Traffic characterization and LTE performance analysis for M2M communications in smart cities

Malandra, Filippo; Chiquette, Laurent-Olivier; Lafontaine-Bédard, L.-P.; Sansò, Brunilde

doi:10.1016/j.pmcj.2018.05.006

Cited by 33 publications

(18 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also assume that the service rates at the Mid-Shadowing and Deep-Shadowing conditions are 0.1 and 0.01 times the ideal one, respectively, since we compensate deep fading in the link budgets (10 dB for mild and 20 dB for deep shadowing) by reducing the rates in the same proportion so that to keep the budget with the same margins approximately. We consider average packet lengths of 100 and 1000 Bytes since these are representative of aggregate IoT application traffic [34], [38].…”

Section: A Lms Queuing Model Validationmentioning

confidence: 99%

On the Queuing Delay of Time-Varying Channels in Low Earth Orbit Satellite Constellations

et al. 2021

View full text Add to dashboard Cite

Low Earth Orbit (LEO) satellite constellations are envisioned as a complementary or integrated part of 5G and future 6G networks for broadband or massive access, given their capabilities of full Earth coverage in inaccessible or very isolated environments. Although the queuing and end-toend delays of such networks have been analyzed for channels with fixed statistics, currently there is a lack in understanding the effects of more realistic time-varying channels for traffic aggregation across such networks. Therefore, in this work we propose a queuing model for LEO constellation-based networks that captures the inherent variability of realistic satellite channels, where ground-to-satellite/satellite-to-ground links may present extremely poor connection periods due to the Land Mobile Satellite (LMS) channel. We verify the validity of our model with an extensive event-driven simulator framework analysis capturing the characteristics of the considered scenario. We later study the queuing and end-to-end delay distributions under such channels with various link, traffic, packet and background conditions, while observing good match between theory and simulation. Our results show that ground-to-satellite/satellite-to-ground links and background traffic have a much stronger impact over the end-to-end delay in mean and particularly variance, even with moderate queues, than unobstructed inter-satellite connections in outer space on an established path between two ground stations and through the constellation. This might hinder the usability of these networks for services with stringent time requirements.INDEX TERMS queuing, delay, LEO, constellation, LMS channel, Internet of Things.

show abstract

Section: A Lms Queuing Model Validationmentioning

confidence: 99%

On the Queuing Delay of Time-Varying Channels in Low Earth Orbit Satellite Constellations

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The system depends on pervasive Wi-Fi and cellular infrastructure, which is capable of providing drivers with real-time parking availability information. In the city of Montreal, (Malandra et al 2018) used LTE embedded into a web-based application to support a huge amount of machine-to-machine (M2M) traffic communication model. The model provides the precise location of different sets of machines such as traffic lights, smart meters, bus stops, etc.…”

Section: City Analyticsmentioning

confidence: 99%

Smart Cities Data: Framework, Applications, and Challenges

Bilal

Usmani

Tayyab

et al. 2020

Handbook of Smart Cities

View full text Add to dashboard Cite

Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.

show abstract

“…We use an LTE simulator 1 similar to the one described in [4]. The way IoT devices gain access to the BSs is based on (from the RACH completion to the reception at EPC).…”

Section: B Network Simulationmentioning

confidence: 99%

A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure

et al. 2020

Self Cite

View full text Add to dashboard Cite

The smooth operation of largely deployed Internet of Things (IoT) applications will depend, among other things, on effective infrastructure failure detection. Access failures in wireless networks Base Stations (BSs) produce a phenomenon called "Sleeping Cells", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell's performance, making it difficult to discover. To detect this kind of failures, we propose a Machine Learning framework, based on the use of Key Performance Indicators (KPIs) statistics from the BS under study, as well as those of the neighboring BSs with propensity to have their performance affected by the failure. A simple way to classify neighbors is to use Voronoi diagrams. In this paper we propose a much more realistic approach based on the nature of the radio-propagation and the way the devices choose the BS to which they send access requests. We gather data from large-scale simulators that use real location data for BSs and IoT devices, and pose the detection problem as a supervised binary classification problem. We measure the effects caused on the detection performance, by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition. Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs) under 5%. The proposed framework holds potential for other pattern recognition tasks in smartcities wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications.

show abstract

Traffic characterization and LTE performance analysis for M2M communications in smart cities

Cited by 33 publications

References 5 publications

On the Queuing Delay of Time-Varying Channels in Low Earth Orbit Satellite Constellations

On the Queuing Delay of Time-Varying Channels in Low Earth Orbit Satellite Constellations

Smart Cities Data: Framework, Applications, and Challenges

A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure

Contact Info

Product

Resources

About