Skip to main content

Über dieses Buch

This six volume set LNCS 11063 – 11068 constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Cloud Computing and Security, ICCCS 2018, held in Haikou, China, in June 2018. The 386 full papers of these six volumes were carefully reviewed and selected from 1743 submissions. The papers cover ideas and achievements in the theory and practice of all areas of inventive systems which includes control, artificial intelligence, automation systems, computing systems, electrical and informative systems. The six volumes are arranged according to the subject areas as follows: cloud computing, cloud security, encryption, information hiding, IoT security, multimedia forensics



IOT Security


Accurate Moving Distance Estimation via Multi-modal Fusion from IMU Sensors and WiFi Signal

Moving distance measurement is an indispensable component for the indoor localization and user trace tracking, which is of great importance to a wide range of applications in the era of mobile computing. The maturity of inertial sensors in smartphones and the ubiquity of WiFi technology ensure the accuracy for indoor distance measurement. Despite its importance, moving distance estimation in the indoor environment for mobile devices is still lacking a cost-effective and precise solution. The state-of-the-art work mostly use build-in sensors, e.g. accelerometer, gyroscope, rotation vector sensor and etc. in the mobile devices for the movement distance measurement. Wireless signal is considered to estimate a humans moving distance as well in prior work. However, both methods suffer from complex deployment and inaccurate estimation results. In this paper, we propose a multi-modal approach to measure moving distance for the user. We mainly innovate in proposing a fusion estimation method leveraging sensors and wireless signals to accurately estimate the human’s moving distance indoor. We implement a prototype with smartphones and commercial WiFi devices. Then we evaluate it in distinct indoor environments. Experimental results show that the proposed method can estimate target’s moving distance with an average accuracy of 90.7%, which sheds light on sub-meter level distance measurements in indoor environments.

Jing Xu, Hongyan Qian, Yanchao Zhao

Accurate UHF Tag Authentication Using Near-Field Capabilities

As the Ultra High Frequency (UHF) passive Radio Frequency IDentification (RFID) technology becomes widespread, it faces various challenges of security attacks due to the inherent characteristics of the design of tag hardware and communication protocol. Recently, physical-layer identification methods are proved to be promising to validate the authenticity of passive tags. However, existing methods are usually highly location-dependent and susceptible to environment variations. This paper presents a new authentication solution, namely SecurArray, that involves analyzing the coupling among tags, profiling the communication signal, and conducting the feature matching. SecurArray deploys an array of tags in close proximity as the identity of an object and utilizes the near-field capability of UHF tags for authentication. We implement our prototype using commercial off-the-shelf UHF RFID reader and tags. Extensive experiment results demonstrate that SecurArray effectively achieves high accuracy of physical-layer authentication.

Cui Zhao, Han Ding, Kaiyan Cui, Fan Liang

ADFL: An Improved Algorithm for American Fuzzy Lop in Fuzz Testing

Fuzz testing is an effective software testing technology being used to find correctness problems and security issues in software. AFL (American Fuzzy Lop) is one of the most advanced fuzzy testing tools. However, it is difficult for AFL to explore deeper parts of the program. This paper proposes an improved method called ADFL for low hit branch of the tested program to solve this problem. The method first optimizes the selection strategy for seed files, and secondly generates test cases with hits and low hits at higher frequencies during the mutation phase. The experimental results show that compared with the latest version of AFL, the coverage of ADFL is significantly increased in 24 h than AFL. ADFL can cover more branches than AFL in each benchmark program and improve branch coverage of program refactoring by 19.7% and 74.5%. Moreover, ADFL can indeed find more bugs, especially for deep nested test programs.

Chenxin Wang, Shunyao Kang

An Improved Distributed PCA-Based Outlier Detection in Wireless Sensor Network

Outlier detection in wireless sensor networks (WSNs) is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. Principal Components Analysis (PCA) has attracted a great interest in the machine learning field especially in outlier detection in WSNs. An efficient and effective method called Improved Distributed PCA-Based Outlier Detection (IDPCA) has been proposed in this paper. The proposed scheme operates on each sensor node respectively, thus reducing the communication cost and prolonging the lifetime of the network. Through taking advantage of the data spatial correlation of adjacent nodes, the proposal can significantly reduce the false alarm rate and distinguish events and errors in real time. Experiments with both synthetic and the real data collected from the Intel Berkeley Research Laboratory indicate that IDPCA achieves a higher detection rate with a lower false alarm rate, while reducing the communication overhead than previous methods.

Wentian Zheng, Lijun Yang, Meng Wu

An Improved IoT Notion-Based Authentication and Key Agreement Protocol for Heterogenous Ad Hoc Wireless Sensor Network

As an important part of Internet of Things (IoT), wireless sensor networks (WSN) attracts a lot of researchers on its security issues. In this paper, we have studied the latest scheme (Tai et al.’s scheme), which is an IoT notion-based authentication and key agreement scheme ensuring user anonymity for heterogeneous ad hoc wireless sensor networks (HWSN). After further analysis, we found that their scheme are vulnerable to some security flaws. It is unable to resist stolen smart card attacks, and can’t provide anonymity for users and sensors. Therefore, our work is to eliminate those threats and to improve the security of the scheme.

Yuxia Zhang, Xin Zhang, Fengtong Wen

Appliance Recognition Based on Continuous Quadratic Programming

The detailed information of residents’ electricity consumption is of great significance to the planning of the use of electrical appliances and the reduction of electrical energy consumption. On the basis of analyzing the characteristics of residents’ load, through the event detection of changes in the status of electrical appliances, using binary planning to solve the idea of global optimal solution, using the constraints of 0–1, proposed a continuous binary planning model. Based on the proposed load identification algorithm, personal power consumption data can be subdivided into load levels. The test results show that the recognition accuracy can be obtained by selecting the appropriate load identification index. The algorithm can be applied to non-intrusive load monitoring systems in residential buildings.

Xiaodong Liu, Qi Liu

Application of BlockChain in Internet of Things

BlockChain (BC) technology is the digital currency—the underlying technology of Bitcoin, which has attracted more and more attention in recent years. BC is a distributed database system with decentralized and unfalsified features that make it be expected to lead a new revolution in the technology industry. The prominent feature of BC makes it break away from currency applications and gradually enter into non-monetary applications. The distributed and anti-attack characteristics of BC technology can be well integrated into Internet of things (IoT). The existing technical features of BC enable it to realize distributed privacy and security in IoT. This paper introduces the main problems in the development of IoT and the characteristics of BC in the application of IoT. We also discusses the main direction of BC in the application of IoT. But BC can’t be applied directly to IoT, and the fusion of two technologies will face many challenges. Based on these, this paper analyzes the challenges of BC in the application of IoT.

Yanhan Yang, Yaming Yang, Jinlian Chen, Mingzhe Liu

Assessment of Haze Effects in Human Lives: A Case Study of Investigation in Nanjing

The continuous development of industry and the advancement of urbanization have brought economic progress as well as a series of environmental problems. In recent years, haze has become one of the most important environmental issues affecting the production, lives, and health of Chinese residents. The Chinese government attaches great importance to haze pollution control and prevention. This article intends to explore the urban residents’ perception of haze and find out the influence of haze on various aspects of lives. Firstly, thirteen indicators are carefully conducted to assess the effects of haze. Secondly, the normal distribution-based weighting method and analytic hierarchy process (AHP) are introduced to assess the haze effects in Nanjing. Finally, we discuss the results of the study and give some policy recommendations.

Chen Jin, Zeshui Xu, Guizhi Wang

Association Analysis of Firmware Based on NoSQL Database

With the continuous expanding of the Internet of Things, the security of networked embedded devices attracts much attention. Large scale embedded device firmware provides basic data for automated and artificial intelligent analysis method. Thus, an association analysis method for the large scale firmware security is proposed in this paper. Then, a firmware database platform based on the proposed analysis method is developed. First, the platform can complete the mainline of embedded device firmware crawl with its web crawler program. Then, a firmware NoSQL database including the firmware and its information (such as its vendor, product, version, URL, files, etc.) is formed. Last, the firmware analysis method is applied on the database by matching the hashes of the web files and programs in the firmware file system with vulnerability file. The experimental result shows that the proposed method is effective and efficient.

Gongbo Wang, Weiyu Dong, Rui Chang

Authentication of Quantum Dialogue Under Noise

In this paper, we verify conversation between two parties to ensure any information in the matter discussed during the dialogue is not disclose to other parties. We tackle this problem through decoy and steganography to detect the parties’ identities if any of them is incorrect, and to ensure that the conversation channel is secured. We propose using DFS immune-combined noise characteristics, a generalized entangled states that converts previously shared IDs into logical quantum states for noise immunity, randomly mixed message sequence and transmit. We perform identity authentication and eavesdropping detections to improve the security and efficiency of agreement between the parties.

Dong-Fen Li, Rui-Jin Wang, Daniel Adu-Gyamfi, Jin-lian Chen, Ya-Ming Yang, Ming-Zhe Liu

Automatic Integrated Exhaust Fan Based on AT89S51 Single Chip Microcomputer

This paper introduces the basic design principle of the fully automatic integrated exhaust fan, the composition of the system hardware and the control algorithm. An exhaust fan which can detect gas automatically and work automatically is designed. It is assembled with smoke sensors, carbon monoxide sensors and exhaust fans to achieve automatic detection, automatic operation and elimination of harmful gases, and ensure people’s health is not infringed.

Shengqian Ma, Fanchen Meng, Yanruixuan Ma, Jisong Su

Bring Intelligence to Ports Based on Internet of Things

In this work, we investigate the problem of how to improve the Intelligent Ports based on Internet of Things technology. We firstly summarized the development and status of Intelligent Ports. Then, focusing on some important parts of port, we discussed in detail of how to utilize IoT advanced technology to build next generation Intelligent Ports. The IoT key technology, detailed military technology application of functional modules and overall solution are proposed. All parts of the port terminal operations, warehousing, logistics, yard and port transportation are closely connected through the wireless network or special network, providing all kinds of information for daily production supervision, related government departments and port shipping enterprises.

Suying Li, Zhenzhou Ma, Peitao Han, Siyang Zhao, Peiying Guo, Hepeng Dai

Centroid Location Technology Based on Fuzzy Clustering and Data Consistency

RSSI technology has no additional hardware support, low energy consumption and low cost, but it has poor adaptability in different environments which would result in large errors when mapping RSSI signal to the measurement distance between nodes directly. In order to improve localization accuracy of Wireless Sensor Network, we propose a Centroid Localization based on Fuzzy Clustering and Data Consistency. Firstly, the measurement distance is preprocessed, and the anchor node with the largest received signal strength is found as the reference node to eliminate the measurement error within communication range of unknown nodes. Secondly, Fuzzy Clustering and Data Consistency are used to remove the coarse error. Finally, the improved Weighted Centroid algorithm is used to locate unknown nodes. The simulation results show that the FCDC-CL algorithms average localization error is approximately 9.4 $$\%$$ and the error is significantly reduced compared with the traditional WCL algorithm.

Shanliang Xue, Mengying Li, Peiru Yang

Classification of Car Scratch Types Based on Optimized BP Neural Network

At present, the detection of scratch types on the surface of automobiles still adopts manual inspection, which has the disadvantages of high leakage detection rate and low efficiency. In order to realize automatic detection, this paper puts forward a kind of car scratch classification method based on optimized BP neural network (H-IGA-BP). The feature vector which extracts obvious scratch characteristic from the texture is served as the input of BP neural network. Aiming at the difficulty in determining the number of hidden layer nodes in BP neural network, the golden section algorithm is used to find the ideal value. The traditional BP neural network has long training time and easily falls into local extremum. By improving the adaptive genetic algorithm (IGA), the selection operator, crossover operator and mutation operator are modified to optimize the weights and thresholds of BP neural network. The experimental results show that this method can effectively improve the accuracy and robustness of scratches classification. It provides a new method for the automatic detection of car scratch types.

Xing Zhang, Liang Zhou

Comparative Study of CNN and RNN for Deep Learning Based Intrusion Detection System

Intrusion detection system plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. Due to huge increase in network traffic and different types of attacks, accurately classifying the malicious and legitimate network traffic is time consuming and computational intensive. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning based intrusion detection systems, aiming to give basic guidance for DNN selection.

Jianjing Cui, Jun Long, Erxue Min, Qiang Liu, Qian Li

Composite Structure Health Monitoring Review Based on FBG Sensor

Structural health monitoring of composite materials has always been a hot topic for researchers at home and abroad. Compared with traditional sensors for monitoring damage, fiber Bragg grating (FBG) sensors have the advantages of being free from electromagnetic interference, light weight, small size, and non-corrosion resistance. Therefore, FBG sensors are widely used in the structural health monitoring of composite materials. This paper briefly describes the structure and principle of FBG sensor, analyzes the application status of FBG sensor in the field of composite structure health monitoring at home and abroad, explains the good performance of FBG sensors in the health monitoring of composite structures and introduces the related neighborhood research benefits; Finally, it is based on FBG Sensor composite health monitoring technology is summarized and forecasted.

Yajie Sun, Yanqing Yuan, Lihua Wang

Correlation Analysis of Alarm Data Based on Fuzzy Rule in Power Network

With the development of science and technology, power network has greatly developed, and people has gradually depended on the power in daily life. However, once the fault in power network, the transmission of power will be difficult and the engineer must check the fault in large amounts of alarm data in power network immediately, which would cost so much time by human experience. To solve this problem, we do correlation analysis of alarm data based on fuzzy rule by human intelligence and then locate the root alarm data for the engineer so that they could repair the fault immediately. We do data preprocessing and feature selection firstly. Then this work introduces the Fuzzy C-means (FCM) method to do clustering, which is based on the fuzzy rule. Finally, we use Aprior algorithm to do correlation analysis in order to locate fault in power network. Experimental results show that correlation analysis based on fuzzy rule has better performance comparing to the competing algorithms.

Wenting Jiang, Yan Chen, Yingqian Liao

Coverage Holes Recovery Algorithm of Underwater Wireless Sensor Networks

Underwater wireless sensor network nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. Underwater wireless sensor network if no wireless sensor node is available in the area due to used up energy or any other reasons, the area which is not detected by any wireless sensor node forms coverage holes. The coverage holes recovery algorithm aiming at the coverage holes in wireless sensor network is designed in this article. The nodes movement is divided into several processes, in each movement process according to the balance distance and location relations move nodes to separate the aggregate nodes and achieve the maximum coverage of the monitoring area. Because of gradually increasing the balance distance between nodes, in each movement process the nodes movement distance is small and reduce the sum of the nodes movement distance. The simulation results show that this recovery algorithm achieves the goal of the nodes reasonable distribution with improving the network coverage and reducing the nodes movement distance thus extends the lifetime of the underwater wireless sensor network in the initial deployment phase and coverage holes recovery phase.

Min Cui, Fengtong Mei, Qiangyi Li, Qiangnan Li

Decision Stump and StackingC-Based Hybrid Algorithm for Healthcare Data Classification

The healthcare data analytics is a demanding task at present due to enormous amount and diversity of information. Several algorithms were developed to analyze healthcare data with the objective to develop a non-invasive, unbiased and robust prediction system. The present study proposed a hybrid algorithm for healthcare data mining by using the Decision stump (DS), StackingC (SC), and voting methods. Five benchmarked healthcare datasets related to cancer, diabetes, hyperthyroid, lymphography have been selected for the analysis and validation. The hybrid algorithm DS-SC results in 0.31%-30.05% improvement in classification accuracy with DS and SC methods, individually.

Sunil Kr. Jha, Parimala Paramasivam, Zhaoqing Pan, Jinwei Wang

Design and Development of Wheat Production Information Management System Based on Internet of Things

Informationization is a propellant for promoting the development of traditional industries. The comprehensive application of informatization technology also plays an important role in improving the macroeconomic decision-making level of agriculture and improving scientific management capabilities. The growth status of wheat in each growth period is closely related to the yield, and the wheat growth process is complicated and changeable. It is very important to make full use of information technology to monitor, manage and analyze wheat field environment and wheat growth information, which is of great significance for improving the scientific management level of wheat production and increasing grain yield. On the basis of fully analyzing the demand of Bohai granary technology demonstration project and the key influencing factors of wheat production, the wheat production information management system was developed by using Internet of things technology, data analysis technology and data visualization technology. It realized the collection and storage of wheat field environmental information, field management information and wheat growth information, and provided information service and technical support for scientific planting and management of Wheat by analyzing and processing related data.

Ziqing Zhang, Pingzeng Liu

Design of Indoor LED Intelligent Dimming and Color Modulation System Based on Zigbee

With the development of science and technology and the popularity of the Internet of things technology, intelligentization of household products have become the trend of development. Intelligent lighting control system can improve the quality of lighting, meet people’s diverse user experience. Using CC2530, LD3320 and LED arrays, an indoor lighting control system is designed based on Zigbee, adopts non-specific human voice recognition technology, and uses RGB theory and PWM technology to realize the adjustment of brightness and color of LED lamps through speech recognition.

Chun-ling Jiang, Song Xue

Detecting PLC Program Malicious Behaviors Based on State Verification

At present, the security of Programmable Logic Controller (PLC) codes mainly depends on the detection of code defects. However, there is no detection of malicious behaviors that violates the safety requirements. In this paper, we propose an approach to detect the malicious behaviors of PLC programs based on state verification. In particular, avoid state space explosion by merging the same output state of the same scan cycle and removing the output states that have been analyzed in previous scan cycles. For timer, we deduce all output states of a timer based on the analysis of part output state transition relationships. Moreover, the sequence of input vector that violates the safety requirement could be obtained when malicious behaviors are found. Based on experimental results, our method takes less than 5 min for the worst case, it can be proved that our method can detect PLC malicious behaviors effectively and accurately.

Tianyou Chang, Qiang Wei, Wenwen Liu, Yangyang Geng

Detection Method of Hardware Trojan Based on Wavelet Noise Reduction and Neural Network

As there are multiple noise exist in data acquisition of chip power consumption, in order to ensure the reliability of the data, a circuit with Trojan logic is written in FPGA and the power consumption data is extracted based on AES circuit. Aiming at the influence of noise on hardware Trojan detection, a power reduction algorithm based on wavelet transform is proposed, and the optimal parameters are chosen to reduce the noise effects. To solve the problem that the feature recognition model has a great influence on the accuracy of the detection in the process of chip normal detection and hardware Trojan recognition, a hardware Trojan recognition algorithm based on neural network is proposed, which can distinguish the data from each other and detect the Trojan after data de-noising. According to the experiment, it shows that the identification rate of hardware Trojan in chip is larger than 90%, and the consumption data which size greater than 0.05% can be identified.

Xiaopeng Li, Fei Xiao, Ling Li, Jiangjiang Shen, Fengchen Qian

Detection of Android Applications with Malicious Behavior Based on Sparse Bayesian Learning Algorithm

Android mobile devices are widely used in recent years. Due to the openness of Android, applications with malicious behavior have more opportunities to get confidential information, which can cause property damage. Most of current solutions are hard to detect these rapidly developing malicious applications with high accuracy. In this paper, a static malicious application detection method based on Sparse Bayesian Learning Algorithm and n-gram analysis is proposed to solve this problem.

Ning Liu, Min Yang, Hang Zhang, Chen Yang, Yang Zhao, Jianchao Gan, Shibin Zhang

Distribution of CA-Role in Block-Chain Systems

Under some applications, identity-authentication must be involved into block-chain systems. However, the introduction of traditional PKI mechanism in block-chain systems is not proper for 3 reasons: (1) a centralized certification authority (CA) represents a single point of failure in the network; (2) the numbers and locations of nodes vary in time; (3) it introduces additional centralized factors in the block-chain system that is already decentralized. For the sake of decentralization multi-CA scenario is considered to distribute CA-functionality to nodes. Further, armed with secret sharing scheme, a practical distributed CA-based PKI scheme is proposed that is well-associated with existed mechanisms (such as POW) in the original system. Finally, solutions of verification and multi-level assigning issues are constructed via verifiable secret sharing and multilevel secret sharing tools.

Yue Fu, Rong Du, Dagang Li

DoS Attacks Intrusion Detection Algorithm Based on Support Vector Machine

An intrusion detection method which is suitable for the characteristics of WSN (wireless sensor networks) is proposed intrusion detection based on single-class support vector machine. SVM (Support vector machines) can directly train and model the collected data sets, automatically generate detection models, and improve the efficiency of intrusion detection systems. A three-layer intrusion detection model is defined based on this algorithm. The model is more effectively for classifying the data collected by cluster member nodes into intrusion data and normal data. Finally, On the QualNet simulation platform, we implement SVM for the detection of DoS (denial of service) attacks intrusion detection algorithm. The result show that it is feasible to apply SVM to the design of intrusion detection system, with higher system detection rate and lower false alarm rate.

Lingren Wang, Jingbing Li, Jieren Cheng, Uzair Aslam Bhatti, Qianning Dai

Dynamic-Enabled Defense Strategy Base on Improved CVSS for the Home Internet

Nowadays, home Internet has attracted a lot of interest. Dynamic-enabled Defense Strategy can reduce the possibility of being hacked to become the mining systems for blockchain. In order to reflect the status of the smart home system more accurately, this paper proposed a method to improve the common vulnerability scoring system (CVSS) and make CVSS more suitable for the smart home system. The proposed method can obtain vulnerabilities scores, reflecting the security status of the system accurately and providing strong support for system security defense and reinforcement. For dynamically-enabled defense with constantly changing state, it is hard for the attacker to obtain all the information in a short time, since it requires too many resources when the changing frequency is too high. Whereas the attacker has enough time to analyze the system with low changing frequency. Combining with the improved vulnerability scoring system, we propose a dynamic-enable defense strategy based on Markov chain and stochastic Petri net to calculate the attack detection probability and the vulnerability value of the system, and obtain the best dynamic switching interval to ensure the security of the system.

Chunru Zhou, Min Lei, Kunchang Li, Li Xu, Wei Bi

Energy Efficient Smart Irrigation System Based on 6LoWPAN

Smart irrigation system requires long-distance transmission, low power consumption and accurate data analysis for precise irrigation and water conservation. In this paper, we designed the long-distance transmission and low power consumption smart irrigation node with SoC CC1310, and the system applies 6LoWPAN in smart irrigation system to implement low power networking and transmission. This paper also proposes an improved fuzzy algorithm for smart irrigation, which determines irrigation strategies. The software design of smart irrigation node is based on Contiki operating system, and we compared the shortest path networking method with the default networking method ETX in Contiki, when the amount of nodes increase to 50, the shortest path networking method could save 10% energy compare with the ETX method.

Xiawei Jiang, Weidong Yi, Yongrui Chen, Hao He

Enhancing Named-Based Caching in NDN

Named Data Networking (NDN) is regarded as a new networking paradigm for future Internet. Designing efficient content caching strategy is critical as query efficiency depends on the distribution structure of named data. Existing solutions, either adopt global path caching (incurs huge of memory consumption) or random content caching (results in imbalance storage), are infeasible in dealing with large-scale namespace. In this paper, we propose a novel caching strategy that jointly considers the content popularity and local potential-field of request/radiation ability of nodes. Specifically, name prefixes are selectively cached on en-route nodes according to their popularity and the target caching nodes are selected from the network with high connection degree so that the requests can be centered on them. Experiment results show that our scheme outperforms the current approaches in term of server hit rate, response delay, and overall memory consumption.

Zhiqiang Ruan, Haibo Luo, Wenzhong Lin

Farmland Intelligent Information Collection System Based on NB-IoT

Aiming at the problems of short-distance communication, large-power consumption and low-network coverage, the farmland intelligent information acquisition system is designed and developed. This system uses the latest technology—NB-IoT, which has high speed, narrow band and wide internet of things technology. Using 5G technology, the system has low cost, low power consumption, excellent architecture. The system uses MSP430F149 ultra-low power chip as the core processor, collecting environmental information in agricultural field, using WH-NB73 NB-IoT module and relying on NB-IoT base station to achieve data interaction between server and terminal devices. The server can receive, check, store and analyse data. The system is stable and reliable, providing good data support for the growth of field crops and providing strong technical support for the research of crops.

Jianyong Zhang, Pingzeng Liu, Wang Xue, Zhao Rui

FRDV: A DTN Routing Based on Human Moving Status in Urban Environments

It is a fast and simple way to run a Delay Tolerant Network (DTN) by mobile terminals in an urban environment, therefore DTN currently plays an important role as a network for Internet of Things (IoT). The network metrics are important for performance of DTN based communication systems. Because moving characteristics in urban environments are different from other challenging network environments, then the routing method is also different in various environments. In general, routing algorithm decides the DTN performance, so it cannot release potential performance with traditional routing algorithms in cities. In this paper, we propose a routing algorithm for urban areas, named Forward Routing based Distance Variation (FRDV), and we designed such approach according to human moving characteristics. FRDV comprises two stages which include selecting relay node and messages transmission decision. At the first stage, FRDV select a relay node depend on sending activity which depends on delivery frequency of nodes. During the short encounter time, the nodes selectively sent messages to the relay node based on moving status of nodes at the second stage. The simulation results suggest that FRDV outperforms than classical algorithms such as Epidemic, Prophet, Direct Delivery and First Contact algorithms in urban environments.

Wenzao Li, Bing Wan, Zhan Wen, Jianwei Liu, Yue Cao, Tao Wu, Jiliu Zhou

High Speed Pharmaceutical Packaging Detection System Based on Genetic Algorithm and Memory Optimization

Aiming at the problem of misprint or obscure of packaging date, production batches and the validity period on the medicine package, a high speed medicine packaging detection technology based on real time image identification is proposed. The memory optimization algorithm is introduced to allocate free memory in the system to the storage and process the image data, and thus the frequent and complex steps of saving and reading of image in the process of image identification are avoided. Therefore, the detection efficiency of the system is improved. The experimental results show that the detection speed of the proposed system increase by 12.5% in the field of character recognition and digital recognition.

Bin Ma, Qi Li

Identifying Influential Spreaders by Temporal Efficiency Centrality in Temporal Network

Identifying influential spreaders is an important issue for capturing the dynamics of information diffusion in temporal networks. Most of the identification of influential spreaders in previous researches were focused on analysing static networks, rarely highlighted on dynamics. However, those measures which are proposed for static topologies only, unable to faithfully capture the effect of temporal variations on the importance of nodes. In this paper, a shortest temporal path algorithm is proposed for calculating the minimum time that information interaction between nodes. This algorithm can effectively find out the shortest temporal path when considering the network integrity. On the basis of this, the temporal efficiency centrality (TEC) algorithm in temporal networks is proposed, which identify influential nodes by removing each node and taking the variation of the whole network into consideration at the same time. To evaluate the effectiveness of this algorithm, we conduct the experiment on four real-world temporal networks for Susceptible-Infected-Recovered (SIR) model. By employing the imprecision and the Kendall’s au coefficient, The results show that this algorithm can effectively evaluate the importance of nodes in temporal networks.

Kai Xue, Junyi Wang

Identifying Rumor Source of Online Social Networks in the SEIR Model

Rumor that propagates through online social networks can carry a lot of negative effects and even disturb the social order. This paper addresses the problem of detecting the rumor source in an online social network based on an observed snapshot. We assume the spreading of a rumor in the social networks follows the susceptible-exposed-infected-recovered (SEIR) model. All nodes are assumed initially in susceptible states, but only one single rumor source is in infected state. The susceptible node receives messages from its infected neighbor social nodes and it can be treated as exposed at each time-slot. Once an exposed node believes these received messages and forwarded them, it would turn into the infected state; otherwise, it would drop these messages and then it is considered as in the recovered state. It is assumed that the recovered nodes will never believe these information again. Given an observed snapshot of online social network, in which the susceptible nodes, exposed nodes and recovered nodes cannot be distinguished, the estimator is evaluated to identify the source associated with the most likely infection process based on induction hypotheses. The effectiveness of the proposed method is validated using experiments based on a tree networks and two real-world networks, and the results demonstrate that our estimator performs better than the existing closeness centrality heuristic.

Yousheng Zhou, Chujun Wu

Intelligent Control System of Cucumber Production in the Greenhouse Based on Internet of Things

In order to improve the level of automation and scientific management of greenhouse planting and reduce the output of labor force, greenhouse cucumber production as an example is studied, a kind of intelligent control system of cucumber production in the Internet of things is designed. On the basis of many factors such as weather, time, humidity, temperature and so on, in combination with the specific needs of daily production of cucumber, the greenhouse shutter control model, recovering stage model, early-flowering stage and result stage model is designed by using wireless communication technology, information collection technology, information processing technology and other intelligent technologies. It has realized the precision automatic control of the greenhouse. At the same time, in order to facilitate the user to see the growth of cucumber and the information of the greenhouse environment in real time, the mobile phone APP remote control, monitoring and warning services are provided. Experiments show that the system is stable in data transmission and reliable in environmental regulation, which meets the needs of intelligent control in a modern greenhouse environment, and significantly improves the efficiency of cucumber production.

Liyang, Pingzeng Liu, Bangguo Li, Xueru Yu

Intelligent Poultry Environment Control System Based on Internet of Things

According to the status quo of poultry breeding, an intelligent poultry house environmental control system was developed. The system design is based on the three-tier architecture of the Internet of Things: sensing layer, transmission layer and application layer. The network model adopts total star structure and is monitored by a server. The center dispatches and manages multiple subordinate monitoring substations. This article introduces the system overall design idea, expounds the function and principle of the system, completes the design and implementation of the server monitoring center and the lower computer monitoring substation. The experimental results show that the system can automatically collect and analyze environmental parameters such as air temperature and humidity, light intensity, wind speed, and air quality in the poultry house, so that the environment of the poultry house can be reasonably controlled and the environment parameters of the poultry house can be in a state of dynamic balance.

YuQun, Zhang Yan, Wang Xiu-li, Li Bao-quan

Internet of Things Security Analysis of Smart Campus

With the development of the Internet of Things (IOT) technology, it makes a great progress in the construction of smart campus. But security problem is exposed in the development of IOT, which becomes one of the factors restricting the development of smart campus. In this paper, the IOT security of smart campus construction is analyzed. Meanwhile, corresponding solving measures is put forward.

Lei Wang, Kunqin Li, Xianxiang Chen

Linked-Behaviors Profiling in IoT Networks Using Network Connection Graphs (NCGs)

The internet of things (IoT) network aims to connect everything from the physical world to cyber world, and has been a significant focus of research nowadays. Precisely monitoring network traffic and efficiently detecting unwanted applications is a challenging problem in IoT networks, which forces the need for a more fundamental behavioral analysis approach. Based on this observation, this paper proposes the Network Connection Graphs (NCGs) to model the social behaviors of connected devices in IoT networks, where edges defined to represent different interactions among them. Specially, focusing on exploring connected patterns and unveiling the underlying associated relationships, we employ a set of graph mining and analysis methods to select different subgraph structures, analyze correlated relationships between edges and uncover the role feature of interaction flows within IoT networks. The experiment results have demonstrated the benefits of our proposed approach for profiling linked-behaviors and to detect distinctive attacks in IoT networks.

Hangyu Hu, Xuemeng Zhai, Mingda Wang, Guangmin Hu

Location Privacy-Preserving Scheme Based on Multiple Virtual Maps

With the popularity of mobile devices, users are accustomed to enjoying abundant services which base on location information. On the other side, attackers can infer sensitive properties of users, such as the hobbies and habits, from location information. In order to protect the users’ location information privacy while enjoying the location service, many effective schemes are proposed. The traditional approach protects users’ location privacy by introducing a trusted third party, but it is difficult to find a fully trusted third party. An untrusted third party collects and obtains the user’s location information, thereby revealing users’ privacy. In this paper, we employ a fourth party to protect the privacy of users, where the fourth party sends to the users and the server multiple sets of urban distribution maps based on seeds without knowing the distribution of users. The map provides a mapping relationship between user location information and virtual location information. The fourth party divides the users’ service into two steps. First, the virtual location space is provided through the map. Second, users are allowed to send requests to the server through the third party in the virtual map space. The server returns the location of the points of interest in the virtual space. The virtual space provided by the fourth party makes it possible to prevent the users’ location information from being leaked even if the third party is attacked. The experimental results show that our method improves the quality of service under the premise of protecting privacy.

Shaojun Yan, Haihua Liang, Xinpeng Zhang

Low-Power Listen Based Driver Drowsiness Detection System Using Smartwatch

Drowsy driving is a major cause of car accidents, because drivers are unable to swiftly perceive, process, and respond to the varying road conditions. Existing detecting solutions includes checking eye-blink, monitoring heartbeat with EEG or ECG device support, and analyzing the way the driver steers the steering wheel. Though effective, these solutions require extra hardware which causes distraction and inconvenience to the driver. We design and implement an unobtrusive and energy-efficient driver drowsiness detection system using only a commercial smartwatch through monitoring the steering behavior and heart rate of the driver. The system comprises two major modules, a hand state monitor and a drowsiness detector. The system is built by following insights. First, when the hand wearing smartwatch is off the steering wheel, no validate steering data will be captured. Thus it’s necessary to detect whether the hand is on the steering wheel to ensure the validity of the steering motion data. Second, heart rate features can reflect the alert level of the driver, and it can work no matter whether the hand is on the steering wheel. Consequently, we adopt the heart rate sensor of the smartwatch as a supplementary indicator of driver’s drowsiness level. Meanwhile, power consumption is considered given the limited smartwatch battery power. We evaluate our drowsiness detection system using a driving simulator, and it achieves an accuracy of 94.39%.

Shiyuan Zhang, Hui He, Zhi Wang, Mingze Gao, Jinsong Mao

Malware Collusion Attack Against Machine Learning Based Methods: Issues and Countermeasures

Android has become the most popular platform for mobile devices, and also it has become a popular target for malware developers. At the same time, researchers have proposed a large number of methods, both static and dynamic analysis methods, to fight against malwares. Among these, Machine learning based methods are quite effective in Android malware detection, the accuracy of which can be up to 98%. Thus, malware developers have the incentives to develop more advanced malwares to evade detection. This paper presents an adversary attack pattern that will compromise current machine learning based malware detection methods. The malware developers can perform this attack easily by splitting malicious payload into two or more apps. The split apps will all be classified as benign by current methods. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. The evaluation results show that ColluDroid is effective in finding out the collusion apps. Also, we showed that it’s easy to split an app to evade detection. According to our split simulation, the evasion rate is 78%, when split into two apps; while the evasion rate comes to 94.8%, when split into three apps.

Hongyi Chen, Jinshu Su, Linbo Qiao, Yi Zhang, Qin Xin

Monitoring Home Energy Usage Using an Unsupervised NILM Algorithm Based on Entropy Index Constraints Competitive Agglomeration (EICCA)

Given that residential sectors in both developed and developing nations contribute to a significant portion of electric energy consumption, addressing energy efficiency and conservation in this sector is envisioned to have a considerable effect on the levels of nationwide and global electric energy consumption. Various approaches have been utilized to address these challenges with a number of positive outcomes being realized through Load Monitoring and Non-Intrusive Load Monitoring (NILM) in particular. These positive outcomes have been attributed to the increase in energy awareness of homeowners. Due to limited resources in a residential environment, methods utilizing unsupervised learning together with NILM can provide valuable and practical solutions. Such solutions are of great importance to developing nations and low-income households as they lower the barrier for adoption by reducing the costs and effort required to monitor electric energy usage. In this paper we present a low-complexity unsupervised NILM algorithm which has practical applications for monitoring electric energy usage within homes. We make use of Entropy Index Constraints Competitive Agglomeration (EICCA) to automatically discover an optimal set of feature clusters, and invariant Active Power (P) features to detect appliance usage given aggregated household energy data which includes noise. We further present an approach that can be used to obtain Type II appliance models, which can provide valuable feedback to homeowners. The results of experimental validation indicate that our proposed work has comparable performance with recent work in unsupervised NILM including the state of the art with regards to energy disaggregation.

Kondwani M. Kamoto, Qi Liu

Monitoring of Root Privilege Escalation in Android Kernel

The Android system has become the first operating system of the intelligent terminal market share as well as an important target of network attack. The root privilege of the Android system gives the user absolute control over the device, but root also lowers the security of the device and opens privileged access channels for the attacker. Temporary root has become an attacker’s favored attack technology based on the command issued by the attacker to complete root, and then to clear the root feature. Such a subtle attack on the detection of research work poses a great challenge. This paper presents a new monitoring method KRPM, which breaks the traditional defense idea, adopts active monitoring and alarming method, obtains all the current process information directly from the kernel, builds state graphs for access permission of the progress, and recognizes the process of root privilege escalation and process hiding. Through various experimental KRPM, the detection effect is good and the universality is strong, which can effectively monitor root power attack and exploit hidden rootkit.

Xueli Hu, Qi Xi, Zhenxing Wang

Network Defense Decision-Making Method Based on Stochastic Differential Game Model

In the actual network attack and defense, the attack-defense behaviors generally change dynamically and continuously. Besides, since kinds of random disturbance is inevitable, the evolution of network security state actually is random. To model and analyze network security problems more accurately, we used the Gaussian white noise to describe the random disturbance. Then from the perspective of real-time attack and defense, we characterized the random and continuous evolution of network security state referring to dynamic epidemical model and the Itó stochastic differential equations. Based on previous statements, the attack and defense stochastic differential game model was constructed, and the saddle point strategy for the game was proposed. Additionally, we designed an optimal defense strategy selection algorithm to achieve real-time selection of the optimal defense strategies in continuous and random attack-defense process, which has greater timeliness and accuracy. Finally, simulations demonstrated that the proposed model and method are valid, and we offered specific recommendations for network defense based on the experimental data.

Shirui Huang, Hengwei Zhang, Jindong Wang, Jianming Huang

Noise Modeling and Analysis for Indoor Broadband Power Line Communication

To analyze the noise characteristics on the power line, a practical noise model including Impulsive Noise (IN) and background noise is proposed. The parameters of IN are derived from measurements of household appliances, while the background noise is modeled based on the superimposed noise from multiple noise sources. In addition, an algorithm to calculate the disturb rate of IN is proposed, and the characteristic between actual noise model and the proposed noise model is compared. Simulation results show that the proposed noise model is very akin to actual power line noise. The results can be used to analyze, simulate, and mitigate the effect of the noise on Power Line Communication (PLC) systems.

Zhouwen Tan, Hongli Liu, Ziji Ma, Yun Cheng

Optimization Algorithm for Freight Car Transportation Scheduling Optimization Based on Process Scheduling Optimization

Aiming at how to improve the efficiency of logistics transportation, taking into account the main constraints of road conditions and the number of commodity vehicles to be transported, proposed a path scheduling algorithm based on path functionalization. That is, the scheduling optimization of the path is regarded as a process scheduling. The starting of the vehicle entering the path is regarded as the beginning of the process, and the return of the vehicle to the general station is regarded as the ending of the process. The purpose of saving scheduling time, shortening distance, and reducing fuel consumption is achieved by finding the optimal path and rationally scheduling the allocation. Ultimately improve the efficiency of transportation.

Changchun Dong, Liang Zhou

Power Data Cleaning Method Based on Isolation Forest and LSTM Neural Network

In the background of big data in power system, data cleaning of power operation and maintenance data can effectively improve data quality, making a good base for data analysis. In the process of data cleaning, the power data anomaly detection accuracy and data correction error have been a technical difficulty. To deal with these problems, we propose a data cleaning method based on Correlation isolation Forest and Attention-based LSTM (CiF-AL). This method constructs the isolation forest based on correlation between data attributes to extract the features of the training dataset, detects the anomalous data in the data set, and then uses the improved LSTM neural network model based on attention mechanism to predict and modify the anomalous data. The experimental results show that the power operation and maintenance data cleaning program based on CiF-AL has been effectively optimized in the accuracy of positioning of anomalous data, the accuracy of data correction, training time and resource consumption.

XingNan Li, Yi Cai, WenHong Zhu

Power Missing Data Filling Based on Improved k-Means Algorithm and RBF Neural Network

Power data mainly comes from power generation, transmission, consumption, scheduling and statistics. However, in the process of power data acquisition, problems such as data missing seriously affect the further analysis. In this paper, we propose a missing data filling method based on improved k-Means clustering and Radial Basis Function neural network (kM-RBF) to solve the problem of missing power data. Firstly, the data samples are clustered by k-Means, and the clustering results are used as the parameters of RBF neural network. The RBF neural network is trained with the complete data samples, and then the missing values are predicted. In order to verify the effectiveness of the algorithm, we have chosen the power consumption and power generation metadata of each province in China for analysis and simulated the absence of data. Simulation results show that the kM-RBF can obtain higher accuracy of missing data filling.

Zhan Shi, Xingnan Li, Zhuo Su

Properties Emulation on TD-LTE Electric Power Wireless Private

Aiming at the existing problems in the application of electric power wireless communication, a simulation platform for TD-LTE electric power wireless private network was established to better serve smart grids and conduct application research on time division long term evolution (TD-LTE) technology. The performance of power wireless network was simulated in aspects of link level and performance simulation, which also analyzed influences of wireless parameters on performance of wireless private network. Establishment of this simulation platform can provide technical support for unified construction of electric power terminal communication and further evaluate the network and application of LTE wireless private network, which effectively lay the theoretical foundation for late network planning.

Shujie Lu, Jia Yu, Ji Zhu

Spatial Search for Two Marked Vertices on Hypercube by Continuous-Time Quantum Walk

Search problem have a wide range of applications both in classical and quantum computers. In this work, the spatial search for a single marked vertex by continuous-time quantum walk (CTQW) is generalized to the search for multiple marked vertices. For many kinds of graphs with symmetrical structure, such as hypercube graph, the search for arbitrary single marked vertex is equivalent. However, this is not true for the search of multiple marked vertices and the search time is depend on the relative location of the marked vertices. We first give the spectrum and eigenspace of hypercube by using the theory of Cartesian product of graphs. Then, with the knowledge of spectrum and eigenspace, we analytical present the spatial search for all different configurations, namely all possible Hamming distance, of two marked vertices on hypercube. We find that although the different Hamming distance lead to unequal search time, this search can be done in $$\mathrm{O}\left( {\sqrt{N} } \right) $$ time for all two uniform marked vertices.

Xi Li, Hanwu Chen, Zhihao Liu, Wenjie Liu, Mengke Xu

Research and Application of Access Control Technology

The arrival of the information age has changed the human way of life and benefits to mankind. Moreover, the security problems are emerging. The security hidden danger becomes urgent in the process of information exchange and information transmission. Therefore, access control technology plays a major role for which is an ancient and important information security technology. Besides, the development of the network makes its application more extensive and makes its technology more mature, providing a strong and stable security guarantee for all walks of life in society. This paper introduces the technology of access control and access control first, then analyzes and description of the role-based, attribute-based, mission-based key technologies involved in current access control, and finally introduces the application of access control technology in big data, cloud computing, Smart Campus and so on, and describes access controls the specific key technologies and roles applied in these areas in detail.

Yixiang Jiang, Limei Fang

Research and Implementation on the Traceability Equipment of the Whole Agricultural Industrial Chain

In order to accurately track the whole industry chain process of agricultural products production, processing, warehousing, inspection, logistics and sales, and improve the effective transmission of traceability information in link docking, a set of traceability equipment is designed and developed. The device is based on the generated two-dimensional code and the uniqueness and authenticity of the time and space information. In conjunction with the PC management system, the whole process management and accurate tracking of the agricultural product supply chain such as production, processing, inspection, warehousing, logistics and sales are realized. The equipment is implemented using Beidou Global Positioning System, Radio Frequency Identification (RFID), two-dimensional code scanning and printing, signal conversion, GPRS telecommunications and other Internet of things-related technologies. Taking the Winter jujube as an example, the entire industry chain traceability tests and tests were conducted. It proves that the traceability equipment is highly efficient, and the device can guarantee the right to know about the quality and safety information of agricultural products based on the unique and authentic traceability QR code generated by the space-time information.

Jianyong Zhang, Pingzeng Liu, Bangguo Li, Changqing Song

Research of Subnetting Based on Huffman Coding

Subnetting has been widely used in computer networks and the Internet. The methods of subnetting are too difficult to grasp quickly for most beginners and ordinary users. With networks unreasonably parted, IP addresses will be seriously wasted. In order to save IP addresses, improve the utilization of IP addresses. In this paper, starting from the optimal network partitioning, in addition to the introduction of the average subnetting and subnetting based on Huffman coding, an improved subnetting method based on Huffman coding is also proposed. And an algorithm for the improved subnetting based on Huffman coding is implemented. The improved subnetting based on Huffman coding transforms the process of subnetting into Huffman tree structure and achieves the optimal partition of the network. Through the comparison of the IP address utilization of the three network partition methods, it can be concluded that the improved subnetting based on Huffman coding can fully improve the utilization of IP addresses and make most of the networks to be optimally partition. Improved subnetting based on Huffman coding is not only suitable for IPV4 but also for IPV6.

Ranran Li, Yongbin Zhao, Qing Xu, Xiaolin Qi

Research on Application of ATC Operation Security Based on Data Mining

In order to study the applicability of data mining in the study of ATC operational safety, take the six typical factors that may affect the safety of ATC as the former, and the level of unsafe incidents in ATC as the next term, use correlation analysis and Apriori algorithm, And set a reasonable degree of confidence in the rules, the degree of support for the rules, analysis of air traffic insecurity incidents. Taking the general ATC operational safety incident as an example, the results show that the data mining has applicability in the problem of ATC operational safety, and each of the influencing factors has a certain relevance; Each of the preceding factors has an impact on the safety of ATC operations, but the degree of impact is different. Among them, the factors that have a greater impact are mainly control load, airspace environment and control equipment.

Zhaoyue Zhang, Jing Zhang, Sen Wang

Research on Application of Network Security Technology Based on Data Mining

With the development of network technology, the problem of network security becomes prominent increasingly. The network security defense becomes the essential content of the network construction. Data mining technology can collect computer network data effectively and monitor the data safely. Data mining is of great significance to network security. This paper mainly starts from the basic principle of data mining, studies the data mining process and the main task, and analyzes the network security technology. So as to further explore the application strategy of data mining technology in the field of computer network security in order to maintain network security.

Ning Wang, Yanyan Qin, Shuyang Guo

Research on Big Data Fusion Method of Smart Grid in the Environment of Internet of Things

The mutual penetration and deep integration of the Internet of Things and the power grid make the modern power grid more intelligent. The big data of the smart grid is distributed among different levels of multiple business systems of each unit. There are different data structures, inconsistent patterns, and inconsistent standards. It is difficult for Chinese smart grid to manage multi-source heterogeneous data uniformly, This paper studies how to combine data fusion technology with enterprise management requirements, and converts distributed data in different business systems into a unified, accurate, and decision-oriented format. Accordingly, we can eliminate information barriers, share enterprise data resources, and promote the company’s management level as well. Firstly, data cleaning technology is adopted in this paper to preprocess multi-source heterogeneous data of the smart grid. The aim is to make a unified structure and facilitate the data fusion. Then a multi-source heterogeneous data fusion model is proposed to achieve data fusion in different levels according to the layered strategies of the Internet of Things. The data fusion and Markov logic network are used to focus on the data conflict problem in the process of fusion.

Ke Jia, Xiaoming Ju, Hongbin Zhang

Research on Building Energy Consumption Acquisition System Based on Configuration

Based on the problem of low stability and high network latency in the traditional building energy consumption acquisition system, in this paper, a building energy consumption acquisition system based on configuration is proposed. The system adopts the embedded technology and WAN communication technology such as TCP/IP, GSM, ZigBee, NB-loT and so on. Sensor-based system, the configuration system to support, embedded MCU as the core, a variety of network communication technologies complement each other, constitute the entire building energy collection system. Through experiments, the system can stably and quickly acquire the data information of the running equipment inside the building, and at the same time it can ensure the integrity and correctness of the data information transmission process. The system has the advantages of high automation, high reliability and fast transmission speed.

Qinghao Zeng, Renjun Tang, Xianjun Chen, Hang Pan, Jinlong Chen, Hui Zhou

Research on Feedback Effects Between Perception of Internet Word of Mouth and Online Reviews Based on Dynamic Endogeneity

Online reviews as the main communication forms of internet word-of-mouth (iwom) always were regarded as an exogenous variable in the study of existing literature, and the dynamic relationship was regarded as a static single direction between them. Under the dynamic endogeneity, the control variables which can be measured and the dummy variables which are difficult to observation and measurement outside of online reviews are introduced. And in the dynamic panel data model with online reviews of mobile phone as the research object which were released during March 1, 2015 to July 1, 2015 from, the endogeneity is controlled by the control variables and the dummy variables, the paper demonstrates that: In the static analysis framework, online reviews and iwom perception are influence each other. The Dummy variables impact online reviews and iwom perception at the same time.

Jinhai Li, Yunlei Ma, Huisheng Zhu, Youshi He

Research on Fire Image Detection Technology Base on RBF

Image fire detection technology can solve the problem of large space fire safety effectively. It is difficult to accurately divide the flame area because of the complex background of large space fire image, so it has a higher problem of false alarm. We propose a three-layer combination segmentation model, which use the differential technology, RGB color segmentation technology and morphological difference technology, the suspected area of the flame is obtained by excluding most of the interference. Some characteristics such as similarity measure, area change value, density, eccentricity ratio, offset distance of centroid point are extracted from suspected area of fire image. Finally, the fire identification model is established by RBF neural network, and the extracted flame characteristics is used as input to classify the fire images. A series of fire images and sample images have been experimented, the simulation results show that the algorithm can reduce the fire alarm rate effectively and improve the accuracy rate of fire alarm.

Li Jin, Li Li

Research on Indoor Positioning Method Based on Visible Light Communication Technology

Indoor visible light positioning becomes attractive due to the increasing demands of location-based services. In this paper, we presented a model of an indoor positioning system based on visible light communication technology firstly. The system uses white light emitting diodes (LED) as light sources, so it has the dual role of communication and lighting. Then, the transmitter and receiver of visible light communication in the system have been designed and realized. Next, a coding protocol was designed and implemented, which can be used to transmit ID of each light source. Finally, the error performance in experiments of the proposed system is analyzed and several suggestions on future research of indoor positioning based on visible light communication were given.

Hongwei Zhu, Yajun Liu, Yingjiu Guo, Jing Jiang

Research on Real-Time Storage Technology of UAV Freight Stream Data

Aiming at the characteristics of large volume, fast storage and interaction speed of unmanned aerial vehicle (UAV) freight data, a new real-time storage optimization method for UAV freight stream data based on HBase is designed. In this paper, we introduce the JavaNIO non-blocking communication technology to reduce system overhead, and adopts a multi-queue thread pool mechanism of priority dynamic switching to solve the problem of high concurrent transaction processing. Finally, the data is stored in parallel to the HBase cluster server using the row key optimization strategy and the multi-source data queue partition strategy. The experimental results show that compared with the native HBase method, the storage performance of the system is greatly improved, and it has a good performance of high concurrent transaction processing.

Xiao Long, Liang Zhou, Hongyuan Zheng

Research on Syndrome Classification and Risk Factors Extraction of Tibetan Medicine Based on Clustering

Clustering which can divide data into a lot of subsets is one of the significant methods in the field of data mining, machine learning, artificial intelligence and so on. It is an unsupervised learning method and can solve the problem which is how to divide some unlabeled objects. The characteristic is that there is no need to provide priori information for clustering analysis. Usually, the procedures of clustering are feature selection, similarity degree calculation, clustering algorithm selection and conclusion test. Choosing different methods on each procedure is a rule which can distinguish clustering algorithm. The purpose of this paper is researching on the ways of common plateau diseases Tibetan medicine syndrome classification and risk factors extraction. Based on the diagnosis data of chronic atrophic gastritis provided by Qinghai Tibetan hospital, this paper uses Elbow Method to choose the best cluster number and applies Weka to classify syndrome according to five clustering algorithms after data preprocessing. Based on the analysis of experiment results and evaluation criteria, the suitable algorithm is selected and the risk factors are extracted. After comparing the algorithms and experiment results, it can be concluded that EM algorithm is effective and it has obvious advantages in discrete data.

Chaoyi Liu, Lei Zhang, Lu Wang, Xiaolan Zhu, Xiaoying Wang

Research on Two-Factor Identity Authentication System Based on Smart Phone and User Password

This paper studied the traditional two-factor authentication system, integrated public-key cryptography, Bluetooth communication, two-dimensional codes and other technologies, designed and realized a new two-factor identity authentication system based on smart phone and user password, which includes three main entities: mobile authentication client, browser extension, and web server. The mobile phone was used to replace the traditional physical authentication devices. Not only does it reduce the cost of manufacturers, but also is easier for users to use. The system is transparent to the people that they do not need to learn new knowledge before using the authentication system. Besides, compared to the traditional two-factor authentication, the system has reached the same security. Our system can resist the man-in-the-middle attacks, phishing attacks, replay attacks and others effectively. The system that we present is reliable and easy to manage, moreover, it has the good portability and the advantages above have important significance to the improvement of the identity authentication.

Lin Hou, Laiwen Wei, Chen Wang, Andi Wang, Jian Xu

Review on Blockchain Application for Internet of Things

Internet of Things (IoT) is a network that connects lots of smart devices around the world. As a revolutionary technology, it has been developing rapidly in recent years. This paper summarizes the obstacles of IoT in terms of security and efficient operation network and introduces the Blockchain to solve these problems. Nowadays, many IoT application use the traditional central structure and collect all data in one center node. However, with connected devices on the rise, it is required that the center node should have a huge computing power, storage space and bandwidth, thus making the operation cost become higher. For IoT, another focus is all about private and security. All transaction data are stored in the third party, which means hackers can break security barriers and steal user information easily. Moreover, some businessmen will sell user information to other parties without owner’s authorization. Through this paper we find that Blockchain can provide a distributed, transparent platform with trustless mechanism and collective maintenance of security for the IoT. But, applying Blockchain directly to IoT may result in many problems, such as limited resource, longer delays, poor scalability. At present, there is not a Blockchain model which can be applied in large scale IoT networks. The future study can be followed along this line.

Qin Zhou, Yaming Yang, Jinlian Chen, Mingzhe Liu

Rumor Spreading Model Considering Rumor’s Attraction in Heterogeneous Social Networks

In this paper, we propose a modified susceptible-infected-removed (SIR) model with introduction of rumor’s attraction and establish corresponding mean-field equations to characterize the dynamics of SIR model on heterogeneous social networks. Then a steady-state analysis is conducted to investigate how the rumor’s attraction influences the threshold behavior and the final rumor size. Theoretical analysis and simulation results demonstrate that the rumor spreading threshold is related to the topological characteristics of underlying network and the infectivity of individual but is independent of the attraction of the rumor itself. In addition, whether a rumor spreads or not is determined by the relationship between the effective spreading rate and the spreading threshold. We also find that when a rumor’s attraction is very high, the effective spreading rate can easily reach the critical rumor spreading threshold, which leads to rumor spreading far and wide.

Ling-Ling Xia, Bo Song, Liang Zhang


Weitere Informationen

Premium Partner