Rate Optimization Scheme for Node Level Congestion in Wireless Sensor Networks

Prabakaran, N.; Raja, B. Shanmuga; Prabakaran, R.; Dhulipala, V. R. Sarma

doi:10.1109/icdecom.2011.5738548

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Rate optimization for node level congestion is another scheme that is used to solve the congestion problem by avoiding the buffer overflow for each WSN node [15]. The main disadvantage of this technique is rate adjustment dependency.…”

Section: Related Workmentioning

confidence: 99%

Software-Fault Mitigation for Derivation of Quality of Services (QoS) in Wireless Sensor Networks (WSN)

Yaseen¹,

Dubey²

2020

I2M

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A ‘Wireless Sensor Network’ (WSN) is a network of autonomous sensors spread out in any environment that is required for the surveillance of environment’s physical condition like pressure, temperature, humidity etc. These sensor networks are used in extreme environmental conditions which can lead to their failure and the damage of the entire environment. Thus, fault detection methods are the need of the hour. Fault tolerance, which is considered a challenging task in these networks, is defined as the ability of the system to offer an appropriate level of functionality in the event of failures. In order to provide better QoS, it is essential that faulty nodes should be diagnosed and handled timely without affecting the underlying work of the network. The present study proposed a throughput efficient mechanism in order to improve fault tolerance of the system against software faults. Since the proposed methodology works on the input variables that are collected on real time basis thus adding to its efficiency in fault detection process. The result shows that our proposed work diagnosis different software faults and during fault diagnosis it is able to maintain the desired throughput. The efficiency of the proposed algorithm is achieved by comparing it with the previous algorithms so far present in the literature.

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Section: Related Workmentioning

confidence: 99%

Software-Fault Mitigation for Derivation of Quality of Services (QoS) in Wireless Sensor Networks (WSN)

Yaseen¹,

Dubey²

2020

I2M

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“…One way of controlling node level congestion is to use data rate control [8]. The second method is resource control [9] where more network resources like bandwidth and additional routes are provided to share the load.…”

Section: Introductionmentioning

confidence: 99%

Congestion bottleneck avoid routing in wireless sensor networks

Thomas¹,

Mathew²

2019

IJECE

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A new efficient method for detecting congested bottleneck nodes and avoiding them in the route formation in a wireless sensor network is described. Sensor nodes with a higher degree of congestion are excluded while determining the best routing path from a given source to destination in a multi-hop transmission. In a scenario where different communication paths have different maximum congestion levels, selecting that path which has least maximum congestion, is a challenging task. A modified Bellman-Ford algorithm is proposed to solve this problem efficiently. The proposed technique is very much useful for the optimal route selection for vehicles in metropolitan cities that avoids high traffic density junctions. Once the desired destination is specified, the traffic control system can use this algorithm to provide the least congested routes to the intra-city vehicles.

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“…It has a capability of sensing, processing, and communication over a wireless channel and to withstand in harsh environment [1,2].wherever sensor nodes sense physical phenomenon begin report the event through wireless links to sink (base station). The sink node is most powerful which is used as gateway to the wired or other wireless network and it is doing data collecting and processing [3,4]. Once a target event occurs, a sudden surge of data traffic will be triggered by all sensor nodes in the event area, which may easily lead to network congestion when offered traffic load exceeds practical network capacity.…”

Section: Introductionmentioning

confidence: 99%

“…Link level congestion occurs when multiple active sensor nodes within range of one another attempt to access transmission medium simultaneously, packets that leave the buffer may fail to reach next hop as a result of collision among sensor nodes, as shown in Figure (1). Figure 1: Congestion in a wireless sensor network [3] Based on these considerations, this paper aims to address these problems by providing a congestion control scheme based on Modified Neural Network with wavelet activation…”

Section: Introductionmentioning

confidence: 99%

Traffic Management in Wireless Sensor Network Based on Modified Neural Networks

Shiltagh

Faisal

2014

ijci

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Wireless Sensor Networks (WSNs) are event-driven network systems consist of many sensors node which aredensely deployed and wirelessly interconnected that allow retrieving of monitoring data. In Wireless sensor network,whenever an event is detected, the data related to the event need to be sent to the sink node (data collection node). Sink nodeis the bottleneck of network there may be chance for congestion due to heavy data traffic. Due to congestion, it leads to dataloss; it may be important data also. To achieve this objective, soft computing based on Neural Networks (NNs) CongestionController approach is proposed. The NN is activated using wavelet activation function that is used to control the traffic ofthe WSN. The proposed approach which is called as Modified Neural Network Wavelet Congestion Control (MNNWCC), hasthree main activities: the first one is detecting the congestion as congestion level indications; the second one is estimated thetraffic rate that the upstream traffic rate is adjusted to avoid congestion in next time, the last activates of the proposedapproach is improved the Quality of Services (QoS), by enhancement the Packet Loss Ratio (PLR), Throughput (TP), BufferUtilization (BU) and Network Energy (NE) . The simulation results show that the proposed approach can avoid the networkcongestion and improve the QoS of network.

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Rate Optimization Scheme for Node Level Congestion in Wireless Sensor Networks

Cited by 10 publications

References 9 publications

Software-Fault Mitigation for Derivation of Quality of Services (QoS) in Wireless Sensor Networks (WSN)

Software-Fault Mitigation for Derivation of Quality of Services (QoS) in Wireless Sensor Networks (WSN)

Congestion bottleneck avoid routing in wireless sensor networks

Traffic Management in Wireless Sensor Network Based on Modified Neural Networks

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