An Optimization Model to Minimize the Expected End-to-End Transmission Time in Wireless Mesh Networks

Silva, Marlon da; Senne, Edson Luiz França; Vijaykumar, Nandamudi Lankalapalli

doi:10.1590/0101-7438.2017.037.02.0209

Cited by 6 publications

(2 citation statements)

References 14 publications

(23 reference statements)

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“…The optimization model proposed in [55] aims to reduce the expected end-to-end transmission time. The proposed method determines the weighted cumulative expected transmission time (WCETT) which depends on the number of links in the network.…”

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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“…In other research [ 27 ] an “optimization model to minimize the expected end-to-end transmission time in wireless mesh networks”, this model is based on the Weighted Cumulative ETT (WCETT) metric, has been proposed by da Silva et al WCETT focuses on fixed nodes of local networks, where the goal is to select a high-throughput path to avoid any loss of information between the source and its destination. The metric assigns weights to the individual links, which are called Expected Transmission Time (ETT).…”

Section: Related Workmentioning

confidence: 99%

Sigma Routing Metric for RPL Protocol

Sanmartin

Rojas

Fernández

et al. 2018

Sensors

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This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX). However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption.

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Interference aware intelligent routing in cognitive radio based Vehicular Adhoc networks for smart city applications

Sethi,

Mahapatro

2023

Int. j. inf. tecnol.

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An Optimization Model to Minimize the Expected End-to-End Transmission Time in Wireless Mesh Networks

Cited by 6 publications

References 14 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

Sigma Routing Metric for RPL Protocol

Interference aware intelligent routing in cognitive radio based Vehicular Adhoc networks for smart city applications

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