How to choose the relevant MAC protocol for wireless smart parking urban networks?

Lin, Trista; Rivano, Hervé; Mouël, Frédéric Le

doi:10.1145/2653481.2653484

Cited by 6 publications

(5 citation statements)

References 21 publications

(13 reference statements)

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“…Although the empirical pdf shows an oscillatory pattern, which smooths out for increasing values of the abscissa, the Weibull nicely captures the general trend. These results are also confirmed by experimental data from the Smart Santander WSN deployment; see [24,26]. …”

Section: System Modelsupporting

confidence: 70%

Data Analytics for Smart Parking Applications

Piovesan

Turi

Toigo

et al. 2016

Sensors

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We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

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Section: System Modelsupporting

confidence: 70%

Data Analytics for Smart Parking Applications

Piovesan

Turi

Toigo

et al. 2016

Sensors

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show abstract

“…• The stability and timeliness of sensor networks -The sensed information on sensor devices shall be collected through wireless networks. When there are lots of sensor devices, the spectrum is often limited and a transmission scheduling is needed [14]. When a vehicle parks on a sensor-equipped parking space, it might block the radio communication and the status change will not be broadcasted on time.…”

Section: A Causes Of Evolutionmentioning

confidence: 99%

“…If users can choose to contribute information on mobile applications, the system will be sensitive to free-riders [50] and selfish liars [54,82,83]. QR codes might be installed on parking spaces to help drivers [35,36] 4,10 13/* Zigbee,CB - [14,38,39] 3 */12-120 802.15.4 -…”

Section: A Information Sensingmentioning

confidence: 99%

“…Lin et al [39] also formulated cities' infrastructures optimal placements as a multiple-objective problem and solved it in real maps. Besides, four different MAC protocols in street parking scenarios are implemented to compare the performance evaluation of schedule, contention-based and hybrid bandwidth allocation methods [14,38].…”

Section: B Sensor Connectivitymentioning

confidence: 99%

“…The results conclude that the rate changes have helped achieve the city's on-street parking occupancy and reduced cruising by 50%. LA ExpressPark [3] cooperates with Streetline 14 as Park Smart pilot program 15 in New York City. Streetline adopts a time synchronized mesh protocol (TSMP) to allocate bandwidth for each device according to an anticipated frequency-time schedule.…”

Section: B Large-scale Deploymentmentioning

confidence: 99%

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