ACSRO: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks

Narawade, Vaibhav; Kolekar, Uttam D.

doi:10.1016/j.aej.2016.10.005

Cited by 28 publications

(12 citation statements)

References 29 publications

(47 reference statements)

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“…The remaining list of parameters used for simulations is shown in Table 2. The proposed algorithm is compared with the nature-inspired algorithm ACSRO [19] and traditional congestion avoidance algorithm CODA [13]. The performance of the proposed algorithm is evaluated against the average throughput, average hop-by-hop delay, and packet delivery ratio and node death percentage.…”

Section: Simulation and Results Discussionmentioning

confidence: 99%

“…It uses a fitness function for share rate, provided the congestion of minimum drop at the congested link. This method maximizes the system performance [19][20][21]. Flock-CC is based on bird behavior proposed by Antoniou et al This approach adopts a swarm intelligence paradigm where packets are guided to form a flock and flow towards the sink like a global attractor.…”

Section: Literature Reviewmentioning

confidence: 99%

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Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

et al. 2021

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Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource-restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP-hard and it has been realized that a heuristic-based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA-HA) to improve the network performance. This approach is a combination of traffic-oriented and resource-oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion-free alternate paths. The forward ant constructs multiple congestion-free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.

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Section: Simulation and Results Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

et al. 2021

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“…Every parent disperses energy parent W , in the response of signals from all child nodes of which cluster, signals aggregation, and aggregate signal transmission to BS. Therefore, the energy dissipated [7] is stated in eq. (9-11) in the parent node.…”

Section: Energymentioning

confidence: 99%

“…( 9), DA E indicates energy exploited by Parent for aggregation of data. At present, energy utilization in a cluster stated in [7] is computed by eq. (12).…”

Section: Energymentioning

confidence: 99%

Hybrid Crow Search and Grey Wolf Optimization Algorithm for Congestion Control in WSN

Daniya¹

2020

JNACS

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In this paper, a novel congestion control method is developed for WSN. Here, a congestion control technique based on the multi-objective optimization algorithm called Hybrid Crow Search (CS) and Grey Wolf Optimization (GWO) named (CSGWO) algorithm for rate optimization and regulating data arrival rate to parent node from each child node is proposed. The multi-objective optimization model considers node energy in its fitness model. The priority-based transmission is set up as an optimization algorithm that controls the arrival rate based on priority: child node energy and output available bandwidth. To alleviate the congestion, rate modification to optimum value is exploited. The novel method is evaluated with conventional methods. Finally, the experimentation outcomes show that the developed model has superior outcomes comparing with the conventional algorithms.

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“…A Secure Selective Dropping Congestion Control (S 2 DCC) mechanism has been proposed by Tortelli et al [33] to improve the image quality at the sink node by adopting different strategies such as end-to-end ciphering, hierarchical and hybrid network design, in-network selective data dropping, and scalable multimedia encoding. Recently, nature inspired computing algorithms such as cuckoo search [34], particle swarm [35] algorithms, Imperialistic Competitive Algorithm (ICA) [36] have been employed for optimizing the rate adjustment at the child node and the parent node for congestion avoidance. In order to maintain the high data accuracy in a cyber physical system, the issue of congestion has been resolved by Zhuang et al [37] with their proposed Congestion-Adaptive Data Collection scheme (CADC).…”

Section: Related Workmentioning

confidence: 99%

Priority Based Adaptive Rate Control in Wireless Sensor Networks: A Difference of Differential Approach

Swain

Nanda

2019

IEEE Access

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Wireless Sensor Networks (WSN), when deployed in a real world scenario have to deal with a wide variety of data. Because of the constrained bandwidth, different types of data need to be handled with different priorities in order to prevent congestion in a network. Traffic class priority based rate control algorithms have been proposed by Yaghmaee et al. for congestion avoidance and these are based on the rate difference at a given node. In this research work, we have proposed a Difference of Differential Rate Control (DDRC) algorithm at a given node, which is based on the difference of differential rate between sink node and the given node. The next proposed one is Weighted Priority Difference of Differential Rate Control (WPDDRC) algorithm which is based on the combination of the weighted priority of the traffic class and the difference of differential rate of a given node. This is intended to handle Real Time (RT) data, which may be bursty in nature, and also a combination of the RT and Non-Real Time (NRT) data. Though the proposed algorithms are validated on a fixed topology, they are also valid for the general topology of WSN. The proposed algorithms have been implemented in NS3 platform and the performances of the algorithms have been found to be superior to those of counterpart techniques. INDEX TERMS Rate control, traffic class priority, wireless sensor networks.

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ACSRO: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks

Cited by 28 publications

References 29 publications

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Hybrid Crow Search and Grey Wolf Optimization Algorithm for Congestion Control in WSN

Priority Based Adaptive Rate Control in Wireless Sensor Networks: A Difference of Differential Approach

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