CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications

Bhandari, Khadak Singh; Hosen, A. S. M. Sanwar; Cho, Gihwan

doi:10.3390/s18113838

Cited by 51 publications

(32 citation statements)

References 36 publications

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“…The node chooses the parent that has less buffer occupancy and lower hop counts from the sink node. Bhandari et al propose a congestion-aware routing protocol (CoAR) [29] that uses a multi-criteria decision-making approach.…”

Section: Related Workmentioning

confidence: 99%

Routing protocol for Low-Power and Lossy Networks for heterogeneous traffic network

Musaddiq

Zikria

Zulqarnain

et al. 2020

J Wireless Com Network

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In a real network deployment, the diverse sensor applications generate a heterogeneous traffic pattern which may include basic sensing measurements such as temperature readings or high-volume multimedia traffic. In a heterogeneous traffic network, the two standardized objective functions (OFs), i.e., objective function zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF) for routing protocol for Low-Power and Lossy Networks (RPL) perform poor routing decisions by selecting an already congested parent node and cause more retransmissions across the network. Therefore, careful consideration is required in designing a new OF for heterogeneous traffic scenarios. In this study, we examine the RPL protocol under a heterogeneous traffic pattern and proposed a new protocol based on queue and workload-based condition (QWL-RPL). The aim of the proposed protocol is to achieve a reliable path with better overall performance. The proposed OF model considers the link workload in addition to mapping the congestion status of the node using the packet queue. We implement the proposed routing model in the Contiki operating system (OS) Cooja environment to compare with the existing technique. The simulation results show that QWL-RPL can improve the performance of a heterogeneous traffic network as compared with both OF0 and MRHOF, specifically in terms of the amount of overhead, packets reception ratio (PRR), average delay, and jitter. Final results indicate that on average, there is a 5%-30% improvement in PRR, 25%-45% reduction in overheads, 12%-30% reduction in average delay, and 20%-40% reduction in jitter.The Internet of Things (IoT) is a large system of interconnected heterogeneous devices to sense our physical environment and communicate the sensed information. Each sensor detects its vicinity and transmits the sensed information to the sink node via its neighboring nodes using multihop communication [1]. The practical applications of such a system include smart cities [2] [3], industrial and building automation, disaster management [4], smart grid, and smart healthcare [5]. A sensor discovers its neighbors, builds the topology, and routes the sensed data. An IoT system requires an efficient networking mechanism to facilitate such an interconnected heterogeneous flow of traffic. An efficient routing mechanism must consider these device characteristics and constrained resources. Furthermore, the IoT sensors usually face unfavorable environmental factors. Thus, designing of such routing protocols is a complicated task due to the resource limitations of these devices, such as limited energy, limited memory, and limited processing power [6]. For any pair of communicating nodes, a reasonable goal for such a protocol is to improve communication performance while maintaining energy consumption at a minimum level. However, achieving this goal requires careful consideration of the lossy nature of the network, fluctuating traffic patterns, varying link qualities, routing loops, and convergence time [7].Several rou...

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Section: Related Workmentioning

confidence: 99%

Routing protocol for Low-Power and Lossy Networks for heterogeneous traffic network

Musaddiq

Zikria

Zulqarnain

et al. 2020

J Wireless Com Network

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“…Another IoT routing protocol is RPL (Routing Protocol for Low-Power and Lossy Networks, [30]), which is widely used and supported by many IoT platforms and operating systems. This is reflected in the existence of many adaptations and variations for it, to enhance its performance in certain scenarios ( [31][32][33]), as well as reviews of RPL-based protocols such as the one in [34].…”

Section: The Extreme-lt Routing Protocolmentioning

confidence: 99%

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

et al. 2020

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The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.

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“…For example, Analytic Hierarchy Process, a famous MCDM technique, uses the geometric mean for a different purpose that focuses on enabling the consideration of fuzzy variables using linguistic terms . As far as we know, the use of MCDM in the context of mobile communication systems has been limited . With no doubt, the similarity between the developed framework and MCDM enables several future work opportunities.…”

Section: Analysis and Evaluationmentioning

confidence: 99%

A mathematical framework for effective routing over low power and lossy networks

Damaj

Mardini

Mouftah

2020

Int J Communication

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Summary With the rapid advancement in the Internet of things, protocols are challenged to perform routing with low power over lossy networks (RPLs). Performance analysis of RPL attracted many researchers in the field. However, to the best of our knowledge, limited or no studies have been made to develop heterogeneous analytical models that aim at the classification and ranking of RPL deployments based on combinations of desired properties. In this paper, we develop an analytical framework that captures the effectiveness of RPL and enables its sound evaluation and classification. Performance metrics include power consumption, churn in, received packets, and duty cycle, to name but a few. The obtained results based on our analytical framework confirms its effectiveness compared to the simulation results obtained in the literature. The best performance is noted for the deployments with 50 m of range and for different number of nodes.

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CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications

Cited by 51 publications

References 36 publications

Routing protocol for Low-Power and Lossy Networks for heterogeneous traffic network

Routing protocol for Low-Power and Lossy Networks for heterogeneous traffic network

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

A mathematical framework for effective routing over low power and lossy networks

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