Congestion control in Wireless Sensor Networks by using Differed Reporting Rate

Deshpande, Vinayak; Chavan, PratibhaP.; Wadhai, Vijay M.; Helonde, J. B.

doi:10.1109/wict.2012.6409076

Cited by 14 publications

(13 citation statements)

References 9 publications

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“…Some papers count the total number of packets received by the server (sink node) and call it goodput [35]. Fairness: is an indication of fair allocation of network resources (e.g., bandwidth) among nodes in the network, e.g., the sink node receives equal number of packets from each node [36]. Some papers use weighted fairness to achieve different throughput according to nodes' priority and importance [37].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…This metric does not take into account the wireless channel loss [43]. Packet loss rate (packet loss ratio): is the ratio between the total number of lost packets and the total number of sent packets in the network [36]. Some papers call it loss probability [42].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…Queue length (queue level): this metric shows the average number of packets stored in the nodes' buffer over time [45], [46]. Packet delivery ratio: is the ratio between the number of successfully received packets at the sink node to the total number of sent packets in the network [36], [47]. Some papers call it packet reception rate [40], [45].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…In [36] Deshpande et al proposed an algorithm called differed reporting rate (DRR) that controls congestion in WSNs. They develop a mathematical model to control the flow of data packets through the network.…”

Section: A Traffic Control Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

et al. 2018

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The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service (QoS) aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: A Traffic Control Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

et al. 2018

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“…Deshpande et al [9], assumes that the initial reporting rate of the nodes near the sink is to be kept minimum and the rate increases as it go away from the sink node. It contains three modules; Congestion Detection Algorithm, Differed Reporting Rate Algorithm and Adaptive Flow Rate Control Algorithm (AFRC).…”

Section: B Congestion Control Techniques For Terrestrial Sensor Netwmentioning

confidence: 99%

Performance evaluation of congestion-aware routing protocols for underwater sensor networks with multimedia data

Jain

Pilli

Govil

et al. 2015

2015 IEEE Underwater Technology (UT)

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Performance advancementsin acoustic communication technology have fostered to provide technical platform for numerous interdisciplinary applications ranging from bathymetry, hydrographic surveys, disaster prevention, to tactical surveillance over underwater sensor networks (UWSN). Supporting real-time data transmission over error-prone UWSN is increasingly important as these networks become more widely deployed. Existing UWSN routing protocols caters the requirements of non-real time applications where as delay sensitive applications requires solutions that can improve efficiency and reliability, often dynamically throughout the event detection and data transmission session. Congestion control is vital to achieve a high throughput and a long network lifetime. In particular, it is important for a routing protocol to provide congestion control by incorporating metrics like throughput, delay, packet loss ratio, etc. This paper presents a comparative analysis of UWSN routing protocols over real time multimedia data, using H.265/HEVC encoded video sequences. The aim of this study is to extend the in-built support in UWSN routing protocols for transmission of real time data traffic. The applicability of congestion control protocols in UW framework has been explored and the performance characteristics of the protocols have been studied under different multimedia load conditions with varying mobility. This methodology has been illustrated using the case studies in the military and ocean monitoring domains.

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Congestion Control for 6LoWPAN Wireless Sensor Networks: Toward the Internet of Things

Al-Kashoash¹

2020

Springer Theses

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Congestion control in Wireless Sensor Networks by using Differed Reporting Rate

Cited by 14 publications

References 9 publications

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

Performance evaluation of congestion-aware routing protocols for underwater sensor networks with multimedia data

Congestion Control for 6LoWPAN Wireless Sensor Networks: Toward the Internet of Things

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