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

• Special Topic • Previous Articles Next Articles

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.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

Figures/Tables 15

Figure 1

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

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

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

Figure 9

Figure 13

Figure 14

Figure 15

Figure 16

References 36

1	HILLS G, LAU C, WRIGHT A, et al. Modern microprocessor built from complementary carbon nanotube transistors [J]. Nature, 2019, 572(7771): 595–602. DOI: 10.1109/tcad.2015.2415492 doi: 10.1109/tcad.2015.2415492
2	REN Y Y, WANG T, ZHANG S B, et al. An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network [J]. Personal and ubiquitous computing, 2020: 1–17. DOI: 10.1007/s00779-020-01440-0 doi: 10.1007/s00779-020-01440-0
3	YU M Y, LIU A F, XIONG N N, et al. An intelligent game based offloading scheme for maximizing benefits of IoT‑edge‑cloud ecosystems [J]. IEEE Internet of Things journal, 2020, early access. DOI: 10.1109/JIOT.2020.3039828 doi: 10.1109/JIOT.2020.3039828
4	Gartner. 20.4 billion connected things by 2020 [EB/OL]. (2017‑2‑09) [2021‑05‑01].
5	LI T, LIU W, ZENG Z W, et al. DRLR: a deep reinforcement learning based recruitment scheme for massive data collections in 6G‑based IoT networks [J]. IEEE Internet of Things journal, 2021, early access. DOI: 10.1109/JIOT.2021.3067904 doi: 10.1109/JIOT.2021.3067904
6	HUANG M F, ZHANG K, ZENG Z W, et al. An AUV‑assisted data gathering scheme based on clustering and matrix completion for smart ocean [J]. IEEE Internet of Things journal, 2020, 7(10): 9904–9918. DOI: 10.1109/JIOT.2020.2988035 doi: 10.1109/JIOT.2020.2988035
7	OUYANG Y, LIU A F, XIONG N X, et al. An effective early message ahead join adaptive data aggregation scheme for sustainable IoT [J]. IEEE transactions on network science and engineering, 2021, 8(1): 201–219. DOI: 10.1109/TNSE.2020.3033938 doi: 10.1109/TNSE.2020.3033938
8	LI A, LIU W, ZENG L J, et al. An efficient data aggregation scheme based on differentiated threshold configuring joint optimal relay selection in WSNs [J]. IEEE access, 2021, 9: 19254–19269. DOI: 10.1109/ACCESS.2021.3054630 doi: 10.1109/ACCESS.2021.3054630
9	WANG T, ZHANG G X, BHUIYAN M Z A, et al. A novel trust mechanism based on fog computing in sensor‑cloud system [J]. Future generation computer systems, 2020, 109: 573–582. DOI: 10.1016/j.future.2018.05.049 doi: 10.1016/j.future.2018.05.049
10	LIU S, HUANG G S, GUI J S, et al. Energy‑aware MAC protocol for data differentiated services in sensor‑cloud computing [J]. Journal of cloud computing, 2020, 9(1): 1–33. DOI: 10.1186/s13677-020-00196-5 doi: 10.1186/s13677-020-00196-5
11	LI F F, HUANG G S, YANG Q, et al. Adaptive contention window MAC protocol in a global view for emerging trends networks [J]. IEEE access, 2021, 9: 18402–18423. DOI: 10.1109/ACCESS.2021.3054015 doi: 10.1109/ACCESS.2021.3054015
12	HUANG C Q, HUANG G S, LIU W, et al. A parallel joint optimized relay selection protocol for wake‑up radio enabled WSNs [J]. Physical communication, 2021, 47: 101320. DOI: 10.1016/j.phycom.2021.101320 doi: 10.1016/j.phycom.2021.101320
13	GUO J L, LI F F, WANG T, et al. Parameter analysis and optimization of polling‑based medium access control protocol for multi‑sensor communication [J]. International journal of distributed sensor networks, 2021, 17(4): 155014772110074. DOI: 10.1177/15501477211007412 doi: 10.1177/15501477211007412
14	PALADINO, FISSORE, NEVIANI. A low‑cost monitoring system and operating database for quality control in small food processing industry [J]. Journal of sensor and actuator networks, 2019, 8(4): 52. DOI: 10.3390/jsan8040052 doi: 10.3390/jsan8040052
15	HUANG S B, ZENG Z W, OTA K, et al. An intelligent collaboration trust interconnections system for mobile information control in ubiquitous 5G networks [J]. IEEE transactions on network science and engineering, 2021, 8(1): 347–365. DOI: 10.1109/TNSE.2020.3038454 doi: 10.1109/TNSE.2020.3038454
16	ZHU X Y, LUO Y Y, LIU A F, et al. Multiagent deep reinforcement learning for vehicular computation offloading in IoT [J]. IEEE Internet of Things journal, 2021, 8(12): 9763–9773. DOI: 10.1109/JIOT.2020.3040768 doi: 10.1109/JIOT.2020.3040768
17	TENG H J, DONG M X, LIU Y X, et al. A low‑cost physical location discovery scheme for large‑scale Internet of Things in smart city through joint use of vehicles and UAVs [J]. Future generation computer systems, 2021, 118: 310–326. DOI: 10.1016/j.future.2021.01.032 doi: 10.1016/j.future.2021.01.032
18	DENG Q Y, OUYANG Y, TIAN S J, et al. Early wake‑up ahead node for fast code dissemination in wireless sensor networks [J]. IEEE transactions on vehicular technology, 2021, 70(4): 3877–3890. DOI: 10.1109/TVT.2021.3066216 doi: 10.1109/TVT.2021.3066216
19	BONOLA M, BRACCIALE L, LORETI P, et al. Opportunistic communication in smart city: Experimental insight with small‑scale taxi fleets as data carriers [J]. Ad hoc networks, 2016, 43: 43–55. DOI: 10.1016/j.adhoc.2016.02.002 doi: 10.1016/j.adhoc.2016.02.002
20	HUANG S B, LIU A F, ZHANG S B, et al. BD‑VTE: A novel baseline data based verifiable trust evaluation scheme for smart network systems [J]. IEEE transactions on network science and engineering, 2021, 8(3): 2087–2105. DOI: 10.1109/TNSE.2020.3014455 doi: 10.1109/TNSE.2020.3014455
21	GUO J L, LIU A F, OTA K, et al. ITCN: an intelligent trust collaboration network system in IoT [J]. IEEE transactions on network science and engineering, 2021, early access. DOI: 10.1109/TNSE.2021.3057881 doi: 10.1109/TNSE.2021.3057881
22	LI T, LIU A F, XIONG N N, et al. A trustworthiness‑based vehicular recruitment scheme for information collections in distributed networked systems [J]. Information sciences, 2021, 545: 65–81. DOI:10.1016/j.ins.2020.07.052 doi: 10.1016/j.ins.2020.07.052
23	HU L, LIU A F, XIE M D, et al. UAVs joint vehicles as data mules for fast codes dissemination for edge networking in smart city [J]. Peer‑to‑peer networking and applications, 2019, 12(6): 1550–1574. DOI: 10.1007/s12083-019-00752-0 doi: 10.1007/s12083-019-00752-0
24	OUYANG Y, ZENG Z W, LI X, et al. A verifiable trust evaluation mechanism for ultra‑reliable applications in 5G and beyond networks [J]. Computer standards & interfaces, 2021, 77: 103519. DOI: 10.1016/j.csi.2021.103519 doi: 10.1016/j.csi.2021.103519
25	ZHU X Y, LUO Y Y, LIU A F, et al. A deep learning‑based mobile crowdsensing scheme by predicting vehicle mobility [J]. IEEE transactions on intelligent transportation systems, 2021, 22(7): 4648–4659. DOI: 10.1109/TITS.2020.3023446 doi: 10.1109/TITS.2020.3023446
26	HUANG W, OTA K, DONG M X, et al. Result return aware offloading scheme in vehicular edge networks for IoT [J]. Computer communications, 2020, 164: 201–214. DOI: 10.1016/j.comcom.2020.10.019 doi: 10.1016/j.comcom.2020.10.019
27	SHEN M Q, LIU A F, HUANG G S, et al. ATTDC: an active and traceable trust data collection scheme for industrial security in smart cities [J]. IEEE Internet of Things journal, 2021, 8(8): 6437–6453. DOI: 10.1109/JIOT.2021.3049173 doi: 10.1109/JIOT.2021.3049173
28	WANG T, LUO H, ZHENG X, et al. Crowdsourcing mechanism for trust evaluation in CPCS based on intelligent mobile edge computing [J]. ACM transactions on intelligent systems and technology, 2019, 10(6): 1–19. DOI: 10.1145/3324926 doi: 10.1145/3324926
29	BAEK D, CHEN J, CHOI B J. Small profits and quick returns: An incentive mechanism design for crowdsourcing under continuous platform competition [J]. IEEE Internet of Things journal, 2020, 7(1): 349–362. DOI: 10.1109/JIOT.2019.2953278 doi: 10.1109/JIOT.2019.2953278
30	LIU Y X, DONG M X, OTA K, et al. ActiveTrust: secure and trustable routing in wireless sensor networks [J]. IEEE transactions on information forensics and security, 2016, 11(9): 2013–2027. DOI: 10.1109/TIFS.2016.2570740 doi: 10.1109/TIFS.2016.2570740
31	WAGGONER B and CHEN Y L. Output agreement mechanisms and common knowledge [C]//Second AAAI Conference on Human Computation & Crowdsourcing (HCOMP). Pittsburgh, USA: AAAI, 2014
32	HUANG C, YU H R, BERRY R A, et al. Using truth detection to incentivize workers in mobile crowdsourcing [J]. IEEE transactions on mobile computing, 2020, early access. DOI: 10.1109/TMC.2020.3034590 doi: 10.1109/TMC.2020.3034590
33	KIM T K, SEO H S. A trust model using fuzzy logic in wireless sensor network [J]. World academy of science, engineering and technology, 2018, 42: 63–66
34	FUANG W D, ZHANG C L, SHI Z D, et al. BTRES: beta‑based trust and reputation evaluation system for wireless sensor networks [J]. Journal of network and computer applications, 2016, 59: 88–94. DOI: 10.1016/j.jnca.2015.06.013 doi: 10.1016/j.jnca.2015.06.013
35	YAO Z Y, KIM D Y, DOH Y M. PLUS: parameterized and localized trust management scheme for sensor networks security [C]//IEEE International Conference on Mobile Adhoc and Sensor Systems. Vancouver, Canada, 2006: 437–446. DOI: 10.1109/MOBHOC.2006.278584 doi: 10.1109/MOBHOC.2006.278584
36	BALAKRISHNAN V, VARADHARAJAN V, TUPAKULA U. Subjective logic based trust model for mobile ad hoc networks [C]//4th International Conference on Security and Privacy in Communication Netowrks. Istanbul, Turkey: ACM, 2008: 1–11. DOI: 10.1145/1460877.1460916 doi: 10.1145/1460877.1460916

[1]	LIN Xinhua, ZHANG Jing, LI Qiang. Cluster Head Selection Algorithm for UAV Assisted Clustered IoT Network Utilizing Blockchain [J]. ZTE Communications, 2021, 19(1): 30-38.
[2]	Mohammed SEID, Stephen ANOKYE, SUN Guolin. Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing [J]. ZTE Communications, 2019, 17(4): 33-45.
[3]	Stephen ANOKYE, Mohammed SEID, SUN Guolin. A Survey on Machine Learning Based Proactive Caching [J]. ZTE Communications, 2019, 17(4): 46-55.
[4]	LI Tongxin, SHENG Min, LYU Ruiling, LIU Junyu, LI Jiandong. UAV Assisted Heterogeneous Wireless Networks: Potentials and Challenges [J]. ZTE Communications, 2018, 16(2): 3-8.

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 36

Related Articles 4

Recommended Articles

Metrics

Comments