Sparse mobile crowdsensing: challenges and opportunities

Wang, Leye; Zhang, Daqing; Wang, Yasha; Chen, Chao; Xiao, Han; M’Hamed, Abdallah

doi:10.1109/mcom.2016.7509395

Cited by 244 publications

(147 citation statements)

References 15 publications

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“…With the emergence of ubiquitous sensing and computing diagrams [26], a massive number of mobile data can now be collected either by mobile crowdsensing paradigms [27,28,29] or from operators' infrastructures. These heterogeneous mobile big data are being extensively analyzed in the literature to retrieve interesting and informative information [30,31,32,33].…”

Section: Mobile Data Analyticsmentioning

confidence: 99%

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Chen

Yang

Zhang

et al. 2018

Journal of Network and Computer Applications

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The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods.

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Section: Mobile Data Analyticsmentioning

confidence: 99%

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Chen

Yang

Zhang

et al. 2018

Journal of Network and Computer Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the emergence of ubiquitous sensing and computing diagrams [17], a massive number of mobile data can now be collected either by mobile crowdsensing paradigms [18]- [20] or from operators' infrastructures. These heterogeneous mobile big data are being extensively analyzed in the literature to retrieve interesting and informative information [21]- [24].…”

Section: B Mobile Data Analyticsmentioning

confidence: 99%

Complementary base station clustering for cost-effective and energy-efficient cloud-RAN

Chen

Liu

Fan

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Abstract-The fast growing mobile network data traffic poses great challenges for operators to increase their data processing capacity in base stations in an efficient manner. With the emergence of Cloud Radio Access Network (Cloud-RAN), the data processing units can now be centralized in a data center and shared among several base stations. By clustering base stations with complementary traffic patterns to the same data center, the deployment cost and energy consumption can be reduced. In this paper, we propose a two-phase framework to find optimal base station clustering schemes in a city-wide Cloud-RAN. First, we design a traffic profile for each base station, and propose an entropy-based metric to characterize the complementarity among base stations. Second, we build a graph model to represent the complementarity as link weight, and propose a distanceconstrained clustering algorithm to find optimal base station clustering schemes. We evaluate the performance of our framework using two months of real-world mobile network traffic data in Milan, Italy. Results show that our framework effectively reduces 12.88% of deployment cost and 9.45% of energy consumption compared with traditional architectures, and outperforms the baseline method.

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“…quantity). The need to satisfy these requirements is driving new crowdsensing paradigms, including the concept of sparse mobile crowdsensing [92]. Sparse mobile crowdsensing, which aims to reduce the overall sensing cost without compromise of data quality, functions by allocating sensing tasks for only a small portion of the target area to be covered while inferring the data of the remaining unsensed area based on the spatial and temporal correlation among the data captured from different sub-areas [92].…”

Section: Requirementsmentioning

confidence: 99%

“…Admittedly, area coverage is better addressed with gamification or monetary incentives and social rewards will have very limited effect. To address area coverage in PetaJakarta, a social incentive can be complemented with the concept of sparse mobile crowdsensing [92], previously discussed in "Requirements" section, by inferring missing data from poorly sensed areas based on the spatial and temporal correlation among the data captured from areas that are adequately covered.…”

Section: Applying Spectrum In Real World: the Case Of Petajakartamentioning

confidence: 99%

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Ogie

2016

Hum. Cent. Comput. Inf. Sci.

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Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

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Sparse mobile crowdsensing: challenges and opportunities

Cited by 244 publications

References 15 publications

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Complementary base station clustering for cost-effective and energy-efficient cloud-RAN

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

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