A Comprehensive Analysis of Congestion Control Protocols in Wireless Sensor Networks

Jan, Mian Ahmad; Jan, Syed Rooh; Alam, Muhammad Mahtab; Akhunzada, Adnan; Rahman, Izaz Ur

doi:10.1007/s11036-018-1018-y

Cited by 57 publications

(26 citation statements)

References 34 publications

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“…This is because the energy insufficiency of sensor nodes limits their operational life [39], and whenever the residual energy of a sensor node is exhausted, this node becomes inactive thus causing coverage holes. Consequently, the use of schemes aiming at the achievement of power control [40], data aggregation [41,42], energy efficiency [43][44][45], energy balancing [46][47][48], data compression and restoration [49][50][51], congestion avoidance, and congestion control [52][53][54][55][56] should be also considered. Likewise, the preservation of network connectivity is an issue of crucial importance for WSNs in order to be operational and thus has to be pursued [57][58][59][60].…”

Section: Discussionmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

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The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.

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Section: Discussionmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

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“…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.…”

Section: Open Research Issuesmentioning

confidence: 99%

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

2020

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Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why energy conservation is considered as the most important research concern for WSNs. Radio communication is the utmost energy consuming function in a WSN. Thus, energy efficient routing is necessitated to save energy and thus prolong the lifetime of WSNs. For this reason, numerous protocols for energy efficient routing in WSNs have been proposed. This article offers an analytical and up to date survey on the protocols of this kind. The classic and modern protocols presented are categorized, depending on i) how the network is structured, ii) how data are exchanged, iii) whether location information is or not used, and iv) whether Quality of Service (QoS) or multiple paths are or not supported. In each distinct category, protocols are both described and compared in terms of specific performance metrics, while their advantages and disadvantages are discussed. Finally, the study findings are discussed, concluding remarks are drawn, and open research issues are indicated.

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“…At the same time, this approach substitutes the outliers with relatively accurate data. At level 2 nodes, this approach is used to (a) improve the quality of service (QoS) locally, eg, the throughput and end‐to‐end reliability within a cluster, and (b) minimize the congestion problem globally by discarding redundant data packets …”

Section: Introductionmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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A Comprehensive Analysis of Congestion Control Protocols in Wireless Sensor Networks

Cited by 57 publications

References 34 publications

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

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