Industrial engineering, machine design and automation iema 2014 and computer science and application ccsa 2014

Contents Editorial Chapter 1: Communication and Information Technology An Energy Consumption Assessment Method for WIFI Large-Scale Wireless Sensor Network Based on Dynamic Channel Ene

Industrial Engineering, Machine Design and Automation (IEMDA 2014) &

Computer Science and Application (CCSA 2014)Proceedings of the 2014 Congress on IEMDA 2014 &

Proceedings of the 2nd Congress on CCSA 2014

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College of Technology, Indiana State University, USA

Proceedings of the 2014 Congress on IEMDA 2014 &

Proceedings of the 2nd Congress on CCSA 2014Sanya, Hainan, China 12 – 14 Dec 2014

British Library Cataloguing-in-Publication Data

ConferenCe ProCeeDings for 2014 Congress on inDustriaL

engineering, MaChine Design anD autoMation (ieMDa 2014) anD

the 2nD Congress on CoMPuter sCienCe anD aPPLiCation (CCsa 2014)

system now known or to be invented, without written permission from the publisher.

Editorial

It has been a great pleasure for me to welcome all of you to the joint conferences of

2014 Congress on Industrial Engineering, Machine Design and Automation (IEMDA2014) and the 2nd Congress on Computer Science and Application (CCSA2014), held in Sanya, China during December 12-14, 2014 During these two-days, international speakers presented their state-of-art research works in industrial engineering, machine design, automation, and computer science to solve today industrial problems We hope you enjoy this opportunity to share the results and make new connection for future collaboration

The conference program consisted of two invited keynote presentations and 4 invited sessions: Communication and Information Technology; Research and Design of Machines and Mechanisms for Manufacturing; Data, Signal and Image Processing, Computational Technology; Mechanical, Automation and Control Engineering

This proceedings collected together the latest research results and applications on industrial engineering, machine design, automation, and computer science and other related Engineering topics All submitted papers were subjected to strict peer-reviewing by 2-4 expert referees, to ensure that all articles selected are of highest standard and are relevance to the conference

On behalf of the organizing committee of IEMDA2014 and CCSA2014, I would like to take this opportunity to express our sincere appreciations and thanks to all authors for their contributions to the conference As well as to all the referees for their time in reviewing the articles and their constructive comments on the papers concerned Finally, as the editor of this proceedings, I am indebted to the support

of the organizing committee for their hard works, World Scientific for their support in publishing this proceedings in such short space in time Without these

excellent supports, the IEMDA2014 and CCSA2014 would not able to publish so timely and successfully

Prof Shihong Qin

Editor of IEMDA2014 and CCSA2014

Contents

Editorial

Chapter 1: Communication and Information Technology

An Energy Consumption Assessment Method for WIFI Large-Scale

Wireless Sensor Network Based on Dynamic Channel Energy Model

W.K Tan, X.Y Lu, Y.X Xu, K.J Zhao and P Gao 1

Research on Cloud-Based LBS and Location Privacy Protection

Y Yan and W.J Wang 9

Research and Exploration of the Hierarchical Management of Campus

Network

C.B Liu, T.Y Zhou, S.L Cai and K Sha 16

Improvement of Localization Algorithm for Wireless Sensor Network in

Environmental Monitoring

C Liu, S.L Wang, Y Ma and Z.Q Zhai 22

A New Study on Bank Interconnected System Security Solutions

J Lin 29

Research on Simulation Platform for Security and Defense of Oil and

Gas Network SCADA System

Q.C Hu, X.D Cao, W.W Zhang, P Liang and Y Qin 34

Research on Print Concentrated Control Scheme Based on Virtual Print

Technology

Y Xie, X.L Zhou, Z.P Wen, G.L Li, S.P Liu and Q Hu 41

Developing Real Time SCM Information System

C.T Huang, C.W Hsu, C.H Hung and W.L Wang 48

An Anomalous Traffic Detection System of the Controlled Network

Based-On the Communication Rules

W.J Han and Y Wang 55

The Study of HARQ Technology FDD-LTE Physical Layer Downlink

W.B Tang and L.P Wang 61

Integrating with Information of Science and Technology on the Meta

Data Ways’ Knowledge Base—the Pyramid Model to Aid the Decisions

in Science and Technology

K Hong, X Chen and J.R Hu 67

Sliding Window Frequent Items Detection in Wireless Sensor Networks

S Wang and L.N Wu 76

Research on Security Mechanism of Cloud Security Immune System

L Huo, J.X Zhou and X.W Liu 83

Design and Realization of Solar Cell Monitoring System Based on

Wireless Sensor Network

X.B Sun, Y Huang, J.J Tan, J.Q Yi and T Hu 90

Research of Multi-UAVs Communication Range Optimization Based on

Improved Artificial Fish-Swarm Algorithm

J.H Wu, J.Z Wang, Y.Q Cao, Y Cao and X.B Shi 98

Analysis of Limiting Factors and Numerical Simulation in Optical

Transmission System

B Yang and W.P Zhong 105

An Improved Timestamp-based User Authentication Scheme with Smart

Card

T.H Feng, C.H Ling and M.S Hwang 111

Cryptanalysis and Improvement of Zhuang-Chang-Wang-Zhu Password

Authentication Scheme

S.M Chen, C.S Pan and M.S Hwang 118

Chapter 2: Research and Design of Machines and Mechanisms for

Manufacturing

Research on Wind Power Simulation Model

Y.S Zhang, A.N Tian and Y.L Pan 124

Model Test Study of Influence of Soil Bag Stacked Form on Ground

Bearing Characteristics

W Li, X.Y Shan and Z.B Jia 131

Analysis and Application of SMED in an Injection Molding Machine

Based on VSM

M Lv, N Zhang, J.C Jiang and Q Wang 143

Equivalent Mechanical Model to Support Real-Time Simulation of the

Deformation of Thin-Walled Structures

L.Z Tu, Q Yang, Y Zhuang, A.L Lu, Z Lin and D.L Wu 150

Nanoscale Sliding Contacts between Multi-Asperity Tips and Textured

Surfaces: Effects of Indentation Depth

T Zhang, D Wu, F Zhang, X.K Mu and R.T Tong 161

Based on Epsilon Method Structural Non-Probabilistic Reliability

Analysis

K Ma and H.P Fu 168

Research on Modeling and Simulation to Control WIP Inventory in EMS

Enterprises Based on Bottleneck

M Cai, T Shang, H.B Liu and H Chen 175

Screening Customer Order under Engineering-To-Order Environment

H.E Tseng and S.C Lee 185

Genetic Algorithm with Unit Processor Applied in Fused Deposition

Manufacturing (FDM) for Minimizing Non-Productive Tool-Path

J Gong and L Zhang 191

Simulation and Analysis of Edge Cam Downward Mechanism Based on

Contact Dynamics Model

J Lu, J.J Zhang, F Lu and X.H Pan 198

Complex Product Collaborative Development Framework

L.W Zhang and W Shen 206

The Similar Conditions and Similar Criterions of Deep-Sea Mining

Experimental System

Y Xu, X.F Zhang, L Liu and W.M Zhang 217

Reliability Analysis of an Air Supply System Design by Shortest Path

Approach Based on Directed Network

P Jiang and Y.Y Xing 224

Genetic Design of Integrated Manufacturing Supply Chains

W Su, K.L Mak and B.L Qiu 230

The Interfacial Rheological Characteristics of ASP Solution and Its

Effect Mechanism on Oil Displacement Efficiency

H.F Xia, Q Fan, M Xu and X Lai 237

Chapter 3: Data, Signal and Image Processing, Computational

Technology

A New Image Edge Extraction Method Combining Canny Algorithm and

Butterworth High-Pass Filter

G.Y Zhang, S.L Chen and K.G Kang 244

Design of a Log Analysis System Based on Hadoop

J.M Li 249

On Computation Theory Problems in Computer Science

R Chang, L.H Jiang, H Shu and Y.B Xie 257

The Algorithm of Target Tracking Under Complex Environment

C Liu, Z Wang and C.H Lu 264

Implementation of Reliability Testing on WebApp Based on TTCN-3

S.M Liu, X.M Liu and Y.P Liu 270

Exploration and Practice in the Reform of the Computer Basic Course

Education Based on the Mode of Compound Talents Training

Q.L Wang 278

De-Noising of Life Feature Signals Based on Wavelet Transform

Y Liu, J.F Ouyang and Y.G Yan 284

Cluster-Oriented Pattern of Web Service Composition

S.Y Deng and Y.Y Du 292

Novel Robust Digital Image Zero-Watermarking Scheme Based on

Contourlet Transform and Cellular Neural-Network

B He and X Wang 304

Unambiguous Synchronization Technique for BOC Signals

J.M Qi, Y Geng and L Mao 310

An Improved Image Registration Method Based on SIFT

K Yang, M.X Zhang, X.B Xian and J.L Zheng 317

Efficient Compressive Signal Recovery Using Prior Statistical

Information

X.T Chen, Z Sun, Y Zhao, S.S Wang and S.Y Liu 324

Automatic Extraction of Commodity Attributes on Webpages Based on

Hierarchical Structure

Z Yu, M.Y Li, W Wang and C Wang 332

Data Analysis and Visualization for National Disabled People’s

Rehabilitation

D.Z Wang, X.B Zhang, X Lou, Q.Q Zhang and X.F Wu 339

Uniform Design and Quadratic Polynomial Regression Analysis Applied

on the Optimization of Compatibility Study in Traditional Chinese

Formula

Y Zhao, H.N Liu, B.T Li, Q Y Zhang and G.L Xu 346

The Image Encryption Algorithm Based on M-Subsequence

H Guo, S Bai, X.Y Ji, H Han and Z.L Pei 352

Research and Application of GIS Data’s Dynamic Storage Technology

Based on Streaming Data Technology

L Liu, W Chen and J Liu 367

Chaotic System Parameter Estimation with Improved Gravitational

Search Algorithm

J.R Wang, Y Huang and W.P Liang 374

Document Clustering Based on Non-Negative Matrix Factorization and

Affinity Propagation Using Preference Estimation

J.W Chen, F Li, X.F Wu and Q.Q Zhang 380

A Fast Iris Image Assessment Procedure

Q Wang, T Zhang and H Wang 386

Chapter 4: Mechanical, Automation and Control Engineering

Practical Thermal Condition of Silicon CVD Reactor for Minimal

Manufacturing

N Li, Habuka, S Ikeda, Y Ishida and S Hara 393

Sail Structure Design and its Control Strategy for Sail-Assisted Ship

J H He, Y.H Hu and S.Y Xue 401

Overall Stability Performance of Alternative Hull Forms of an

Automated Oceanic Wave Surface Glider Robot Using Maxsurf

A Elhadad, W.Y Duan and K.Y Hu 412

Kinematics and Mechanics Analysis of Economical Welding Robot

J.J Wei and S.W Cui 421

The Application of Fuzzy-PID in Speed Control of Smart Car

C Wang, X.C Dong, Y Tang and S.F Gu 430

Application of Speed Sensorless Vector Control in the Induction Motor

Y Gao, Q.R Zhang, A.R Xu, L Zhang, D Bai and Q.P Zou 437

Design and Feasibility Study of Slip Vibration Platform

W Peng, G.P Chen and X.Y Yan 445

Study on Conditions of Planar Pin-Jointed Five-Bar Mechanism with the

Requirement of Minimum Transmission Angle

Z.H Luo 452

The Design and Research of the Vehicle Intelligent System of Avoiding

Sleeping Based on Pulse

S.X Qian, Z.H Yu, X.M Shen and F.L Huang 458

Application Research of the Special Amphibious Vehicle Driving

Simulator

J.H Li and S.T Zheng 466

Static Output Feedback Reliable Control with Actuator Failures

D Ge, B Yao and F.Z Wang 473

Design of Dynamic Output Feedback H2 Reliable Control Based on LMI

N Peng, B Yao and F.Z Wang 479

Application Research of Neural Network Hybrid Modeling Method for

Torque Measurement on Centrifuge Suspended Basket Trunnion

S.L Chen 484

Study on Additional Damping Control Strategy of Permanent Magnet

Synchronous Generator

S.Y Ye, Y.T Zhang, R.J Ruan, Q Tang, S.L Dai, and T.Y Wang 492

Multi-Application Integrated Intelligent Maintenance System of

Lead-Acid Batteries on Communication Bases

X.F Tong, C.C Sun and F Huang 502

Measurement and Control System by Computer for High Voltage

Termination

C.G Zhou, R Qiu and J.L Ke 509

An Energy Consumption Assessment Method for WIFI Large-Scale Wireless Sensor Network Based on Dynamic Channel Energy Model

Weikai Tana†, Xiaoyuan Lub, Yunxiang Xuc, Kejun Zhaod and Peng Gaoe

National Engineering Research Center for Broadband Networks & Applications,

Shanghai 200336, China E-mail: a† wktan@bnc.org.cn, b xylu@bnc.org.cn, c yxxu@bnc.org.cn,

d kjzhao@bnc.org.cn, e pgao@bnc.org.cn

Energy efficiency is one of the most serious constraints for the deployment of large-scale wireless sensor network (WSN) significantly However, an excellent strategy to raise energy efficient depends on a precise energy consumption assessment method In this paper, an energy consumption assessment method based on dynamic channel energy model is proposed Energy consumption is divided into two parts: static and dynamic The former includes receiving, idle and clear channel assessment state, whose energy consumption is only related to a stationary working current and duration Transmission energy consumption refers to dynamic energy consumption, function of which is described

as a cubic function The energy consumption calculation is adjusted to meet the transmission power dynamically and timely Simulation results show that the dynamic energy consumption during transmission is summed accurately Compared with the others, such as some simple energy models without dynamic case It provides to support for the deployment of WIFI large-scale WSN

Keywords: Channel Energy Model; Large-Scale Sensor Network; Energy Consumption

Assessment

1 Introduction

Recently, wireless sensor network (WSN), providing emergency monitoring, remote monitoring and environmental awareness, has been significantly interested With the development of sensor network theory and technology, it has been widely used Most of the applications are still limited to a small-scale wireless sensor network However, many applications require large-scale deployment to achieve high coverage, high-precision sensing purposes, such as forest fire monitoring 1 Large-scale wireless sensor network based on WIFI has received a lot of attention 2

In WSN, two aspects of problems we face to are the limited battery life and efficient usage of energy, which become more serious in large-scale WSN In fact,

compared to small scale applications, large-scale WSN manages a large number of nodes to achieve high coverage, which leads to greater energy consumption Therefore, strategies, such as routing and QoS control, must be improved to raise energy efficiency3 However, the design of an excellent strategy depends on a precise energy consumption assessment method extremely Especially, the assessment results of the impact from the varying circumstances where the sensor works This directly affects the validity of the strategy design In 4, authors suggest

a simple energy model The model only takes into account energy dissipation during the start-up, receive, and transmit modes For the transmit energy it’s too simple In 5, a radio energy dissipation model is described but it isn’t accurate enough Various energy-efficient methods are considered in literatures but only use the simple model of energy consumption, which leads to a fuzzy simulation result and a fuzzy effectiveness of their proposed methods 6, 7 Therefore, an accurate energy consumption assessment method for WIFI large-scale wireless sensor network is proposed to provide an exact reference for the deployment of WIFI large-scale WSN

2 Sensor Model

A sensor usually consists of the following subsystems: communication subsystem, processing subsystem and sensor subsystem The energy consumed by communication subsystem is much higher than processing subsystem, up to 80 percent Thus, the communication subsystem is main source of system energy consumption in WIFI large-scale sensor network 8

Basic circuit

Another sensor

d

mod

frequency synthesizer VCO

Receive circuit Demod

Sensor/DSP

filter

Fig.1 Sensor model

The basic structure of a sensor is shown in Fig.1 We divide the energy consumption calculation into two parts: dynamic and static Dynamic energy consumption includes a short-circuit power that flows directly from the supply to ground during a transition at the output of a CMOS gate Dynamic part, namely transmission circuit (TX), is composed of digital to analog converter, modulation

circuit and generates emission signal Static energy consumption is associated with maintaining the logic values of internal circuit nodes between the switching events, such as basic circuit Static part includes: (a) basic circuit (BA) composed

of voltage controlled oscillator (VCO) and frequency synthesizer, provides the power and frequency of the basic circuit; (b) transmission amplifier (PA) is signal modulation circuit to FM and launch; (c) receiving circuit (RX) is the low noise amplifier (LNA), mixer, filter, intermediate frequency amplifier and demodulation circuit, AD converter; (d) Sensor is detection of Sensor signals

3 Proposed Dynamic Channel Energy Model

In communication subsystem, node state can be divided into four types: transmitting state, receiving state, idle state and clear channel assessment (CCA) state They are represented by STX, SRX, SCC, SID, respectively

We have to pass through the state of SID when switching between any two states of STX, SRX and SCC EID, ETX, ERXand ECC are used to represent the energy consumption of each state; IID, ITX, IRX and ICC are used to represent the current of each state From dynamic and static parts analyzed in Section Sensor Model, the total system energy consumption can be calculated as

=

= ∑ , (3) where U is the working voltage, N is the time of IDLE state, t i is the

duration of i-th time of IDLE state As the same as Eq.3, ERX can be obtained by

1

N i i

i

t i

transmission power because transmission power is easy to be obtained while the current is impossible to be measured Then, the current is necessary to be estimated by transmission power In most datasheets, only several current values with respect to current transmit power are given Fig.2 shows the relation between transmission power and current in common WIFI chips such as CC2500, CC2420 and rn171

In Fig.2, the three curves in the right side describe linear transmission power and those in the left side describe the power transformed to dBm Some curves in the left side are convex, whereas the others are concave However, all of the curves

in the right side are concave, meaning of which is easy to find the functional relationship between transmission power and current This function can be approximated by a cubic function Basic circuit current only shifts the curve but

not change the original shape So, the basic circuit current is not considered as a parameter

1x 10-3

0.005 0.01 0.015 0.02

Fig 2 Transmission power in CC2500, CC2420, and rn171

Therefore, at time t, the power functional relationship between the

transmission power and current is

PDTX,t =a II DTX,3 t+b II DTX,2 t+c II DTX,t+dI , (8) where aI, bI ,cIanddI are constants Similar to Eq.8, the current can be obtained by

DTX,t DTX,t DTX,t DTX,t

I =aP +bP +cP +d , (9)

where a , b c and d are constants We take the WIFI chip of CC2420 as an

example The current function is fitted as follows

DTX,t 4.9 10 DTX,t 1.6 10 DTX,t 2.1 10 DTX,t 10.9

In Fig.3, it can be shown that the current function of CC2500, CC2420, and m171 can be calculated correctly by using Eq.10

20 30

150 200 250

10 15 20

current in datasheet Fitting curve

Fig 3 Transmission current function ofCC2500, CC2420, and m171

4 Simulation Results

We propose a sensor network with 50, 100,…,250 nodes These nodes are located

at 500×500m network topologies The nearest distance between any two nodes is 20m The WIFI chipBCM4330 we used is widely applicate, current consumption

of which in each state refers to 9 By Eq.9, its transmission current model is fitted

as

IDTX,t = −443PDTX,3 t−1986PDTX,2 t+523PDTX,t+217 (11) Fig.4 shows the transmission current fitting result for BCM4330 by Eq.11 Fig.5 shows comparison of energy consumption per-state of BCM4330 under dynamic channel energy model and simple energy model The total energy consumption is also presented as references We observe that their energy consumption is the same (i.e., their curves are overlapped in Fig.5) in RX, CCA BUSY and IDLE states However, in TX state, the energy consumption in simple energy model is the same at any time, while in dynamic channel energy model the

energy consumption is lower and changes by different number of nodes Because Interference and conflict with the rising number of nodes is increased, sending a bit will consume more energy In fact, the transmission power in simple energy model can be only modeled by a maximum transmission power, but in dynamic channel energy model, it can be modeled by the actual energy consumption corresponding to the minimum transmission power at different transmission distances

Fig 4 Transmission current model for BCM4330

Fig 5 Comparison on energy consumption for BCM4330 in different energy models

5 Conclusions

In this paper, we have analyzed the energy consumption in a large-scale WSN, and proposed an energy consumption assessment based on dynamic channel energy model The energy consumption model is divided into static and dynamic parts in order to reflect the power dissipation changing over time A cubic function is proposed to model this power dissipation Simulation results show the transmission current model inBCM4330 can be fitted by the cubic function exactly Based on our proposal, the energy consumption results are closer to the actual value than others The proposed method reflects the dynamic energy consumption accurately The total energy consumption of model simulations is summed accurately while the others have not considered

References

1 S Park, E Lee, F Yu, Scalable and robust data dissemination for large-scale

wireless sensor networks, IEEE Transactions on Consumer Electronics

2010, 56 (3) 1616-1624

2 Z Jianping, T Zhengsu, L Chunfeng, Performance improvement for IEEE 802.15.4 CSMA/CA scheme in large-scale wireless multi-hop sensor

networks, IET Wireless Sensor Systems 2013, 3 (2) 93-103

3 R Iyer, L Kleinrock, QoS control for sensor networks, IEEE International Conference on Communications, 2003, (1) 517-521

4 A.Y Wang, C.G Sodini, A simple energy model for wireless microsensor

transceivers, IEEE Global Telecommunications Conference 2004, (5)

3205-3209

5 W.B Heinzelman, A.P Chandrakasan, H Balakrishnan, An application-specific protocol architecture for wireless microsensor networks,

IEEE Transactions on Wireless Communications 2002, 1 (4) 660-670

6 Koutsopoulos, S Stanczak, The Impact of Transmit Rate Control on

Energy-Efficient Estimation in Wireless Sensor Networks, IEEE Transactions on Wireless Communications 2013, 11 (9) 3261-3271

7 S.D Muruganathan, D.C.F Ma, R.I Bhasin, A centralized energy-efficient

routing protocol for wireless sensor networks, IEEE Communications Magazine 2013, 43 (3) 8-13

8 M Pedram, J Rabaey, Power Aware Design Methodologies, first ed., New

York, 2002

9 Broadcom, Single Chip IEEE 802.11™ a/b/g/n MAC/Baseband/Radio with Integrated Bluetooth® 4.0 + HS and FM Transceiver, Preliminary Data Sheet BCM4330 2011

Research on Cloud-Based LBS and Location Privacy Protection

Wan Jun Wang

Information Engineering College, Lanzhou University of Arts and Science, China

E-mail: wangwanjun1@163.com

Location-based services have already been widely used in many different areas With the popularization of intelligent terminals, providing mobile internet services on the cloud have enormous commercial prospects However, the high adhesion degree of mobile terminals to users not only brings facility but also results in the risk of privacy leak The paper emphasized the necessity and advantages to provide mobile internet services based

on cloud computing technology, analyzed the security issues of location privacy to LBS system brought by mobile cloud computing, and proposed the framework and implement method of LBS system under mobile cloud computing environment

Keywords: Mobile Cloud Computing; Location-Based Services; Location privacy;

Confidentiality; Completeness

1 Introduction

With the rapid development of mobile communication technology and the growing popularity of intelligent terminals, there is an urgent need to get information and services from the Internet at anytime and anywhere even during the movement Among the services in mobile internet, location-based services (LBS) are the most widely used one Via different kinds of positioning technologies (such as satellite positioning, network-based positioning, sensing positioning, etc.), location-based services can provide many personalized services for mobile users according to their locations[1]

However, the high adhesion of mobile terminals to users not only brings facility but also brings new security risks If the specific location information of user has been leaked while using LBS services, it may disclosure privacy information The paper analyze the needs of location privacy protection of LBS services under mobile cloud computing environment, and propose systematic

framework and implementation method of LBS services based on mobile cloud computing

2 Location-based Services on Cloud Computing

The continued developing and integrating of Cloud Computing and Mobile Internet results in a new application model—Mobile Cloud Computing, which provides a new business model for LBS services LBS providers do not have to invest a lot of money and equipment to improve their storage and query capabilities, and do not necessarily need to have their own cloud platform, but to outsource their data and services on to the cloud computing platform and achieve massive data storage and query services

Developing location-based services on cloud computing platform has many advantages Firstly, compared with desktop computers, the significant problem

of mobile terminal is lacking of resources LBS system based on cloud computing breaks through the hardware limitations of terminals, it transfers complex calculation and data query processing from local to the "cloud" [2] Users only need a smart mobile device to send commands to the "cloud" and receive data from it Secondly, cloud computing makes unified management and scheduling on large number of hardware and software resources, and forms a resource pool to provide services to users according to their demand LBS providers can outsource their data and services to the cloud computing platform without investing a lot of money and equipment to improve the storage and query capabilities Thanks to the huge resource pool supplied by cloud computing, it not only solved the massive data storage problem, maintenance pressure and bottlenecks caused by high concurrent retrieval, but also improved the service quality and scalability of system, facilitated the access of location-based services

3 Location Privacy of LBS System

3.1. Privacy protection of LBS

In order to protect user’s location privacy, the most frequently used method is to publish a pseudonym, or adopt spatial and temporal cloaking to prevent or reduce the recognizability of positional information Representative algorithms for the first kind is the SpaceTwist method proposed in reference [3] Marco Gruteser is the first one to use the concepts of K-anonymity in location privacy protection, and proposed location-based K-anonymity method in reference [4] Its

main idea is to include at least k users in a certain region (called anonymous

region) and the users cannot be identified by their ID number, so that an

adversary may manage to identify that a spatial region has been visited by k

different people, but it will not know who was there at time of the service

request On the basis of k-anonymity, many other algorithms have been

proposed[5][6]

3.2. New security problems brought by mobile cloud computing

The combination of cloud computing and mobile internet introduces new security risks of cloud computing technology, which brings unprecedented security challenges to user’s data In the model of mobile cloud computing, the right of ownership and management of user data has been separated Service providers will store and manage their geographic data and information through the cloud platform, and end-user will query, access and transmit information via the mobile Internet How to ensure the correctness of information storage, access, management and destruction on the mobile cloud platform? How to prevent user data from been lost, stolen, tampered during the network transmission? These are all the major problems to deal with under the mobile cloud computing environment

4 Cloud-based LBS System and its Location Privacy Protection

4.1. Improvement on system structure

LBS system based on cloud computing mainly consists of three entities (shown

in Fig.1) As a bridge, cloud platform provides storage and computing resources for location service provider on one hand, on the other hand, it provides frequent interaction and inquiry services for end users

Implement process of the cloud-based LBS system can be divided into two stages Firstly, location service provider has to complete the collection and settlement work of geographic data in advance, form the final location information that can be published and upload them to the cloud platform for storage This process can be regarded as an “offline” pretreatment Because there is not excessive need for the real-time requirements, the "offline" processing stage can give priority to the protection extent of data privacy The real-time interactive processing between location service provider, cloud platform and users belongs to the "online" stage (shown in Fig.2) Each user should register a unique account in the location service provider Register information has been processed in order to protect the privacy information of users and store in the cloud database Then, users should log in to the location

Fig 1 Structure of LBS system based on cloud computing

Fig 2 Interactive processing in “online” stage

service provider with their ID and password before they use LBS services During the stage of activity, users send their service request to the location service provider, which contains the physical location information and protection extent request of privacy Location service provider then initiate the query progress and get the query result from the cloud database according to certain strategies determined in advance Final result will be sent back to the user and relevant records will be stored in the cloud database

4.2. Improvement on algorithms

The LBS system not only needs to achieve the confidentiality of temporal and spacial data outsourced to the cloud platform, but also needs to prevent tampering and deleting of the query results by cloud service providers or illegal attackers We propose a new method to achieve confidentiality, completeness and authentication [7][8] at the same time, and it adopts the following principle (1) On the side of transmitter:

Step 1: Use some irreversible encryption methods (for example the Hash

functions) to calculate the feature code of data waiting to be sent, in order to verify the completeness of data This process can be described as

Hash data →F

Step 2: The transmitter encrypts the feature code F by his private key

c

SK : encode F SK( , c)→C F The new feature code C after encryption will F

be put behind the data waiting to be sent, in order to authenticate the identity of transmitter

Step 3: The transmitter generates another key K , and encrypts the data D

segment composed by original data and the new feature code C after F

encryption: encode data( +C F,K D)→Q That will be used to ensure the confidentiality of data during network transmission

Step 4: Use the public key of receiver PK to encrypt u K , and set the D

result behind Q to form the final transmission data:

( D, u)

Q+encode K PK →H

(2) On the side of receiver:

Step 1: The user decrypts the received data and gets K by using its D

private keySK : u decode H( −Q SK, u)→K D

Step 2: Use K to decrypt D Q and get the original data and the feature code after encryption: decode Q K( , D)→data+C F

Step 3: Decrypt C by the public key ( F PK ) of transmitter If it can be c

done, the data has been proved sending by the correct transmitter, otherwise, the received data may not be sent from the pronounced transmitter

Step 4: User may take the same irreversible encryption method to calculate

the feature code of received data, and compare it with C which is got from F

step 2 If they are identical to each other, the received data has not been distorted during transmission; if not, there might have some changes in the received data

5 Conclusion

The paper analyzed the necessity and advantages to provide mobile internet services based on cloud computing technology, pointed out the security risks of LBS system brought by mobile cloud computing, emphasized that the confidentiality of outsourced data and the integrity of query results are the key points to ensure location privacy for the cloud-based LBS system Finally, the paper proposed a framework for LBS system based on mobile cloud computing and described the realization process of cloud-based LBS business

Acknowledgment

This work is supported by the Natural Science Foundation of Gansu Province (1310RJYA004), and HongLiu Programme of Lanzhou University of Technology

References

1 Yin Jiwang Development Status and Trend of LBS in the Era of Mobile

Internet China Interne, 2013(6): 9-12

2. Jiehui Ju, Jiyi Wu, Jianqing Fu, et al A survey on cloud storage Journal of Computers, 2011, 6(8): 1764-1771

3 Man Lung Yiu, Christian S Jensen, Xuegang Huang, Hua Lu SpaceTwist: managing the trade-offs among location privacy, query performance, and

query accuracy in mobile services ICDE, 2008, 366-375

4 Gruteser M, Grunwald D Anonymous usage of location-based services

through spatial and temporal cloaking Proceedings of the 1st international conference on Mobile systems, applications and services, 2003, 31-42

5 To Quoc Cuong, Dang Tran Khanh, Küng Josef A Hilbert-based

framework for preserving privacy in location-based services International Journal of Intelligent Information and Database Systems, 2013, 7(2):

113-134

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Research and Exploration of the Hierarchical Management of

c

zhoutianyue@smmu.edu.cn, d caisongliang@smmu.edu.cn

Nowadays, campus network is in the face of the difficulties in the management and control of the core information such as scientific research patents or important software codes, which would easily lead to leakage of the core information, causing incalculable loss to the colleges and universities This article analyzes the necessity of the hierarchical campus network management, introduces new technologies of the hierarchical network management, proposes a building program of the campus hierarchical network management based on the network topological structure of the Internet and campus network connection, dividing the campus network into two logical subnets, achieving the centralized control of the core electronic information, provides a guideline and reference

to solving the contradictions which the campus network is facing with in an effective way

Keywords: Campus; Network; Hierarchical Management

1 Introduction

As an internal local Area Network (LAN) affiliated with the universities, the campus network is connected to the Internet, and has developed into the core network and the main support to the university information system However, besides the fruitful accomplishment, the campus network construction is also facing with the difficulties in controlling core information such as scientific research patents and important software codes, which easily leads to the leakage

of the core information, causing incalculable loss to the colleges and universities Hierarchical network management, as the development trend of network management, is the effective way to solve the difficulties in controlling the core information of the campus network

2 The New Technology in Hierarchical Network Management

2.1 Sandbox technology

Sandbox is an environment that provides the testing environment for the programs which are from untrusted sources or with destructive power or be unable to determine its intent The programs will be able to run freely within sandbox and once been proved to be viruses or malicious programs, the system will note the features of the programs and take the rollback operation That is to say, the actual system will not be harmed or threatened no matter how the viruses or the malicious programs may run 1 Sandbox technology is widely used

in the field of information security technology in recent years For example, it is used in Google Chrome browser, Microsoft Office 2013 applications in order to enhance their safety 2

The features of the sandbox technology is that an isolated environment is provided for the program under test When a suspicious behavior was detected, the program still keeps running in the sandbox and no rollback operation will be taken until the program is confirmed to be a virus or a malicious program, which ensures that the program does not affect the outside system environment To extend this theory to the upper application, it offers a new direction of the network security development That is to apply the sandbox technology, isolating the unsafe behavior which may do harm to the holistic internal network and reducing the security risk to the minimum level 3 With the sandbox technology, one or more sandboxes will be created in the computers in the network and two or more isolated environments will be formed, providing technical support for hierarchical network management

2.2 Cloud storage technology

Cloud storage is a brand new concept extended and developed from the concept

of Cloud Computing With distributed file system, clustered application technologies, network technologies, and other technologies, various storage devices in the network are integrated to work cooperatively to provide the public with storage and access business It is one of today's mainstream network storage technologies, representing the emerging clustered storage technology 4

The principal orientation of application of the cloud storage technology is represented by network disk, storage space rental services, remote backup and disaster recovery Its main advantages are 5:

(1).Distribution According to Needs & Easy to Expand The cloud storage systems can be freely expanded to the required storage space depending on the demand, and improve storage efficiency

(2).Running Efficiently & Reliable Service Using the advanced hard disk and data management along with a variety of optimization technologies, efficient I/O services are provided Setting backup storage devices ensures the users will not be affected by the special circumstances such as the maintenance, upgrades and malfunction of the original devices

(3).Transparent Underlying & Simple to Use During the usage of the cloud storage, users will not need to understand how it is provided or its underlying infrastructure Cloud storage is like a remote data center Users are able to get access to the remote resources, read and store data with internet browser or other clients 6

3 The Construction of the Hierarchical Campus Network

Management

This article is based on the status quo of the network topology that the internet and the campus network are connected together, adopting sandbox technology and cloud storage technology to combine to configure the network access control policy, logic partitioning the campus network into office subnet and public subnet, in order to realize the centralized control of the core electronic information Program are shown in figure 1

Through the development of technology-based sandbox client (hereinafter referred to as: Sandbox client), the proposed program mainly divide the campus network computers into mutually isolated office environment and public environment With the cloud storage technology, the campus network computers

in the office environment are ensured that the files and information could not get access to stored in the local storage, while all the processed files and information can only be stored in the cloud sever instead of being stored locally, so as to realize core electronic information centralized control

Meanwhile, by configuring the client, network access control policies such

as mandatory access authentication, environmental security isolation and disjoint server IP address are adopted in combination, ensure that the office environments of all campus network computers are interconnected to form the office subnet and the public environments of all campus network computers are interconnected to form the public subnet Moreover, a logical insulation lies between the office subnet and the public subnet to prevent any possible exchange visits, in order to logic partition the campus network into office subnet and public subnet Logical insulated from the internet, the office subnet is equivalent to the unit LAN, which is mainly for office use to store and process

core data The public subnet is interconnected to the Internet, mainly used for storage and processing the internal information and resources of the unit

Fig 1 Diagram of hierarchical network management

3.1 Mandatory access authentication

Based on the campus network access authentication software such as Rui Jie Client, by monitoring the process of the local computer startup, the program forces the campus network users to install the sandbox client Otherwise the users will not get access to the campus network Meanwhile, a layer of authentication will be added between the campus network and the Internet With the sandbox client, it is enforced that internet access authentication is not allowed under the office environment and is allowed to get access only under the public environment Thereby it is ensured that the internet is accessible only under public environment rather than the office environment

3.2 Environmental safety isolation

Based on the security encryption technology, the program realizes the encryption transmission of the network data under the office environment It ensures that the network data packets of the office environment cannot be read correctly under public environment and vice versa, in order to achieve the security isolation between the office environment and the public environment That is to say, the computer under two environments cannot get access to each other

3.3 Disjoint server IP address

Based on the importance level of the applications running on the campus network server, the server is divided into two different categories: core server

and non-core server Different types of server IP lie in different IP addresses By setting the server IP address blacklists, one accessible sever under either environments doesn’t intersect with the other, namely: preventing computers from different environments exchange data through the network server Meanwhile, isolating different types of server with the firewall makes sure that servers of different categories are unable to visit one another

4 Construction Effects of Campus Network Hierarchical

Management

4.1 Building unified internal shared network platforms

Building a public subnet covering all persons in the unit provides the university with a relatively free information technology environment to support a full-featured, concept-advanced, resource-rich information platform; provides the users with an open and efficient resource-sharing environment, facilitating the users’ work, study and life

4.2 Building secure core office network platform

The office subnet provides the university with a network environment where office business is stably operated and the core information is safely controlled,

in order to support various types of core applications and information resources, providing users with a tightly-guarded and transparently-detailed core environment to ensure the security of core information

4.3 Achieving the centralized control of the core electronic information

Under the office environment, neither individuals nor terminals are able to retain the core electronic information That is to achieve the centralized control of the core electronic information The core electronic information is kept under control since it is generated, which greatly improves the level of campus information security

4.4 Realizing multiplexing share of the campus network computers

Logic partitioning the campus network into office subnet and public subnet, maintains the topology of the underlying network and the amount of the campus network computers The program fully reuse the existed campus network computers, dividing the computers into the isolated public environment and office environment, connecting the pubic subnet and the office subnet

separately, not only avoiding the re-construction of the hardware resources, but also corresponding to the energy conservation and emission reduction, and reducing the trouble of too many computers on the user’s office desk

5 Conclusions

This article analyzes the necessity of the campus hierarchical network management, introduces new technologies of the hierarchical network management, proposes a building program of the campus hierarchical network management based on the network topological structure of the Internet and campus network connection, dividing the campus network into two logical subnets, achieving the centralized control of the core electronic information, provides a guideline and reference to solving the contradictions which the campus network is facing with in an effective way

References

1 Wang Yang, Wang Qin Research on Sandbox Security Technology [J]

Software Guide.2013, 8:152-153

2 Chen Dan-wei, Tang Ping, Zhou Shu-tao Malware Detection Model Based

on the Sandbox [J] Computer Science 2012, 6:12-14

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4 Liang JUN, Xie Wen-jun Research of Cloud Storage Technology [J]

China CIO News 2013, 9:76-86

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Performance Evaluation of Cloud Storage [J] Computer Science 2014,

4(41):190-194

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of cloud storage applications [J] Internet of Things Technologies 2014,

2:69-71

Improvement of Localization Algorithm for Wireless Sensor

Network in Environmental Monitoring

Chun Liua, Shiling Wangb*, Ying Ma, Zhiqiang Zhai

School of Electrical Engineering and Automation, Hefei University of Technology,

HeFei 230009, China E-mail: a hfliuchun@126.com, b wangsl709@163.com

Nodes localization problem now has became the core of the problems in agricultural wireless sensor network In this paper, according to the characteristics of agricultural application, an improved algorithm is proposed based on DV-Hop algorithm and some existed improved algorithms The algorithm joins a new anchor nodes selection strategy

in the localization stage and locates nodes by the idea of weighted centroid The simulation results show that the new algorithm improves the localization accuracy and has lower energy cost and better stability

Keywords: Wireless Sensor Network; DV-Hop; Localization; Anchor Nodes Selection;

Centroid

1 Introduction

Precision agriculture is the new development trend of agriculture[1] Wireless sensor network (WSN) combining with agriculture can precisely monitor environment and reduce manual labor and impact on agricultural environment Location information is greatly important to WSN monitoring activities As lager number of network nodes, constraint energy and hardware, etc, how to realize location of low cost and high accuracy is one of the key issues about agricultural WSN

The WSN localization algorithms can be divided into two categories: range-based and range-free [2] Range-free algorithms attract much attention for its low cost DV-Hop[3] is currently one of the most widely used range-free algorithms, with merits of easy realization, low requirements for hardware, etc But, it has a big error when nodes are distributed in the randomly network environment [4] What’s more, its communication cost will be large with the increasing node density

Liu et al.[4] proposed an improved DV-Hop algorithm based on weighted hop-size for WSN, improving the localization accuracy, but increasing

*

Corresponding author.

computational complexity In [5], to reduce communication cost, anchor nodes only flooded their coordinates The unknown node calculated hop-size itself and located by a new multilateral localization algorithm, improving localization accuracy and reducing communication cost, but increasing computational cost

In [6], a threshold was introduced for collinearity degree, balancing the localization accuracy and computational complexity by reducing the number of anchor nodes in localization

In this paper, given localization accuracy, communication cost and computational complexity, an improved DV-Hop algorithm (IDV-Hop) is proposed based on DV-Hop and the reference [5]

2 The Improved DV-Hop Localization Algorithm

The nodes minimum hop count calculation method of IDV-Hop is similar to DV-Hop

2.1. Average hop-size calculation

In DV-Hop algorithm, anchor nodes broadcast their coordinates and the average hop-size, increasing communication cost Based on [5], anchor nodes only broadcast coordinates and unknown nodes calculate anchor nodes hop-size Consider that the more the hop count is, the greater error of using curve instead

of linear distance is, and it will increase calculation amount and error to calculate the average hop-size HopSizei of anchor node i by all anchor nodes received, thus, different from [5], unknown nodes calculate HopSizei only with anchor nodes whose hops less than 5, as shown in Eq.1:

2.2. Unknown node localization

In the localization stage of DV-Hop, multilateral localization algorithm needs so much floating point arithmetic that its energy cost from computation can not be ignored In [6], it is pointed out that anchor nodes are collinear or approaching collinear, small range error will cause lager localization error Aimed at the above problems, an anchor node selection strategy is introduced to select non-collinear anchor nodes and nodes are located by weighted centroid[7]

2.3. Anchor nodes selection

In [8], it judges whether nodes are collinear by calculating collinearity degree, causing high computational complexity This paper adopts a new way to judge collinearity In Fig.1, d1, d2 and d3 are expressed the distance between anchor nodes, and d1≤d2≤d3.If d3=d1+d2, the three nodes are collinear If 2×d3=d1+d2, the three nodes form an equilateral triangle and its localization error is minimum

So, when d1+d2 is approaching to d3, nodes are tending to collinear, we must eliminate these collinear or similar collinear nodes while locating This paper selects the optimal anchor nodes, satisfying d1+d2 is greater than or equal to 1.1 times of d3

When locating by centroid algorithm, it will greatly improve the localization accuracy and reliability to make unknown node constrained in the triangle made of anchor nodes[9] Learning from [9], when the distance between unknown node and the farthest anchor node from unknown node is less than the longest side of anchor nodes triangle, unknown node is in the triangle As shown

in Fig.2, in the triangle △ABC made of anchor nodes A, B, and C, dAC≤dBC≤dAB,

if dUA≤dUC≤dUB and dUB<dAB, then unknown node U is in the △ABC and A, B, and C are the optimal anchor nodes

Fig.1 Distribution of anchor nodes Fig.2 Distribution of anchor nodes and unknown node

2.4. Centroid localization

Selecting the first five anchor nodes which are nearest to the unknown node, and choosing the optimal anchor nodes according to the optimal anchor nodes strategy, meanwhile, located by weighted centroid algorithm Considering that anchor nodes with different distances have different effects on unknown node when locating, i.e, the smaller the distance is, the greater the weight is In this paper, the weight ϖ is calculated by Eq.2, reflecting the relative influence of different anchor nodes, (x,y) and (xi,yi) are coordinates of unknown node and the optimal anchor nodes, hopsi is hop-count between unknown node and anchor node,(x,y) can be calculated by Eq.3

is set to 30m.The average value of 10 experiments are used for comparison The average localization error and standard deviation which may reflect algorithm stability[10] are used as the evaluation criteria of localization accuracy

3.1. Localization accuracy analysis

We compare localization accuracy of three kinds of algorithms in two cases of different node number and different percentage of anchor nodes

Case 1: The total number of nodes increases from 100 to 400, the interval is

50, the percentage of anchor nodes is 10%

From Fig.3 and Fig.4,with the increasing number of nodes, the localization error of all algorithms tend to decrease, and the standard deviation of IDV-Hop

is less than DV-Hop and PERLA, showing a better localization stability As is shown in Fig 3, the localization error of IDV-Hop is less than DV-Hop, but slightly larger than PERLA This is because in the randomly distributed network, different distance of per hop brings about error in using hop-size in place of distance, increasing weight and localization error However, IDV-Hop localization error is less than 0.4R, meeting the most application requirements of WSN[11]

Fig.3 Localization error

Fig.4 Standard deviation of localization error

Fig.5 Localization error

Fig.6 Standard deviation of localization error

Case 2: The total number of nodes is 100, the percentage of anchor nodes increases from 10% to 40%, interval is 5%

Fig 5 and Fig 6 show that the localization error of all algorithms has a downward trend with the increasing proportion of anchor nodes and the standard

deviation of IDV-Hop is less than the other two algorithms The localization error of IDV-Hop is less than DV-Hop, larger than PERLA, but also less than

0.4R, satisfying the application requirements

3.2. Energy cost analysis

In this paper, we compare 3 algorithms energy cost by communication and computational cost Table 1 is comparison of energy cost for 3 kinds of localization algorithms N is the number of unknown nodes in network, A is number of anchor nodes, k is the number of anchor nodes involved once multilateral localization, n is the number of anchor nodes calculating the average hop-size, n is much less than k, F is a single flop energy cost Table 1 shows that IDV-Hop has less computational cost than PERLA and less communication cost than DV-Hop Thus, its total energy cost is less than the other two algorithms

Table 1 Comparison of energy cost for 3 kinds of localization algorithms

Acknowledgment

The work is supported by Anhui Science and Technology Key Project [1201a0301008]

References

1 A Rehman, A.Z Abbasi, N Islam, et al, A review of wireless sensors and

networks' applications in agriculture Computer Standards & Interfaces, Vol

36 (2014), No.2, p 263-270

2 Y Peng, D Wang, A review: wireless sensor networks localization, J

Journal of Electronic Measurement and Instrument Vol 25 (201l), No.5,

p 389-399