Using UAV to Detect Truth for Clean Data Collection in Sensor‑Cloud Systems

doi:10.12142/ZTECOM.202103005

ZTE Communications ›› 2021, Vol. 19 ›› Issue (3): 30-45.DOI: 10.12142/ZTECOM.202103005

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Using UAV to Detect Truth for Clean Data Collection in Sensor‑Cloud Systems

LI Xiuxian¹, LI Zhetao¹(), OUYANG Yan², DUAN Haohua³, XIANG Liyao³

^1.Xiangtan University, Xiangtan 411100, China
^2.Central South University, Changsha 410000, China
^3.Shanghai Jiao Tong University, Shanghai 200000, China

Received:2021-06-08 Online:2021-09-25 Published:2021-10-11
About author:LI Xiuxian is currently pursuing his master’s degree at School of Computer Science and School of Cyberspace Science from Xiangtan University, China. His research interests include mobile crowding sensing, IoT devices, and edge computing.|LI Zhetao (liztchina@hotmail.com) is a professor with the College of Computer, Xiangtan University, China. He received his B.Eng. degree in electrical information engineering from Xiangtan University in 2002, the M.Eng. degree in pattern recognition and intelligent system from Beihang University, China in 2005, and the Ph.D. degree in computer application technology from Hunan University, China in 2010. From December 2013 to December 2014, he was a postdoc in wireless network at Stony Brook University, USA. He is a member of IEEE and CCF.|OUYANG Yan is currently a postgraduate student with the School of Computer Science and Engineering, Central South University, China. Her research interests include crowd sensing networks and wireless sensor networks.|DUAN Haohua received his bachelor’s degree in computer science and technology from Jilin University, China in 2020. He is currently pursuing his Ph.D. degree in computer science, Shanghai Jiao Tong University, China. His research interests include security and privacy in machine learning and blockchain.|XIANG Liyao received her B.Eng. degree in electrical and computer engineering from Shanghai Jiao Tong University, China in 2012, and Ph.D. degree in computer engineering from the University of Toronto, Canada in 2018. She is currently an assistant professor with Shanghai Jiao Tong University. Her research interests include security and privacy, privacy analysis in data mining, and mobile computing.
Supported by:
National Natural Science Foundation of China(62032020);Hunan Science and Technology Planning Project(2019RS3019);the National Key Research and Development Program of China(2018YFB1003702)

Abstract

Abstract:

Mobile edge users (MEUs) collect data from sensor devices and report to cloud systems, which can facilitate numerous applications in sensor?cloud systems (SCS). However, because there is no effective way to access the ground truth to verify the quality of sensing devices’ data or MEUs’ reports, malicious sensing devices or MEUs may report false data and cause damage to the platform. It is critical for selecting sensing devices and MEUs to report truthful data. To tackle this challenge, a novel scheme that uses unmanned aerial vehicles (UAV) to detect the truth of sensing devices and MEUs (UAV?DT) is proposed to construct a clean data collection platform for SCS. In the UAV?DT scheme, the UAV delivers check codes to sensor devices and requires them to provide routes to the specified destination node. Then, the UAV flies along the path that enables maximal truth detection and collects the information of the sensing devices forwarding data packets to the cloud during this period. The information collected by the UAV will be checked in two aspects to verify the credibility of the sensor devices. The first is to check whether there is an abnormality in the received and sent data packets of the sensing devices and an evaluation of the degree of trust is given; the second is to compare the data packets submitted by the sensing devices to MEUs with the data packets submitted by the MEUs to the platform to verify the credibility of MEUs. Then, based on the verified trust value, an incentive mechanism is proposed to select credible MEUs for data collection, so as to create a clean data collection sensor?cloud network. The simulation results show that the proposed UAV?DT scheme can identify the trust of sensing devices and MEUs well. As a result, the proportion of clean data collected is greatly improved.

Key words: sensor?cloud system, truth detection, trust reasoning and evolution, mobile edge user, unmanned aerial vehicle

LI Xiuxian, LI Zhetao, OUYANG Yan, DUAN Haohua, XIANG Liyao. Using UAV to Detect Truth for Clean Data Collection in Sensor‑Cloud Systems[J]. ZTE Communications, 2021, 19(3): 30-45.

Figures/Tables 15

Figure 1 Sensor?cloud system model

Table 1 Parameters in System Model and Problem Statement

Parameter	Meaning
$V$	Collection of sensor nodes
$U$	Collection of mobile edge users
$M$	Collection of malicious nodes
$T$	Number of verification tasks per round
$R$	Number of rounds of a verification task
$D$	Difference of trust values between normal and malicious nodes
$P$	Total cost of system
$σ n o r ˉ$	Average trust of normal nodes
$σ m a l ˉ$	Average trust of malicious nodes
$R t$	Discrimination rate of trusted nodes
$R m$	Discrimination rate of malicious nodes
$n u m t ˉ$	Number of nodes judged to be trusted
$n u m t$	Total number of trusted nodes
$n u m m ˉ$	Number of nodes judged to be malicious
$n u m m$	Total number of malicious nodes
$f i, j$	Participation flag for user labelled as $j$ in the $i t h$ round
$B i, j$	Result flag for node labelled as $j$ in the $i t h$ task
$p i, j$	Payment for user labelled as j in the $i t h$ round
$L i$	Number of nodes that need UAVs to inspect in the $i t h$ round
$υ$	Cost of UAV verification of one node
$N$	Number of sensor nodes
$K$	Number of malicious nodes
$L$	Number of mobile edge users
$σ 0$	Initial trust value
$σ m a x$ , $σ m i n$	Trust value threshold
$σ c o m i$	Communication trust value
$σ r e c (i, ? j)$	Cooperative recommendation coefficient between nodes labeled as $i$ and $j$
$σ r e c i$	Cooperative recommendation trust value
$σ i n t i$	Comprehensive trust value
$b i d i$	Bid of mobile edge user
$b i$	Expected reward of mobile edge user
$P o I i$	Number of task nodes in the active range
$α 1, ? α 2$	Weight coefficient of winning bids set selection algorithm
$ω 1, ω 2$	Weight coefficient of comprehensive trust

Table 1 Parameters in System Model and Problem Statement

Parameter	Meaning
$V$	Collection of sensor nodes
$U$	Collection of mobile edge users
$M$	Collection of malicious nodes
$T$	Number of verification tasks per round
$R$	Number of rounds of a verification task
$D$	Difference of trust values between normal and malicious nodes
$P$	Total cost of system
$σ n o r ˉ$	Average trust of normal nodes
$σ m a l ˉ$	Average trust of malicious nodes
$R t$	Discrimination rate of trusted nodes
$R m$	Discrimination rate of malicious nodes
$n u m t ˉ$	Number of nodes judged to be trusted
$n u m t$	Total number of trusted nodes
$n u m m ˉ$	Number of nodes judged to be malicious
$n u m m$	Total number of malicious nodes
$f i, j$	Participation flag for user labelled as $j$ in the $i t h$ round
$B i, j$	Result flag for node labelled as $j$ in the $i t h$ task
$p i, j$	Payment for user labelled as j in the $i t h$ round
$L i$	Number of nodes that need UAVs to inspect in the $i t h$ round
$υ$	Cost of UAV verification of one node
$N$	Number of sensor nodes
$K$	Number of malicious nodes
$L$	Number of mobile edge users
$σ 0$	Initial trust value
$σ m a x$ , $σ m i n$	Trust value threshold
$σ c o m i$	Communication trust value
$σ r e c (i, ? j)$	Cooperative recommendation coefficient between nodes labeled as $i$ and $j$
$σ r e c i$	Cooperative recommendation trust value
$σ i n t i$	Comprehensive trust value
$b i d i$	Bid of mobile edge user
$b i$	Expected reward of mobile edge user
$P o I i$	Number of task nodes in the active range
$α 1, ? α 2$	Weight coefficient of winning bids set selection algorithm
$ω 1, ω 2$	Weight coefficient of comprehensive trust

Figure 2 Using UAV to detect the trust of sensing devices and MEUs (UAV?DT)

Figure 3 Route generation strategy

Figure 4 Successful transmission and suspect paths

Figure 5 Mobile edge user (MEU) collects data package

Figure 6 Trust reasoning mechanism based on communication behavior

Figure 7 Trust reasoning mechanism based on communication behavior

Figure 8 Reverse auction framework

Table 2 Experimental parameters

Parameter	Value
Size of area/m²	$100 ? × 100$
Number of sensor nodes	$1 ? 000$
Number of MEUs	$500$
Active radius of MEU/m	$[5, ? 10]$
Payment of hiring MEU	$[10, ? 25]$

Table 2 Experimental parameters

Parameter	Value
Size of area/m²	$100 ? × 100$
Number of sensor nodes	$1 ? 000$
Number of MEUs	$500$
Active radius of MEU/m	$[5, ? 10]$
Payment of hiring MEU	$[10, ? 25]$

Figure 9 Influence of different parameters on the average trust between normal and malicious nodes

Figure 13 Trust evaluation capability under the optimal threshold

Figure 14 Collection rate

Figure 15 Rate of packet loss

Figure 16 System Costs

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Using UAV to Detect Truth for Clean Data Collection in Sensor‑Cloud Systems

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