Wireless Sensor Network for Vehicle Speed Monitoring and Traffic Routing System

Megalingam, Rajesh Kannan; Mohan, Vamshi Sunku; Mohanan, Ajay; Leons, Paul; Shooja, Rizwin

doi:10.1109/icmet.2010.5598438

Cited by 14 publications

(3 citation statements)

References 2 publications

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“…In [29] an adaptive segmentation method for traffic flow time-series analysis and in-network aggregation algorithm for data fusion for real-time traffic management is presented. In [30] a WSN system is proposed for vehicle to vehicle and vehicle to central command centre for traffic congestion management and to detect speeding vehicles. These systems either rely on multi-hop routing to transmit information from origin to destination or they make assumptions about the potential range of each node.…”

Section: Introductionmentioning

confidence: 99%

Signal Propagation Analysis for Low Data Rate Wireless Sensor Network Applications in Sport Grounds and on Roads

Ndzi¹,

Arif²,

Shakaff³

et al. 2012

PIER

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Abstract-This paper presents results of a study to characterise wireless point-to-point channel for wireless sensor networks applications in sport hard court arenas, grass fields and on roads. Antenna height and orientation effects on coverage are also studied and results show that for omni-directional patch antenna, node range is reduced by a factor of 2 when the antenna orientation is changed from vertical to horizontal. The maximum range for a wireless node on a hard court sport arena has been determined to be 70 m for 0 dBm transmission but this reduces to 60 m on a road surface and to 50 m on a grass field. For horizontal antenna orientation the range on the road is longer than on the sport court which shows that scattered signal components from the rougher road surface combine to extend the communication range. The channels investigated showed that packet error ratio (PER) is dominated by large-scale, rather than small-scale, channel fading with an abrupt transition from low PER to 100% PER. Results also show that large-scale received signal power can be modeled with a 2nd order log-distance polynomial equation on the sport court and road, but a 1st order model is sufficient for the grass field. Small-scale signal variations have been found to have a Rice distribution for signal to noise ratio levels greater than 10 dB but the Rice K-factor exhibits

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Section: Introductionmentioning

confidence: 99%

Signal Propagation Analysis for Low Data Rate Wireless Sensor Network Applications in Sport Grounds and on Roads

Ndzi¹,

Arif²,

Shakaff³

et al. 2012

PIER

View full text Add to dashboard Cite

show abstract

“…The common approach to tackle the traffic surveillance is an exhaustive monitoring of vehicles [2][3][4] including counting them, the estimation of the speed [5], the weight, the type of vehicle, and even the number of people inside them [6]. For these purposes, most conventional traffic surveillance systems use intrusive sensors that need to be inserted somehow into the road for monitoring.…”

Section: Introductionmentioning

confidence: 99%

Simple Design of Wireless Sensor Networks for Traffic Jams Avoidance

Jiménez

Garcia

2015

Journal of Sensors

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Intelligent transportation systems (ITS) are usually approached by exhaustive measuring and complex signal processing including medium-high cost hardware deployment. In this paper, a novel design of a wireless sensor network system using magnetometers and microphones for the detection and avoidance of traffic jams is described and analyzed. The system, which can also be used for traffic monitoring and surveillance, is simple, energy efficient, and accurate which allows to be implemented with a reduced hardware cost. In order to reduce the maintenance tasks, mini solar panels would also be installed for powering up the motes in the near future.

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“…6 In addition to this, the SIoT creates opportunities that provide numerous challenges toward data processing systems thereby enhancing the data collection process, cleaning of data as well as data storage thereby accomplishing real-time analysis. 7,[8][9][10][11][12][13] Furthermore, in the present scenarios of big data, numerous platforms and standards have been established by the vendors of relational databases and perhaps employed for aggregating the data and analyzing the data. 14 The SIoT and big data provide an exact representation of IoT and social systems for human progression characterization.…”

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confidence: 99%

A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

Shaji

Singh

Nisha

2022

Concurrency and Computation

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Social Internet of Things (SIoT) is considered as one of the most recently evolved topics that connects people and object, object and object as well as people and people. The SIoT and big data provide an exact representation of IoT and social system for human progression characterization. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way. In this article, a deep neural network based marine predator (DRNN-MP) algorithm is proposed in classifying big data. Here, an adaptive Savitzky-Golay filter is employed for selecting the subset and to eliminate undesirable data, as well as different noises. The big data databases are reduced using a Hadoop map reducing framework thereby enhancing the performances of the proposed approach. In addition to this, a modified relief technique is employed to select optimal features thereby performing better classification. The testing and training process based on the proposed approach for optimal classification of features from the big data considers four different databases namely coronary illness, GPS trajectories, localization data, and water treatment plant obtained from UCI machine learning repository. In addition to this, the proposed approach is evaluated for diverse performance measures namely accuracy, precision, specificity, sensitivity, throughput, and energy consumption. Finally, the proposed approach is compared for various metrics to illustrate the effectiveness of the system and the results demonstrated that the accuracy of our work is 98.25%. K E Y W O R D Sbig data, database, deep recurrent neural network, marine predator, modified relief, social internet of things INTRODUCTIONInternet of things (IoT) illustrates a separate universe of heterogeneous objects namely actuators, sensors as well as smartphones in which each object and everything possesses a sovereign identity. 1 Nowadays, developing an IoT enabled technology and providing solutions are considered as tedious tasks. On the other hand, IoT deals with sharing and pervasive collection of data regarding a common intention. In IoT, various attribute factors namely integer value and variable define the data thereby permitting specific functions to be performed using a predetermined interface. 2Numerous research scholars are fascinated peculiarly in determining the risk problems emerging while integrating and discovering the data withinIoT. In recent years, the social Internet of Things (SIoT) has been established which is another utilization of IoT. 3 They constitute various distinctive characteristics including diverse communication protocols, operating systems, related standards, and various operational platforms; however, all such differences are neglected. 4

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Wireless Sensor Network for Vehicle Speed Monitoring and Traffic Routing System

Cited by 14 publications

References 2 publications

Signal Propagation Analysis for Low Data Rate Wireless Sensor Network Applications in Sport Grounds and on Roads

Signal Propagation Analysis for Low Data Rate Wireless Sensor Network Applications in Sport Grounds and on Roads

Simple Design of Wireless Sensor Networks for Traffic Jams Avoidance

A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

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