A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

Shi, Changting

doi:10.32604/cmc.2018.03754

Cited by 21 publications

(8 citation statements)

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“…Security is also one of the main researches for mobile services in IoTs. For example, the IoT device security [16], network attacks [17], content security [18], and privacy protection, etc. The privacy protection is the main focus of this paper.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Anonymous Geometric Routing for Internet of Things

Sun¹,

Wang²,

Li³

et al. 2019

IEEE Access

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Mobile service computing relies on efficient and secure data transfer. Geometric routing, which guarantees scalability, efficiency, and mobility, is a promising routing scheme for mobile services in the resource-constrained Internet of Things (IoT). However, the private data transmitted by geometric routing may be eavesdropped by the malicious node and the privacy-preserving becomes one of the important issues for the mobile service in IoTs. Due to the resource constrains, approaches using encryption or hashing are not suitable for geometric routing. In this paper, we propose a lightweight anonymous geometric routing (LAGER) to protect the node-related private data. Instead of using the encryption or hashing approach, LAGER adopts a coordinate confusion mechanism which provides each node an anonymous coordinate of virtual node, such that the private data is decoupled from the node coordinate. The malicious node cannot determine which node the data belongs to even if it obtains the private data. To support the data transmission with anonymous coordinates, LAGER adopts a hybrid routing scheme by combining the greedy routing and a source routing. The experiment results show that LAGER provides effective anonymity with acceptable costs of scalability and efficiency.

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Section: Related Workmentioning

confidence: 99%

Lightweight Anonymous Geometric Routing for Internet of Things

Sun¹,

Wang²,

Li³

et al. 2019

IEEE Access

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“…The resistance value of the MQ-3 alcohol sensor differs in the values exhibited by gases of different types and concentrations [Shi (2018); Chen, Liu, Ma et al (2018)]. Therefore, when designing its hardware circuit, the voltage value is changed by changing the resistance value, and the alcohol concentration is calculated by reading the voltage value ;Fan, Zhu, Zhao et al (2017); ].…”

Section: Sensor Module Designmentioning

confidence: 99%

Design of Intelligent Drunk Driving Detection System Based on Internet of Things

Zhao¹,

Zhu²,

Qian³

et al. 2019

Journal on Internet of Things

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In recent years, with the rapid development of China's economy and the continuous improvement of people's living standards, the number of motor vehicles and the number of drivers in the country have grown rapidly. Due to the increase in the number of vehicles and the number of motorists, the traffic accident rate is increasing, causing serious economic losses to society. According to the traffic accident statistics of the Ministry of Communications of China in 2009, more than 300,000 car accidents occurred in the year, most of which were caused by drunk driving. Therefore, this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system. The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system, combined with alcohol sensor MQ-3 circuit, LCD1602 liquid crystal display circuit, buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system. The main functions of the system are as follows: the alcohol sensor in the car detects the driver's alcohol concentration value, and displays the value on the LCD screen. The buzzer alarm is exceeded and the information is sent to the traffic police department and the family's mobile phone through the GPRS module. The system can effectively make up for the shortcomings of traffic police detection, which has certain research significance.

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“…The integration and diversification of IoT terminals has brought many security uncertainties to the IoT business, and the problems are mainly reflected in the following points [11][12][13][14][15][16]: *Correspondence: jiangyu@seu.edu.cn 1 School of Cyber Science and Engineering, Southeast University, Sipai Lou, 210096 Nanjing, China Full list of author information is available at the end of the article • Most of the IoT terminals cannot integrate software and hardware for security protection because of the limitation of cost and performance. • Many application scenarios are open and unattended, so this self-running mode is vulnerable to external attacks.…”

Section: Introductionmentioning

confidence: 99%

Physical layer identification of LoRa devices using constellation trace figure

Jiang

Peng

et al. 2019

J Wireless Com Network

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LoRa wireless technology is a revolutionary wireless network access technology with a wide application prospect. An identification method for Lora devices based on physical layer fingerprinting is proposed to provide identities for authentication. Contrary to previous works, a differential constellation trace figure is established from the radio frequency (RF) fingerprinting features of LoRa devices, which transforms the feature matching to the image recognition. A classification method based on Euclidean distance of clustering center of LoRa signal is performed to analyze the differential constellation trace figure. The experimental results show that six LoRa transmission modules can be recognized accurately, and even in a low signal-to-noise ratio (SNR) environment, the different LoRa devices can still be distinguished and identified effectively.

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A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

Cited by 21 publications

References 0 publications

Lightweight Anonymous Geometric Routing for Internet of Things

Lightweight Anonymous Geometric Routing for Internet of Things

Design of Intelligent Drunk Driving Detection System Based on Internet of Things

Physical layer identification of LoRa devices using constellation trace figure

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