scholarly journals An Intelligent Data Uploading Selection Mechanism for Offloading Uplink Traffic of Cellular Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6287
Author(s):  
Qian Wang ◽  
Juan Fang ◽  
Bei Gong ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Prediction Model ◽  
Cellular Networks ◽  
Selection Mechanism ◽  
Trade Off ◽  
Performance Objective ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Temporal Aspects ◽  
Probability Prediction ◽  
Mobile Crowd

Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or the reduced uploading cost. In this paper, we propose an Intelligent Data Uploading Selection Mechanism (IDUSM) to realize a trade-off between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The mechanism first helps the source participant choose an appropriate data uploading manner based on the proposed probability prediction model, and then optimizes its performance objective for the chosen data uploading manner. In IDUSM, our proposed probability prediction model precisely predicts a participant’s mobility from spatial and temporal aspects, and we decrease data redundancy produced in the Wi-Fi offloading process to reduce waste of participants’ limited resources (e.g., storage, battery). Simulation results show that the offloading efficiency of our proposed IDUSM is (56.54×10−7), and the value is the highest among the other three Wi-Fi offloading mechanisms. Meanwhile, the offloading ratio and uploading cost of IDUSM are respectively 52.1% and (6.79×103). Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost.

Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

2022 ◽  
Vol 22 (2) ◽  
pp. 1-15
Author(s):  
Tu N. Nguyen ◽  
Sherali Zeadally
Keyword(s):  
Mobile Devices ◽  
Cyber Physical System ◽  
Redundant Information ◽  
Mobile Crowd Sensing ◽  
Sensing System ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Mobile Crowd ◽  
Collection Methods

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

scholarly journals SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3280
Author(s):  
Nsikak Pius Owoh ◽  
Manmeet Mahinderjit Singh
Keyword(s):  
Computational Cost ◽  
Security And Privacy ◽  
Location Information ◽  
Communication Overhead ◽  
Spatial Coding ◽  
Mobile Crowd Sensing ◽  
Compression Technique ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Mobile Crowd

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.

scholarly journals A Comprehensive Location-Privacy-Awareness Task Selection Mechanism in Mobile Crowd-Sensing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 77541-77554 ◽  
Author(s):  
Ke Yan ◽  
Guangchun Luo ◽  
Xu Zheng ◽  
Ling Tian ◽  
Akshita Maradapu Vera Venkata Sai
Keyword(s):  
Location Privacy ◽  
Selection Mechanism ◽  
Mobile Crowd Sensing ◽  
Task Selection ◽  
Crowd Sensing ◽  
Mobile Crowd
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