Neighbor discovery in mobile sensing applications: A comprehensive survey

2016 ◽  
Vol 48 ◽  
pp. 38-52 ◽  
Author(s):  
Lin Chen ◽  
Kaigui Bian
Keyword(s):  
Mobile Sensing ◽  
Neighbor Discovery ◽  
Sensing Applications ◽  
Comprehensive Survey

scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 169
Author(s):  
Sherief Hashima ◽  
Basem M. ElHalawany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohamed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Comprehensive Survey ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.

Heterogeneous wireless connectivity for fixed and mobile sensing applications in industrial environments

ETFA2011 ◽  
2011 ◽  
Author(s):  
Jose Antonio Palazon ◽  
Miguel Sepulcre ◽  
Javier Gozalvez ◽  
Jaime Orozco ◽  
Oscar Lopez
Keyword(s):  
Mobile Sensing ◽  
Sensing Applications ◽  
Industrial Environments ◽  
Wireless Connectivity

Efficient 3G/4G Budget Utilization in Mobile Sensing Applications

2017 ◽  
Vol 16 (6) ◽  
pp. 1601-1614 ◽  
Author(s):  
Chao Xu ◽  
Shaohan Hu ◽  
Wei Zheng ◽  
Tarek F. Abdelzaher ◽  
Pan Hui ◽  
...  
Keyword(s):  
Mobile Sensing ◽  
Sensing Applications

scholarly journals Mobile sensing for behavioral research: A component-based approach for rapid deployment of sensing campaigns

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987418 ◽  
Author(s):  
Ivan R Felix ◽  
Luis A Castro ◽  
Luis-Felipe Rodriguez ◽  
Oresti Banos
Keyword(s):  
Mobile Phones ◽  
Mobile Sensing ◽  
Timely Manner ◽  
New Approach ◽  
Reusable Components ◽  
Sensing Applications ◽  
Effectiveness And Efficiency ◽  
Multiple Sensing ◽  
Definition Of ◽  
Rapid Deployment

Collecting experimental data from multiple sensing devices has just recently become quite popular in behavioral and social sciences. Among existing devices, mobile phones stand out as they allow researchers to collect data from individuals in an unbiased, precise, unobtrusive, and timely manner. Current mobile sensing applications are typically developed from scratch, provide no reusable components, and frequently do not take advantage of the devices’ processing capabilities. In light of such limitations, this work presents a novel tool that leverages mobile phones not only to collect data via their sensors but also to process them on the device as soon as they are gathered. The tool provides researchers with easy-to-use services that allow them to configure the required processing routines on the mobile phones. This work proposes a new approach for rapid deployment of sensing campaigns targeted at scientists with basic technical knowledge and requiring low effort. We performed an evaluation aimed at determining whether there is a significant improvement in terms of user effectiveness and efficiency in the definition of new components. The results suggest that the proposed tool speeds up the time and reduces the effort taken for setting up and deploying a sensing campaign.

scholarly journals Emergent Technologies in Big Data Sensing: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Sheng Xiao ◽  
Qingquan Zhang ◽  
Yu Gu ◽  
Ping Yi ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Mobile Sensing ◽  
Emergent Technologies ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Research Architecture

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

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