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Congestion Control in Internet of Things Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 3442

Special Issue Editors


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Guest Editor
Faculty of Technical Physics, Computer Science and Applied Mathematics, Lodz University of Technology, 90-924 Lodz, Poland
Interests: networked systems; logistic systems; inventory control; robust control; optimal control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Information Technology, Lodz University of Technology, 90924 Lodz, Poland
Interests: communication networks; congestion control; distributed systems; network control systems; industrial networks; cybersecurity; game theory

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Guest Editor
Colleague of Physical Science and Technology, Central China Normal University, Wuhan 430079, China
Interests: communication networks; chaos; uncertain systems; adaptive control; convex programming; artificial intelligence; robust control; variable structure systems

Special Issue Information

Dear Colleagues,

Information systems have evolved from centralized, through distributed, to Internet of Things (IoT) architectures. While different in type and scale, ranging from simple home monitoring to complex Industry 4.0 installations, and constructed using different tools and technologies, all require an efficient communication platform to fulfill their purpose.

When dedicated links are not available, in particular, when the cost and flexibility considerations dictate using wireless media, the streams generated by the system components interfere with each other and with unrelated traffic, threatening the realization of application-level functionality. Meanwhile, classical congestion control solutions, involving drops or delay changes as network state indicators, fall short of expectations in the noisy, wireless environment in which temporal conditions undergo significant variations. Therefore, in IoT communication, other measures and congestion control mechanisms should be provided to answer the ever more stringent quality objectives.

This Special Issue targets the current challenges of shaping data transfer in the uncertain, dynamic networking environment of IoT systems. New traffic models, protocols, and control algorithms are needed to handle the diversity of application requirements, while at the same time obeying the resource, energy, and robustness constraints of physical devices.

Dr. Przemysław Ignaciuk
Dr. Michał Morawski
Prof. Dr. Shaocheng Qu
Guest Editors

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Keywords

  • congestion control
  • Internet of Things
  • sensor networks
  • networked control systems
  • communication system modeling
  • quality of service
  • fairness
  • robustness
  • security and safety

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Published Papers (1 paper)

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Research

14 pages, 2430 KiB  
Article
Enhanced Message Replication Technique for DTN Routing Protocols
by Siham Hasan, Meisam Sharifi Sani, Saeid Iranmanesh, Ali H. Al-Bayatti, Sarmadullah Khan and Raad Raad
Sensors 2023, 23(2), 922; https://doi.org/10.3390/s23020922 - 13 Jan 2023
Cited by 8 | Viewed by 2263
Abstract
Delay-tolerant networks (DTNs) are networks where there is no immediate connection between the source and the destination. Instead, nodes in these networks use a store–carry–forward method to route traffic. However, approaches that rely on flooding the network with unlimited copies of messages may [...] Read more.
Delay-tolerant networks (DTNs) are networks where there is no immediate connection between the source and the destination. Instead, nodes in these networks use a store–carry–forward method to route traffic. However, approaches that rely on flooding the network with unlimited copies of messages may not be effective if network resources are limited. On the other hand, quota-based approaches are more resource-efficient but can have low delivery rates and high delivery delays. This paper introduces the Enhanced Message Replication Technique (EMRT), which dynamically adjusts the number of message replicas based on a node’s ability to quickly disseminate the message. This decision is based on factors such as current connections, encounter history, buffer size history, time-to-live values, and energy. The EMRT is applied to three different quota-based protocols: Spray and Wait, Encounter-Based Routing (EBR), and the Destination-Based Routing Protocol (DBRP). The simulation results show that applying the EMRT to these protocols improves the delivery ratio, overhead ratio, and latency average. For example, when combined with Spray and Wait, EBR, and DBRP, the delivery probability is improved by 13%, 8%, and 10%, respectively, while the latency average is reduced by 51%, 14%, and 13%, respectively. Full article
(This article belongs to the Special Issue Congestion Control in Internet of Things Systems)
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