RETRACTED ARTICLE: QoS and QoE Enhanced Resource Allocation for Wireless Video Sensor Networks Using Hybrid Optimization Algorithm

Ramesh, S.; Yaashuwanth, C.

doi:10.1007/s10766-018-0581-y

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Statistical criteria are used to rank features, and the highest-ranking features are chosen [15,16]. Consistency based feature selection (CNF) [16] and Correlation based feature selection (CFS) [17][18][19][20][21][22][23] are the filtering methods employed. This work has presented a solution for WSN and artificial neural networks through intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

Rameshkumar¹,

Ganesan²,

Merline³

2023

Computer Systems Science and Engineering

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In The Wireless Multimedia Sensor Network (WNSMs) have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets. By utilising portable technologies, it achieves solid and significant results in wireless communication, media transfer, and digital transmission. Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature, moisture content, and other environmental conditions in recent decades. WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appropriate audio and video information. Many video sensor network studies focus on lowering power consumption and increasing transmission capacity, but the main demand is data reliability. Because of the obstacles in the sensor nodes, WMSN is subjected to a variety of attacks, including Denial of Service (DoS) attacks. Deep Convolutional Neural Network is designed with the state-action relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture. The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network. It reduces the attacker detection delay and improves the detection accuracy. In order to protect the network against DoS assaults, an improved machine learning technique must be offered. An efficient Deep Neural Network approach is provided for detecting DoS in WMSN. The required parameters are selected using an adaptive particle swarm optimization technique. The ratio of packet transmission, energy consumption, latency, network length, and throughput will be used to evaluate the approach's efficiency.

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Section: Related Workmentioning

confidence: 99%

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

Rameshkumar¹,

Ganesan²,

Merline³

2023

Computer Systems Science and Engineering

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show abstract

“…A close observation and monitoring of the intended environmental changes and the physical phenomenon and continuous reporting of all the changes or incidents of important activities recorded at a remote location over a wireless ad hoc network and executed by a small‐sized group of sensing nodes separated by equal or unequal space over a geographical area are the basic operations of a WSN, and the physical or environmental state is not limited to pressure gauging, sound detection and temperature fluctuations [4]. Many QoS improvements are needed to get a flawless sensing network [5]. The current view of WSN growth is pushed hard by the immense need for applications in the defence and space departments, specifically surveillance in the battlefield including border monitoring and auto detection of intruders.…”

Section: Introductionmentioning

confidence: 99%