GeoServ: A Distributed Urban Sensing Platform

Ahnn, Jong Hoon; Lee, Uichin; Moon, Hyun Kyung

doi:10.1109/ccgrid.2011.10

Cited by 15 publications

(12 citation statements)

References 34 publications

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“…We propose GeoPS, a publish-subscribe service where the data updates on a region are published to all users who have subscribed to that region. This section details GeoPS's locality-preserving multicast tree construction and management methods and their performance bounds via mathematical proofs [13].…”

Section: B Location-aware Publish-subscribe Servicementioning

confidence: 99%

“…The following theorem shows that once a query is routed to the target area at the cost of O(log 2 n). On the other hand, concurrent geocasting is considered in our prior work [13]. GeoTable uses Mercury's load balancing mechanism to preserve locality of content retrieval [15].…”

Section: Cloud-based Vehicular Sensing Architecturementioning

confidence: 99%

“…We formulate further problems for how to assign the data rate to each mobile client in Section III-B. The problem formulated in Equation 13 consists of a concave objective over linear constraints, and it becomes convex. Therefore, there are various convex optimization algorithms to solve it from [4].…”

Section: A Program Partitioning Problemmentioning

confidence: 99%

“…VeSense is a two-tier sensor networking platform that exploits the P2P-based Cloud servers similar to GeoServ [13]. Since most sensor data is generated on the roads (and most queries are location sensitive), we assume that the primary search key (or key space) is geographic location.…”

Section: Cloud-based Vehicular Sensing Architecturementioning

confidence: 99%

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VeSense: Energy-Efficient Vehicular Sensing

Ahnn¹,

Potkonjak²

2013

2013 IEEE 77th Vehicular Technology Conference (VTC Spring)

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Abstract-Although vehicular sensing where mobile users in vehicles continuously gather, process, and share location-sensitive and context-sensitive sensor data (e.g., street images, road condition, traffic flow) is emerging, little effort has been investigated in a model-based energy-efficient network paradigm of sensor information sharing in vehicular environments. Upon these optimization framework, a suite of optimization subproblems: a program partitioning and network resource allocation problem, we propose a distributed vehicular sensing platform, called VeSense where mobile users in vehicles publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the vehicular sensing application's quality of service requirements by modeling each subsystem: mobile clients, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 37 times more energyefficient and 73 times faster compared to a standalone mobile application, in various vehicular sensing scenarios applying a realistic mobility model.

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Section: B Location-aware Publish-subscribe Servicementioning

confidence: 99%

Section: Cloud-based Vehicular Sensing Architecturementioning

confidence: 99%

Section: A Program Partitioning Problemmentioning

confidence: 99%

Section: Cloud-based Vehicular Sensing Architecturementioning

confidence: 99%

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VeSense: Energy-Efficient Vehicular Sensing

Ahnn¹,

Potkonjak²

2013

2013 IEEE 77th Vehicular Technology Conference (VTC Spring)

Self Cite

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“…In addition, most of the existing work ignores the marginal effect of sensing data. As a consequence, most existing mobile crowd sensing systems and applications 3,8 blindly make smartphones to collect sensing data, either periodically or randomly.…”

Section: Introductionmentioning

confidence: 99%

A distributed algorithm for maximizing utility of data collection in a crowd sensing system

Chen

Weng

Yang

et al. 2016

International Journal of Distributed Sensor Networks

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Mobile crowd sensing harnesses the data sensing capability of individual smartphones, underpinning a variety of valuable knowledge discovery, environment monitoring, and decision-making applications. It is a central issue for a mobile crowd sensing system to maximize the utility of sensing data collection at a given cost of resource consumption at each smartphone. However, it is particularly challenging. On the one hand, the utility of sensing data from a smartphone is usually dependent on its context which is random and varies over time. On the other hand, because of the marginal effect, the sensing decision of a smartphone is also dependent on decisions of other smartphones. Little work has explored the utility maximization problem of sensing data collection. This article proposes a distributed algorithm for maximizing the utility of sensing data collection when the smartphone cost is constrained. The design of the algorithm is inspired by stochastic network optimization technique and distributed correlated scheduling. It does not require any prior knowledge of smartphone contexts in the future, and hence sensing decisions can be made by individual smartphone. Rigorous theoretical analysis shows that the proposed algorithm can achieve a time average utility that is within O(1/V) of the theoretical optimum.

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Time-Based Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

Yan

Zhao

2019

Green, Pervasive, and Cloud Computing

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GeoServ: A Distributed Urban Sensing Platform

Cited by 15 publications

References 34 publications

VeSense: Energy-Efficient Vehicular Sensing

VeSense: Energy-Efficient Vehicular Sensing

A distributed algorithm for maximizing utility of data collection in a crowd sensing system

Time-Based Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

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