Performance Analysis of Flow-Based Traffic Splitting Strategy on Cluster-Mesh Sensor Networks

She, Huimin; Lu, Zhonghai; Jantsch, Axel; Zhou, Dian; Zheng, Li

doi:10.1155/2012/232937

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…Traffic management and control necessitates a clear understanding of traffic flow operations for road transportation which constitute a necessity for enabling trade and the movement of goods and people (Yu et al, 2019). Traffic flow is the theory developed for vehicles to have its connection with pedestrians, signals, and other vehicles which are moving on the road (She et al, 2012). Measurement of the traffic flow is important in traffic management because it helps to detect traffic congestion on the roads, assess the requirement of a traffic signal at an intersection, and estimate the capacity of the roadway ( Sun and Qu, 2015).…”

Section: Introductionmentioning

confidence: 99%

Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)

Nyirajana

Coker

Akintayo

2021

EAJSTI

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Traffic flow study plays a key important in various functionalities of cities all over the world. The study of traffic flow is also viewed as an essential facility of the country when it wants to establish traffic operations patterns in the progress of road planning. Blockages are accountable for a sequence of harmful effects such as loss of time, scheduling difficulties, carbon dioxide production, and risks of accidents as well as external expenses. Besides, inadequate transportation facilities and increased traffic volume hamper urban development, especially in developing nations. The objective of the study was to assess the traffic flow state in two selected national roads of Kigali city. The traffic data were collected from 5 am to 8 pm on two National Roads (RN1 and RN3). The relationship between density and flow rate was analyzed using the fundamental diagram of traffic flow. It was found that the peak hours were from 6 am to 8 am and 5 pm to 8 pm. The highest number of vehicles counted were motorcycles due to shortcuts taken to reduce travel time. The results on RN3 revealed a proportion increase of traffic flow and density in the free-flow regime from 0 to maximum flow of 3346.6 veh/h correspondent to a critical density of 114.9 veh/km. However, in the congested zone regime, there was a decrease in traffic flow and an increase in density. It was found that the curve of flow versus density tended to increase on-road RN1. This study proposed the promotion of public transport and e-commerce as strategies to mitigate the congestion. Also, further research may be carried out on all roads of Kigali city, to provide the level of congestion useful for traffic deviation accordingly.

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Section: Introductionmentioning

confidence: 99%

Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)

Nyirajana

Coker

Akintayo

2021

EAJSTI

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“…Application of the SensorNC in the case of multiple sinks in the sensor field has been demonstrated in [34]. In [35], it was investigated how traffic splitting algorithms perform in terms of worst-case performance in mesh-based WSNs. A MAC protocol has been designed to exactly match the assumptions of SensorNC in order to enable an efficient and effective real-time communication behavior in WSNs [36].…”

Section: Further Applicationsmentioning

confidence: 99%

The Sensor Network Calculus as Key to the Design of Wireless Sensor Networks with Predictable Performance

Schmitt

Bondorf

Poe

2017

JSAN

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Abstract:In this article, we survey the sensor network calculus (SensorNC), a framework continuously developed since 2005 to support the predictable design, control and management of large-scale wireless sensor networks with timing constraints. It is rooted in the deterministic network calculus, which it instantiates for WSNs, as well as it generalizes it in some crucial aspects, as for instance in-network processing. Besides presenting these core concepts of the SensorNC, we also discuss the advanced concept of self-modeling of WSNs and efficient tool support for the SensorNC. Furthermore, several applications of the SensorNC methodology, like sink and node placement, as well as TDMA design, are displayed.

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“…Since the initial proposal in [2], it has been extended in several aspects: Multiple sinks [3], in-network processing [4], improved delay analysis [5], [4], and [6], to name a few. Furthermore, it was applied for diverse purposes, e.g., to model and analyze cluster-tree based IEEE 802.15.4 networks [7], [6], to evaluate traffic splitting in meshed WSNs [8], or to plan the trajectories of multiple mobile sinks in a large-scale, time sensitive WSN [9].…”

Section: Introductionmentioning

confidence: 99%

Boosting sensor network calculus by thoroughly bounding cross-traffic

Bondorf

Schmitt

2015

2015 IEEE Conference on Computer Communications (INFOCOM)

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Sensor Network Calculus (SensorNC) provides a framework for worst-case analysis of wireless sensor networks. The analysis proceeds in two steps: For a given flow, (1) the network is reduced to a tandem of nodes by computing the arrival bounds of cross-traffic; (2) the flow is separated from the crosstraffic by subtracting cross-flows and concatenating nodes on its path. While the second step has seen much treatment, the first step has not at all. This is in sharp contrast to the fact that arrival bounding takes roughly 80% of the total analysis time and is equally crucial for the tightness of the bounds. Therefore, we turn our attention to this first SensorNC analysis step with the goal to boost the performance and applicability of the overall framework. The main technical contribution is a generalized version of the concatenation theorem within the SensorNC setting. This generalization is instrumental in simplifying and streamlining the cross-traffic arrival bound computations such that run times can be reduced by more than a factor of 5. Even more important, it enables a localization of the information necessary to execute the calculations at the node level, thus enabling a distribution of the SensorNC analysis within a self-modeling WSN.

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Performance Analysis of Flow-Based Traffic Splitting Strategy on Cluster-Mesh Sensor Networks

Cited by 6 publications

References 39 publications

Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)

Traffic flow rate on Kigali roads: a case of national roads (RN1 and RN3)

The Sensor Network Calculus as Key to the Design of Wireless Sensor Networks with Predictable Performance

Boosting sensor network calculus by thoroughly bounding cross-traffic

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