Network-assisted offloading for mobile cloud applications

IEEE Trans. Sustain. Comput.

Kliazovich

et al. 2017

Self Cite

Abstract-Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns.

Section: Smartphone Sensing Potentialmentioning

confidence: 99%

A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures

Capponi

IEEE Trans. Sustain. Comput.

Kliazovich

et al. 2017

Self Cite

“…Reporting is always performed at the beginning of the timeslot t for samples collected during timeslot t − 1. Energy cost related to communication E r s depends on the employed technology, LTE or WiFi, and is defined as follows: When both WiFi and LTE interfaces are active, transmissions take place via WiFi as it is more energy efficient [11], [12] and users do not consume the data plan they pay to the cellular operators [13]. The energy E W spent during the transmission time τ t x is defined as:…”

Section: B Smartphone Sensing Potentialmentioning

confidence: 99%

Energy efficient data collection in opportunistic mobile crowdsensing architectures for smart cities

Capponi

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Kliazovich³

et al. 2017

Self Cite

Abstract-Smart cities employ latest information and communication technologies to enhance services for citizens. Sensing is essential to monitor current status of infrastructures and the environment. In Mobile Crowdsensing (MCS), citizens participate in the sensing process contributing data with their mobile devices such as smartphones, tablets and wearables. To be effective, MCS systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and energy-efficient framework for data collection in opportunistic MCS architectures. Opportunistic sensing systems require minimal intervention from the user side as sensing decisions are application-or device-driven. The proposed framework minimizes the cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. We evaluate performance of the framework with simulations, performed in a real urban environment and with a large number of participants. The simulation results verify cost-effectiveness of the framework and assess efficiency of the data generation process.

“…Offloading stands for the capability of moving the load from one device to another [6] and can refer to both traffic offloading [7] or computation offloading [8]. In the second case, the tasks offloaded are executed remotely in the cloud or in the devices in the vicinity [9].…”

Section: Introductionmentioning

confidence: 99%

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms

Sciarrone

2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

Bisio

et al. 2016

Self Cite

Abstract-Indoor navigation and localization are becoming fundamental services nowadays. WiFi-based solutions such as FingerPrinting (FP) are the most widely adopted techniques for positioning and provide better results if compared to other approaches. They require to compare WiFi Received Signal Strength (RSS) with an pre-computed radio map called fingerprint. The recently proposed Smart Probabilistic FingerPrinting (P-FP) algorithm reduces the computational complexity of the traditional FP approach without any accuracy detriment. On the other hand, fog computing has emerged as a new promising paradigm in the recent years, which extends traditional mobile cloud computing capabilities towards the edge of the network and enables location-aware services. In this paper we propose to offload Smart P-FP computation over a fog platform exploiting a novel distributed algorithm. Performance evaluation validates the effectiveness of the proposed approach with the analysis of: i) the amount of power saved and ii) the efficiency of candidate selection process for offloading. Having 2 or more devices in the vicinity contributing to the computation makes offloading beneficial from a power standpoint. The offloading effectiveness increases with the number of devices willing to contribute and the amount of data to be transferred. Power savings can be as high as 80% if compared with local computation.