scholarly journals A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding

2013 ◽  
Vol 9 (4) ◽  
pp. 331-345
Author(s):  
Jianwei Niu ◽  
Mingzhu Liu ◽  
Han-Chieh Chao
Keyword(s):  
Social Network ◽  
Success Rate ◽  
Mobile Devices ◽  
Betweenness Centrality ◽  
State Of The Art ◽  
Opportunistic Networks ◽  
The State ◽  
Opportunistic Networking ◽  
Wireless Interfaces ◽  
Query Algorithm

With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF), for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC), which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER) with much less resource consumption.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

scholarly journals AI Feynman: A physics-inspired method for symbolic regression

2020 ◽  
Vol 6 (16) ◽  
pp. eaay2631 ◽  
Author(s):  
Silviu-Marian Udrescu ◽  
Max Tegmark
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Success Rate ◽  
Unknown Function ◽  
State Of The Art ◽  
Practical Interest ◽  
Symbolic Regression ◽  
The State ◽  
Np Hard ◽  
Test Set

A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.

scholarly journals From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Sensors ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 184 ◽  
Author(s):  
Ivan Pires ◽  
Nuno Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta
Keyword(s):  
Data Fusion ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
State Of The Art ◽  
The State ◽  
Sensor Data ◽  
Battery Life ◽  
Sensor Data Fusion ◽  
Processing Power

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

scholarly journals Co-authorship prediction in academic social network

2019 ◽  
Author(s):  
William Takahiro Maruyama ◽  
Luciano Antonio Digiampietri
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Network ◽  
State Of The Art ◽  
The State ◽  
Artificial Intelligence Techniques ◽  
Academic Social Network ◽  
Academic Social Networks

The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

Hardware and Software Aspects of VM-Based Mobile-Cloud Offloading

2015 ◽  
pp. 247-271 ◽  
Author(s):  
Yang Song ◽  
Haoliang Wang ◽  
Tolga Soyata
Keyword(s):  
Mobile Devices ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Architectural Support ◽  
Advantages And Disadvantages ◽  
Support Resource ◽  
Cloud Resources

To allow mobile devices to support resource intensive applications beyond their capabilities, mobile-cloud offloading is introduced to extend the resources of mobile devices by leveraging cloud resources. In this chapter, we will survey the state-of-the-art in VM-based mobile-cloud offloading techniques including their software and architectural aspects in detail. For the software aspects, we will provide the current improvements to different layers of various virtualization systems, particularly focusing on mobile-cloud offloading. Approaches at different offloading granularities will be reviewed and their advantages and disadvantages will be discussed. For the architectural support aspects of the virtualization, three platforms including Intel x86, ARM and NVidia GPUs will be reviewed in terms of their special architectural designs to accommodate virtualization and VM-based offloading.

Web browsers on smart mobile devices: A gap analysis on the state of the art

2013 ◽  
Author(s):  
Chinthaka Dharmasiri ◽  
Rythmal Jayendranath ◽  
Amila L. Ariyarathna ◽  
Pahan M. Perera ◽  
Shahani M. Weerawarana
Keyword(s):  
Mobile Devices ◽  
Gap Analysis ◽  
State Of The Art ◽  
The State ◽  
Web Browsers ◽  
Smart Mobile ◽  
Smart Mobile Devices

scholarly journals A Survey of Privacy Vulnerabilities of Mobile Device Sensors

2022 ◽  
Author(s):  
Paula Delgado-Santos ◽  
Giuseppe Stragapede ◽  
Ruben Tolosana ◽  
Richard Guest ◽  
Farzin Deravi ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
State Of The Art ◽  
The State ◽  
Sensitive Data ◽  
Data Utility ◽  
Open Research ◽  
Research Questions ◽  
Behaviour Recognition ◽  
Critical Aspects

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

scholarly journals Exploiting Temporal and Spatial Regularities for Content Dissemination in Opportunistic Social Network

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zhiyuan Li ◽  
Junlei Bi ◽  
Carlos Borrego
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Success Rate ◽  
State Of The Art ◽  
Time Varying ◽  
Hop Count ◽  
Content Dissemination ◽  
System Overhead ◽  
Temporal And Spatial ◽  
Query Latency

Recently, content dissemination has become more and more important for opportunistic social networks. The challenges of opportunistic content dissemination result from random movement of nodes and uncertain positions of a destination, which seriously affect the efficiency of content dissemination. In this paper, we firstly construct time-varying interest communities based on the temporal and spatial regularities of users. Next, we design a content dissemination algorithm on the basis of time-varying interest communities. Our proposed content dissemination algorithm can run in O(nlog⁡n) time. Finally, the comparisons between the proposed content dissemination algorithm and state-of-the-art content dissemination algorithms show that our proposed content dissemination algorithm can (a) keep high query success rate, (b) reduce the average query latency, (c) reduce the hop count of a query, and (d) maintain low system overhead.

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