A Routing Metric for Load Balance in Wireless Mesh Networks

Yang, Sooyeol; Li, Jilong; Yun, Jangkyu; Lee, Icksoo; Han, Kijun

doi:10.1109/mmit.2008.204

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Another metric to achieve load balancing is named ETT-LB [35] which considers the transmission rate and size of the packet in addition to the expected delay time. Similarly, the authors in [36] proposed another load balancing technique called ALABAMO, which considers the node selection using both the ETX and the traffic load profiles of the nodes.…”

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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by considering the link transmission rate and packet size, adding to it the Expected Delay Time (EDT), which is the average link load at a node in order to achieve load balancing.…”

Section: Related Workmentioning

confidence: 99%

Multiple Instances QoS Routing in RPL: Application to Smart Grids

Nassar

Berthomé

Dubrulle

et al. 2018

Sensors

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The Smart Grid (SG) aims to transform the current electric grid into a “smarter” network where the integration of renewable energy resources, energy efficiency and fault tolerance are the main benefits. This is done by interconnecting every energy source, storage point or central control point with connected devices, where heterogeneous SG applications and signalling messages will have different requirements in terms of reliability, latency and priority. Hence, data routing and prioritization are the main challenges in such networks. So far, RPL (Routing Protocol for Low-Power and Lossy networks) protocol is widely used on Smart Grids for distributing commands over the grid. RPL assures traffic differentiation at the network layer in wireless sensor networks through the logical subdivision of the network in multiple instances, each one relying on a specific Objective Function. However, RPL is not optimized for Smart Grids, as its main objective functions and their associated metric does not allow Quality of Service differentiation. To overcome this, we propose OFQS an objective function with a multi-objective metric that considers the delay and the remaining energy in the battery nodes alongside with the dynamic quality of the communication links. Our function automatically adapts to the number of instances (traffic classes) providing a Quality of Service differentiation based on the different Smart Grid applications requirements. We tested our approach on a real sensor testbed. The experimental results show that our proposal provides a lower packet delivery latency and a higher packet delivery ratio while extending the lifetime of the network compared to solutions in the literature.

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“…Q-metric provides load balancing by selecting the lightest parent in terms of traffic load by solving an optimization problem and mainly considering reliability, transmission and reception power consumption. ETT-LB was proposed in [30]. It is based on the ETT metric [12], which extends ETX by considering the link transmission rate and packet size, adding to it the Expected Delay Time (EDT), which is the average link load at a node in order to achieve load balancing.…”

Section: Related Workmentioning

confidence: 99%

Towards Multi-instances QoS Efficient RPL for Smart Grids

Nassar¹,

Gouvy

Mitton

2017

Proceedings of the 14th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, &Amp; Ubiquitous Networks

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The Internet of Thing is an ongoing revolution which promises to interconnect most of our world with billions of connected devices. Hence, data routing and prioritization in IoT is a main challenge in this gigantic network. This is all the more true for the Smart Grids data management where heterogeneous applications and signalling messages have different requirements in terms of reliability, latency and priority. So far, standards on Smart Grid have recommended the use of RPL (Routing Protocol for Low-Power and Lossy networks) protocol for distributing commands over the grid. RPL assures Quality of Service (QoS) at the network layer in wireless sensor networks through the logical subdivision of the network in multiple instances, each one relying on a specific Objective Function. However, RPL is not optimized for Smart Grids, as its main objective function and its associated metric does not allow for QoS differentiation. In order to overcome this, we propose OFQS an objective function with a multi-objective metric that considers the delay and the remaining energy in the battery nodes alongside with the quality of the links. Our function automatically adapts to the number of instances (traffic classes) providing a QoS differentiation based on the different Smart Grid applications requirements. Simulations show that our proposal provides a low packet delivery latency and a higher packet delivery ratio while extending the lifetime of the network compared to literature solutions.

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A Routing Metric for Load Balance in Wireless Mesh Networks

Cited by 8 publications

References 4 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

Multiple Instances QoS Routing in RPL: Application to Smart Grids

Towards Multi-instances QoS Efficient RPL for Smart Grids

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