ZTE Communications ›› 2021, Vol. 19 ›› Issue (3): 30-45.DOI: 10.12142/ZTECOM.202103005
收稿日期:
2021-06-08
出版日期:
2021-09-25
发布日期:
2021-10-11
LI Xiuxian1, LI Zhetao1(), OUYANG Yan2, DUAN Haohua3, XIANG Liyao3
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 (Supported by:
. [J]. ZTE Communications, 2021, 19(3): 30-45.
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.
Parameter | Meaning |
---|---|
Collection of sensor nodes | |
Collection of mobile edge users | |
Collection of malicious nodes | |
Number of verification tasks per round | |
Number of rounds of a verification task | |
Difference of trust values between normal and malicious nodes | |
Total cost of system | |
Average trust of normal nodes | |
Average trust of malicious nodes | |
Discrimination rate of trusted nodes | |
Discrimination rate of malicious nodes | |
Number of nodes judged to be trusted | |
Total number of trusted nodes | |
Number of nodes judged to be malicious | |
Total number of malicious nodes | |
Participation flag for user labelled as | |
Result flag for node labelled as | |
Payment for user labelled as j in the | |
Number of nodes that need UAVs to inspect in the | |
Cost of UAV verification of one node | |
Number of sensor nodes | |
Number of malicious nodes | |
Number of mobile edge users | |
Initial trust value | |
Trust value threshold | |
Communication trust value | |
Cooperative recommendation coefficient between nodes labeled as | |
Cooperative recommendation trust value | |
Comprehensive trust value | |
Bid of mobile edge user | |
Expected reward of mobile edge user | |
Number of task nodes in the active range | |
Weight coefficient of winning bids set selection algorithm | |
Weight coefficient of comprehensive trust |
Table 1 Parameters in System Model and Problem Statement
Parameter | Meaning |
---|---|
Collection of sensor nodes | |
Collection of mobile edge users | |
Collection of malicious nodes | |
Number of verification tasks per round | |
Number of rounds of a verification task | |
Difference of trust values between normal and malicious nodes | |
Total cost of system | |
Average trust of normal nodes | |
Average trust of malicious nodes | |
Discrimination rate of trusted nodes | |
Discrimination rate of malicious nodes | |
Number of nodes judged to be trusted | |
Total number of trusted nodes | |
Number of nodes judged to be malicious | |
Total number of malicious nodes | |
Participation flag for user labelled as | |
Result flag for node labelled as | |
Payment for user labelled as j in the | |
Number of nodes that need UAVs to inspect in the | |
Cost of UAV verification of one node | |
Number of sensor nodes | |
Number of malicious nodes | |
Number of mobile edge users | |
Initial trust value | |
Trust value threshold | |
Communication trust value | |
Cooperative recommendation coefficient between nodes labeled as | |
Cooperative recommendation trust value | |
Comprehensive trust value | |
Bid of mobile edge user | |
Expected reward of mobile edge user | |
Number of task nodes in the active range | |
Weight coefficient of winning bids set selection algorithm | |
Weight coefficient of comprehensive trust |
Parameter | Value |
---|---|
Size of area/m2 | |
Number of sensor nodes | |
Number of MEUs | |
Active radius of MEU/m | |
Payment of hiring MEU |
Table 2 Experimental parameters
Parameter | Value |
---|---|
Size of area/m2 | |
Number of sensor nodes | |
Number of MEUs | |
Active radius of MEU/m | |
Payment of hiring MEU |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
No related articles found! |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||