Congestion avoidance and fault detection in WSNs using data science techniques

Abstract

Transmission rate is one of the contributing factors in the performance of wireless sensor networks. Congested network causes reduced network response time, queuing delay, and more packet loss. To address the issue of congestion, we have proposed transmission rate control methods. To avoid the congestion, we have adjusted the transmission rate at current node based on its traffic loading information. Multiclassification is done to control the congestion using an effective data science technique, namely support vector machine (SVM). In order to get less miss classification error, differential evolution (DE) and grey wolf optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE-SVM and GWO-SVM are more proficient than other classification techniques. Moreover, DE-SVM and GWO-SVM have outperformed the benchmark technique genetic algorithm-SVM by producing 3% and 1% less classification errors, respectively.For fault detection in wireless sensor networks, we have induced four types of faults in the sensor readings and detected the faults using the proposed enhanced random forest. We have made a comparative analysis with state of the art data science techniques based on two metrics, ie, detection accuracy and true positive rate. Enhanced random forest has detected the faults with 81% percent accuracy and outperformed the other classifiers in fault detection.

A Delay-Tolerant Data Congestion Avoidance Algorithm for Enterprise Cloud System Based on Modular Computing

“…In order to verify the congestion avoidance of different methods for enterprise cloud platform, and to verify the congestion avoidance of different methods, the congestion avoidance algorithm based on cross layer processing, [9] the congestion avoidance algorithm based on data science and technology, [10] the congestion avoidance algorithm based on ECODA algorithm [11] and the congestion avoidance algorithm based on modular computing algorithm are used to detect the packet loss rate of delay-tolerant data. The experimental results are shown in Fig.…”

“…In reference [9], a cross layer congestion avoidance algorithm is proposed to establish a node neighbor link in multihop wireless network, and a cross layer framework is designed to solve the problem of heterogeneous exposure and heterogeneous concealment. At the same time, the threshold of sending rate of interest packets is adjusted by cache interaction to smooth the burst traffic.…”

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

“…In addition, the presence of congestion not only obstructs the transmission of data, but it also causes packet losses and consequently necessitates packet retransmissions that deplete the energy reserves of sensor nodes. Congestion avoidance [180][181][182][183] and congestion control [184][185][186] methods are used to prevent the occurrence of upcoming congestion and eradicate existent congestion, respectively.…”

Internet of Multimedia Things (IoMT): Opportunities, Challenges and Solutions

“…Hence, these constrained devices rely upon efficient routing protocols [2] and standardized communication stack [3]. Technological advancements in 5G [4], intelligent 5G-based IoT [5], IoT operating systems (OS) [6], data-driven intelligence in wireless networks [7], scheduling approaches for heterogeneous content-centric IoT [8], congestion avoidance techniques in IoT using data science [9], vehicular ad hoc networks (VANETS) [10], Information-centric networks (ICN) [11], reinforcement learning-based solutions for next-generation networks [12,13], coexistence networks [14], IoT adaptation in agriculture [15] and Healthcare IoT [16] are helping to realize to connect everything and anywhere.…”