cloud Architecture

Hsieh

Wang

2021

IEEE Access

View full text Add to dashboard Cite

At present, enterprises need data for data analysis. They mostly use a kind of point-to-point data transmission form, which does not have regulation mechanism in the process. However, it has the problem of low data reliability, including two parts as follows: 1) Faulty sensor affects the collected data amount of the terminal server; 2) Offensive data invade the data in transmission. In view of this, we propose the hierarchical private cloud architecture, including three aspects as follows. Firstly, we use distributed computing and virtualization capabilities of cloud computing to realize the hierarchical transmission of data. Secondly, through this mechanism of hierarchical transmission and classification algorithm of machine learning, we realize hierarchical filtering of offensive data. Finally, by combining hierarchical transmission mechanism with threshold value, classification algorithm, and limit tolerance mechanism, we regulate the data amount to monitor fault sensor in real time. Experiments are conducted to assess the proposed architecture's performance. The results show that each layer acts as a protective screen to counterattack the offensive data, which shows good robustness, real-time, and adaptive ability. Moreover, compared with OM mode, the identification efficiency of fault sensor of TM mode is improved by 2 times. Also, TM mode improves 33.33% identification acuity, which is suitable for the enterprises that are mainly based on streaming computing. In summary, the hierarchical private cloud architecture achieves the filtering of offensive data and the real-time identification of faulty sensor, which guarantees the security, accuracy, and integrity of the data transmission process. INDEX TERMS Balance regulation, cloud computing, machine learning, threshold value.

show abstract

Section: Related Workmentioning

confidence: 99%

A Balance Regulation Algorithm for Reliable Data in Hierarchical Private Cloud Architecture

Hsieh

Wang

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…e development trend of the IoT will guide the development direction of information technology. IoT has been applied in various fields such as smart homes, smart cities, industrial automation, intelligent transportation, and healthcare systems [1][2][3]. For example, the Internet of ings can be applied in smart agriculture [4].…”

Section: Introductionmentioning

confidence: 99%

Resource Scheduling Based on Unequal Clustering in Internet of Things

Bai

Zhang

Xie³

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Resource scheduling in a fair and efficient manner is a significant challenge in the Internet of Things. Although unequal clustering is an effective technique for alleviating the “energy holes” problem in multihop communication, resource scheduling based on unequal clustering is scarcely conducted. In the present study, a new resource scheduling based on unequal clustering in the Internet of Things (RSUC) is proposed. In RSUC, unequal clustering and multihop routing are considered, and the “energy holes” problem is alleviated effectively. RSUC includes resource scheduling of intracluster communication and intercluster communication. In resource scheduling of intracluster communication, according to the threshold of the number of cluster members, the cluster heads (CHs) recycle resources of failed nodes. In resource scheduling of intercluster communication, CHs in the different layers based on transmission chain obtain different sending and receiving time slots. In RSUC, CHs that are near the base station (BS) have fewer intracluster communication time slots and more intercluster communication time slots. Clusters that are further away from the BS end intercluster communication earlier and enter into intracluster communication instead of waiting for all CHs to complete intercluster communication. The simulation results reveal that RSUC significantly increases the network throughput and reduces the energy consumption of the Internet of Things.

show abstract

“…In a deterministic NDS, nodes are placed with careful planning of separation distances, elevations, and node orientations, to achieve a deployment where all nodes fall within each other’s communication range 6 . The NDS in arable land before sowing should envisage crop height and density at the maturity stage to estimate potential signal attenuation, which has been ignored in recent developments 7 – 10 . In the literature, NDSs assume that nodes are in a direct line-of-sight, overlooking the fact that when deployed for crop monitoring, the network cannot withstand increased signal attenuation as vegetation increases.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

Pal

Sharma

Tripathi

et al. 2021

Sci Rep

View full text Add to dashboard Cite

This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

show abstract

Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

Cited by 30 publications

References 25 publications

A Balance Regulation Algorithm for Reliable Data in Hierarchical Private Cloud Architecture

A Balance Regulation Algorithm for Reliable Data in Hierarchical Private Cloud Architecture

Resource Scheduling Based on Unequal Clustering in Internet of Things

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

Contact Info

Product

Resources

About