An Optimization Framework for Heterogeneous Access Management

Bühler, Jörg; Wunder, Gerhard

doi:10.1109/wcnc.2009.4917590

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…According to many researchers such as [3] and [5], RATs can be largely characterized into two categories based on means to share their channels: interference constrained RATs and orthogonal RATs. In this paper, we focuse on interference constrained RATs.…”

Section: B Wireless Network Modelmentioning

confidence: 99%

“…For simplicity, interface constrained RATs such UMTS are only considered. Unless specified, network parameters are given form [5]. Figure 4 compares the execution time between the mobile and the cloud by applying to three different vehicular sensing applications: A1-A3, while Figure 5 compares energy consumption in the same settings.…”

Section: B Location-aware Publish-subscribe Servicementioning

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 Wireless Network Modelmentioning

confidence: 99%

Section: B Location-aware Publish-subscribe Servicementioning

confidence: 99%

VeSense: Energy-Efficient Vehicular Sensing

Ahnn¹,

Potkonjak²

2013

2013 IEEE 77th Vehicular Technology Conference (VTC Spring)

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“…According to many researchers such as [12] and [11], RATs can be largely characterized into two categories based on means to share their channels: interference constrained RATs and orthogonal RATs [12].. In this paper, we only consider the latter.…”

Section: Wireless Network Modelmentioning

confidence: 99%

“…For simplicity, orthogonal-based WLAN and TDMA-based GSM are only considered. Unless specified, the network selection follows a strategy given in Equation 6 and network parameters form [11]. We assume that each BS knows its accurate geographic coordinate and thus can dynamically change their associated BS without any errors (e.g., no bouncing at the boundary).…”

Section: Evaluating Cloud Serversmentioning

confidence: 99%

Toward energy-efficient and distributed mobile health monitoring using parallel offloading

Ahnn

Potkonjak

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Although mobile health monitoring where mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. We propose a distributed and energy-saving mobile health platform, called mHealthMon where mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the mobile health monitoring application's quality of service requirements by modeling each subsystem: mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone mobile health monitoring application, in various mobile health monitoring scenarios applying a realistic mobility model.

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2012

Resource Allocation in Uplink OFDMA Wireless Systems

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An Optimization Framework for Heterogeneous Access Management

Cited by 11 publications

References 11 publications

VeSense: Energy-Efficient Vehicular Sensing

VeSense: Energy-Efficient Vehicular Sensing

Toward energy-efficient and distributed mobile health monitoring using parallel offloading

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