Sensors 2010, 10(9), 8663-8682; doi:10.3390/s100908663
Article
Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment
Woojin Lee, Juil Kim and JangMook Kang*
SCI급 저널 (컴퓨터 및 네트워크 분야)
http://www.mdpi.com/1424-8220/10/9/8663
Secret key extraction from wireless signal strength in real environmentsMuthu Sybian
Sybian Technologies is a leading IT services provider & custom software development company. We offer full cycle custom software development services, from product idea, offshore software development to outsourcing support & enhancement. Sybian employs a knowledgeable group of software developers coming from different backgrounds. We are able to balance product development efforts & project duration to your business needs.
Sybian Technologies invests extensively in R&D to invent new solutions for ever changing needs of your businesses, to make it future-proof, sustainable and consistent. We work in close collaboration with academic institutions and research labs across the world to design, implement and support latest IT based solutions that are futuristic, progressive and affordable. Our services continue to earn trust and loyalty from its clients through its commitment to the following parameters
Final Year Projects & Real Time live Projects
JAVA(All Domains)
DOTNET(All Domains)
ANDROID
EMBEDDED
VLSI
MATLAB
Project Support
Abstract, Diagrams, Review Details, Relevant Materials, Presentation,
Supporting Documents, Software E-Books,
Software Development Standards & Procedure
E-Book, Theory Classes, Lab Working Programs, Project Design & Implementation
24/7 lab session
Final Year Projects For BE,ME,B.Sc,M.Sc,B.Tech,BCA,MCA
PROJECT DOMAIN:
Cloud Computing
Networking
Network Security
PARALLEL AND DISTRIBUTED SYSTEM
Data Mining
Mobile Computing
Service Computing
Software Engineering
Image Processing
Bio Medical / Medical Imaging
Contact Details:
Sybian Technologies Pvt Ltd,
No,33/10 Meenakshi Sundaram Building,
Sivaji Street,
(Near T.nagar Bus Terminus)
T.Nagar,
Chennai-600 017
Ph:044 42070551
Mobile No:9790877889,9003254624,7708845605
Mail Id:sybianprojects@gmail.com,sunbeamvijay@yahoo.com
Revealing AES Encryption Device Key on 328P Microcontrollers with Differentia...IJECEIAES
This research demonstrates the revealing of an advanced encryption standard (AES) encryption device key. The encryption device is applied to an ATMEGA328P microcontroller. The said microcontroller is a device commonly used in the internet of things (IoT). We measured power consumption when the encryption process is taking place. The message sent to the encryption device is randomly generated, but the key used has a fixed value. The novelty of this research is the creation of a systematic and optimal circuit in carrying the differential power analysis or difference of means (DPA/DoM) technique, so the technique can be applied in key revealing on a microcontroller device by using 500 traces in 120 seconds.
WSN performance based on node placement by genetic algorithm at smart home en...TELKOMNIKA JOURNAL
Wireless sensor connectivity is one of several factors that determines the communication reliability of each node. The placement of the node depends on the area that covered by wireless coverage area, so the node placement should be optimally placed. But the other aspect is the sensor coverage area. Sensor coverage area sometimes could be different with wireless sensor coverage area. Based on that situation, it needs to optimize that situation. Genetic Algorithm is an algorithm that utilizes a heuristic approach that uses biological mechanism evolution. It used to evolution the best position of Sensor Node based on Wireless and Sensor coverage area. After the position of each node generated by Genetic Algorithm, it still needs to evaluate the wireless sensor node performance. The performance indicates that the genetic algorithm can be used to determine sensor node placement in the smart home environment. The smart home environment used to monitor event at the house such as wildfire. In this research used Quality of Services (QoS) to measure wireless sensor performance. The experimental testing scenario will be used to place several nodes that generated. The QoS performed systems reliability that produced based on 3, 4 and 5 testing nodes, the minimum and maximum of each: delay is 6.21 and 8.74 milliseconds, jitter is 0.11 and 1.59 Hz and throughput is 68.83 and 90.49 bps. Based on ETSI classification, the performance of sensor node placement is Good and acceptable in real-time systems.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Case Study 2: Instrumentation SoftwareJunaid Lodhi
Instrumentation Software architecture by at Tektronix.
This work was carried out as a collaborative
effort between several Tektronix product divisions and the Computer
Research Laboratory over a three year period Since then the framework has been
extended and adapted to accommodate a broader class of system, while at the
same time being better adapted to the specific needs of instrumentation
software.
Power analysis attack against encryption devices: a comprehensive analysis of...TELKOMNIKA JOURNAL
Cryptography is a science of creating a secret message and it is constantly developed. The development consists of attacking and defending the cryptography itself. Power analysis is one of many Side-Channel Analysis (SCA) attack techniques. Power analysis is an attacking technique that uses the information of a cryptographic hardware’s power consumption. Power analysis is carried on by utilizing side-channel information to a vulnerability in a cryptographic algorithm. Power analysis also uses a mathematical model to recover the secret key of the cryptographic device. This research uses design research methodology as a research framework started from research clarification to descriptive study. In this research, power analysis attack is implemented to three symmetrical cryptographic algorithms: DES (Data Encryption Standard), AES (Advanced Encryption Standard), and BC3 (Block Cipher 3). The attack has successfully recovered 100% of AES secret key by using 500 traces and 75% DES secret key by using 320 traces. The research concludes that the power analysis attack using Pearson Correlation Coefficient (PCC) method produces more optimal result compared to a difference of means method.
Secret key extraction from wireless signal strength in real environmentsMuthu Sybian
Sybian Technologies is a leading IT services provider & custom software development company. We offer full cycle custom software development services, from product idea, offshore software development to outsourcing support & enhancement. Sybian employs a knowledgeable group of software developers coming from different backgrounds. We are able to balance product development efforts & project duration to your business needs.
Sybian Technologies invests extensively in R&D to invent new solutions for ever changing needs of your businesses, to make it future-proof, sustainable and consistent. We work in close collaboration with academic institutions and research labs across the world to design, implement and support latest IT based solutions that are futuristic, progressive and affordable. Our services continue to earn trust and loyalty from its clients through its commitment to the following parameters
Final Year Projects & Real Time live Projects
JAVA(All Domains)
DOTNET(All Domains)
ANDROID
EMBEDDED
VLSI
MATLAB
Project Support
Abstract, Diagrams, Review Details, Relevant Materials, Presentation,
Supporting Documents, Software E-Books,
Software Development Standards & Procedure
E-Book, Theory Classes, Lab Working Programs, Project Design & Implementation
24/7 lab session
Final Year Projects For BE,ME,B.Sc,M.Sc,B.Tech,BCA,MCA
PROJECT DOMAIN:
Cloud Computing
Networking
Network Security
PARALLEL AND DISTRIBUTED SYSTEM
Data Mining
Mobile Computing
Service Computing
Software Engineering
Image Processing
Bio Medical / Medical Imaging
Contact Details:
Sybian Technologies Pvt Ltd,
No,33/10 Meenakshi Sundaram Building,
Sivaji Street,
(Near T.nagar Bus Terminus)
T.Nagar,
Chennai-600 017
Ph:044 42070551
Mobile No:9790877889,9003254624,7708845605
Mail Id:sybianprojects@gmail.com,sunbeamvijay@yahoo.com
Revealing AES Encryption Device Key on 328P Microcontrollers with Differentia...IJECEIAES
This research demonstrates the revealing of an advanced encryption standard (AES) encryption device key. The encryption device is applied to an ATMEGA328P microcontroller. The said microcontroller is a device commonly used in the internet of things (IoT). We measured power consumption when the encryption process is taking place. The message sent to the encryption device is randomly generated, but the key used has a fixed value. The novelty of this research is the creation of a systematic and optimal circuit in carrying the differential power analysis or difference of means (DPA/DoM) technique, so the technique can be applied in key revealing on a microcontroller device by using 500 traces in 120 seconds.
WSN performance based on node placement by genetic algorithm at smart home en...TELKOMNIKA JOURNAL
Wireless sensor connectivity is one of several factors that determines the communication reliability of each node. The placement of the node depends on the area that covered by wireless coverage area, so the node placement should be optimally placed. But the other aspect is the sensor coverage area. Sensor coverage area sometimes could be different with wireless sensor coverage area. Based on that situation, it needs to optimize that situation. Genetic Algorithm is an algorithm that utilizes a heuristic approach that uses biological mechanism evolution. It used to evolution the best position of Sensor Node based on Wireless and Sensor coverage area. After the position of each node generated by Genetic Algorithm, it still needs to evaluate the wireless sensor node performance. The performance indicates that the genetic algorithm can be used to determine sensor node placement in the smart home environment. The smart home environment used to monitor event at the house such as wildfire. In this research used Quality of Services (QoS) to measure wireless sensor performance. The experimental testing scenario will be used to place several nodes that generated. The QoS performed systems reliability that produced based on 3, 4 and 5 testing nodes, the minimum and maximum of each: delay is 6.21 and 8.74 milliseconds, jitter is 0.11 and 1.59 Hz and throughput is 68.83 and 90.49 bps. Based on ETSI classification, the performance of sensor node placement is Good and acceptable in real-time systems.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Case Study 2: Instrumentation SoftwareJunaid Lodhi
Instrumentation Software architecture by at Tektronix.
This work was carried out as a collaborative
effort between several Tektronix product divisions and the Computer
Research Laboratory over a three year period Since then the framework has been
extended and adapted to accommodate a broader class of system, while at the
same time being better adapted to the specific needs of instrumentation
software.
Power analysis attack against encryption devices: a comprehensive analysis of...TELKOMNIKA JOURNAL
Cryptography is a science of creating a secret message and it is constantly developed. The development consists of attacking and defending the cryptography itself. Power analysis is one of many Side-Channel Analysis (SCA) attack techniques. Power analysis is an attacking technique that uses the information of a cryptographic hardware’s power consumption. Power analysis is carried on by utilizing side-channel information to a vulnerability in a cryptographic algorithm. Power analysis also uses a mathematical model to recover the secret key of the cryptographic device. This research uses design research methodology as a research framework started from research clarification to descriptive study. In this research, power analysis attack is implemented to three symmetrical cryptographic algorithms: DES (Data Encryption Standard), AES (Advanced Encryption Standard), and BC3 (Block Cipher 3). The attack has successfully recovered 100% of AES secret key by using 500 traces and 75% DES secret key by using 320 traces. The research concludes that the power analysis attack using Pearson Correlation Coefficient (PCC) method produces more optimal result compared to a difference of means method.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
지혜 구성 방법 및 기술 프로젝트 수행의 일환으로 진행된 워크샵입니다. 그 중 데이터 마이닝 기술과 언어 처리 기술을 이용하여 교육정보의 데이터마이닝을 연구하고 있습니다.
연구실에 여러 석.박사 학생들과 졸업생 이하 여러 교수들과 협업하는 지혜 구성 마켓을 구축하고 있습니다.
소셜 네트워크 서비스 동향과 구현
강장묵
본 특강은 스마트 폰에서 구현되는 다양한 어플리케이션을 소개하고
이를 통해 구체적으로 실현되는 소셜 네트워크 서비스가 어떻게 진화할 것인지를
소개합니다.
예를 들면, 위치기반의 고왈라, 포스퀘어가 어떻게 소셜한 관계로 확장하는지를
사례로 소개합니다. 더불어 이미지 중심의 플리커가 참여, 공유, 개방을 통한
소셜 관계를 지향하는 서비스가 된 것도 설명합니다.
또한 페이스북을 통해 프로필기반의 서비스와 방송통신의 융합 서비스인 트위터에
대한 소개를 통해 소셜 네트워크 서비스를 구체적으로 체험해보도록 할 것입니다.
본 특강을 통해 소셜 네트워크의 다양한 사례와 구체적인 구현 예를 보고
정보통신 전공의 3-4학년 학생들이, 여러분이 앞으로 배울 교과목을 응용하여 어떤 기술 개발을 고민해볼 것인지를
함께 고려하도록 하겠습니다.
본 자료는 비공개 회의를 준비하는 과정에서 드래프트로 작성한 정보일 뿐, 특정 기관과 개인의 확정된 의견은 아님을 밝힙니다. 다만, 현재 부족한 정보 상황에서 기술적 보안 이슈와 보안 정책 상의 문제 등에 대해 이슈와 해결방안 등을 검토하는 과정에 글로 보시면 됩니다. 이 글을 작성한 저자 역시 사고의 좌표가 완결된 것이 아니므로 공유를 통해 더 많은 전문가들의 의견을 제공받고 싶습니다.
본 파일은 수정된 버젼 2.0 임
웹의 [자발성과 다양성] 대 [책임성과 신뢰성]을 부여할 수 있는 '기술과 정책'의 참여 모델에 관한 발표JM code group
2009년 06월 23일 오후 4시 KISDI자문회의 주제발표인
웹공간의 신뢰성 향상을 위한 기술적 방법과 정책적 모델을 하나의 그림으로 깔끔히 그려서 소셜한 공간 형성을 위한 기술적 과제와 정책적 하모나이즈를 위한 제안에서 최종 깔끔한 정리를 게으름으로 빠트리고 설명해가는 그 과정을 PT로 작성함
네트워크의 정치학을 위한 이론적 모색을 주제로 한국국제정치학회 2009년 학술대회에 발표한 자료
장소 한양대학교
일시 2009년 12월 12일
토론자 윤성이 황종성 강원택 류석진 외
후원 네이버
네트워크로 새롭게 형성되는 정치현상을 추수려 정치이론으로 재구성하는 구성요소와 네트워크의 원리 등에 대한 시론적 발표
본 자료는 강장묵 뉴미디어와 소통의 정치학 한울 2009년 출간된 책을 근간으로 재구성함
A Trust Service and System based on Social NetworkJM code group
소셜 미디어를 활용한 소셜 네트워크의 상호작용이 콘텐츠의 신뢰성과 웹의 건전성을 향상시키는 기술
그리고 기술과 사회 구성 간의 구조적 배치를 고려한 소셜 네트워크 서비스 설계 및 아키텍쳐에 도움을 주고자 한다.
본 자료는 한국정보과학회 정보통신소사이어티 주관의 단기 강좌 프로그램 중
Session3 : networking for service(1)에 초청 강사인 강장묵(세종대) 교수의 강연 자료임.
저작권자를 표시하는 조건으로 공정사용을 허용함. 2009년 11월 11일 수요일 강좌가 진행되었음.
Email : redsea@sejong.ac.kr mooknc@gmail.com
Blog : http://blog.ohmynews.com/UCnam/category/10205
일 시 :2009년 11월 11일 수요일
진 행 : 15:50-16:20 발표, 16:20-16:30질의 응답
본 강연을 통해 소셜한 서비스와 네트워크가 웹의 신뢰를 향상시킬 수 있는 기술적 사례와 서비스 구현을 위한 아키텍처 설계에 대한 심층적 이해를 돕고자 한다.
2010년 5월 디지털 혁명의 빛과 그늘을 주제로 르네21의 금요대중강좌에서 발표한 자료입니다.
뉴미디어와 소통의 정치학(2009)의 저자 강장묵의 강의 교안입니다.
책에 있는 이미지와 간략한 설명 그리고 새롭게 추가한 사진 동영상 등이 담겨있습니다.
출처를 밝히고 자유로운 이용을 권장합니다.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
지혜 구성 방법 및 기술 프로젝트 수행의 일환으로 진행된 워크샵입니다. 그 중 데이터 마이닝 기술과 언어 처리 기술을 이용하여 교육정보의 데이터마이닝을 연구하고 있습니다.
연구실에 여러 석.박사 학생들과 졸업생 이하 여러 교수들과 협업하는 지혜 구성 마켓을 구축하고 있습니다.
소셜 네트워크 서비스 동향과 구현
강장묵
본 특강은 스마트 폰에서 구현되는 다양한 어플리케이션을 소개하고
이를 통해 구체적으로 실현되는 소셜 네트워크 서비스가 어떻게 진화할 것인지를
소개합니다.
예를 들면, 위치기반의 고왈라, 포스퀘어가 어떻게 소셜한 관계로 확장하는지를
사례로 소개합니다. 더불어 이미지 중심의 플리커가 참여, 공유, 개방을 통한
소셜 관계를 지향하는 서비스가 된 것도 설명합니다.
또한 페이스북을 통해 프로필기반의 서비스와 방송통신의 융합 서비스인 트위터에
대한 소개를 통해 소셜 네트워크 서비스를 구체적으로 체험해보도록 할 것입니다.
본 특강을 통해 소셜 네트워크의 다양한 사례와 구체적인 구현 예를 보고
정보통신 전공의 3-4학년 학생들이, 여러분이 앞으로 배울 교과목을 응용하여 어떤 기술 개발을 고민해볼 것인지를
함께 고려하도록 하겠습니다.
본 자료는 비공개 회의를 준비하는 과정에서 드래프트로 작성한 정보일 뿐, 특정 기관과 개인의 확정된 의견은 아님을 밝힙니다. 다만, 현재 부족한 정보 상황에서 기술적 보안 이슈와 보안 정책 상의 문제 등에 대해 이슈와 해결방안 등을 검토하는 과정에 글로 보시면 됩니다. 이 글을 작성한 저자 역시 사고의 좌표가 완결된 것이 아니므로 공유를 통해 더 많은 전문가들의 의견을 제공받고 싶습니다.
본 파일은 수정된 버젼 2.0 임
웹의 [자발성과 다양성] 대 [책임성과 신뢰성]을 부여할 수 있는 '기술과 정책'의 참여 모델에 관한 발표JM code group
2009년 06월 23일 오후 4시 KISDI자문회의 주제발표인
웹공간의 신뢰성 향상을 위한 기술적 방법과 정책적 모델을 하나의 그림으로 깔끔히 그려서 소셜한 공간 형성을 위한 기술적 과제와 정책적 하모나이즈를 위한 제안에서 최종 깔끔한 정리를 게으름으로 빠트리고 설명해가는 그 과정을 PT로 작성함
네트워크의 정치학을 위한 이론적 모색을 주제로 한국국제정치학회 2009년 학술대회에 발표한 자료
장소 한양대학교
일시 2009년 12월 12일
토론자 윤성이 황종성 강원택 류석진 외
후원 네이버
네트워크로 새롭게 형성되는 정치현상을 추수려 정치이론으로 재구성하는 구성요소와 네트워크의 원리 등에 대한 시론적 발표
본 자료는 강장묵 뉴미디어와 소통의 정치학 한울 2009년 출간된 책을 근간으로 재구성함
A Trust Service and System based on Social NetworkJM code group
소셜 미디어를 활용한 소셜 네트워크의 상호작용이 콘텐츠의 신뢰성과 웹의 건전성을 향상시키는 기술
그리고 기술과 사회 구성 간의 구조적 배치를 고려한 소셜 네트워크 서비스 설계 및 아키텍쳐에 도움을 주고자 한다.
본 자료는 한국정보과학회 정보통신소사이어티 주관의 단기 강좌 프로그램 중
Session3 : networking for service(1)에 초청 강사인 강장묵(세종대) 교수의 강연 자료임.
저작권자를 표시하는 조건으로 공정사용을 허용함. 2009년 11월 11일 수요일 강좌가 진행되었음.
Email : redsea@sejong.ac.kr mooknc@gmail.com
Blog : http://blog.ohmynews.com/UCnam/category/10205
일 시 :2009년 11월 11일 수요일
진 행 : 15:50-16:20 발표, 16:20-16:30질의 응답
본 강연을 통해 소셜한 서비스와 네트워크가 웹의 신뢰를 향상시킬 수 있는 기술적 사례와 서비스 구현을 위한 아키텍처 설계에 대한 심층적 이해를 돕고자 한다.
2010년 5월 디지털 혁명의 빛과 그늘을 주제로 르네21의 금요대중강좌에서 발표한 자료입니다.
뉴미디어와 소통의 정치학(2009)의 저자 강장묵의 강의 교안입니다.
책에 있는 이미지와 간략한 설명 그리고 새롭게 추가한 사진 동영상 등이 담겨있습니다.
출처를 밝히고 자유로운 이용을 권장합니다.
본 교안은 2013년 정보통신정책연구원 주최의 ICT인문융합 포럼에서 발표한 자료를 재구성하였음을 밝힙니다. 유사한 내용이 슬라이드쉐어에 있으나, 모바일 미디어 환경에서 언론학적 가치를 실현하거나 포함시킬 수 있는 메타 태그는 무엇이고 어떻게 찾고 그 태그를 중심으로 어떤 소통, 참여, 매개, 전환의 메커니즘을 설계할지를 강의합니다.
The rapid growth that has taken place in Computer Vision has been instrumental in driving the advancement of Image processing techniques and drawing inferences from them. Combined with the enormous capabilities that Deep Neural networks bring to the table, computers can be efficiently trained to automate the tasks and yield accurate and robust results quickly thus optimizing the process. Technological growth has enabled us to bring such computationally intensive tasks to lighter and lower-end mobile devices thus opening up a wide range of possibilities. WebRTC-the open-source web standard enables us to send multimedia-based data from peer to peer paving the way for Real-time Communication over the Web. With this project, we aim to build on one such opportunity that can enable us to perform custom object detection through an android based application installed on our mobile phones. Therefore, our problem statement is to be able to capture real-time feeds, perform custom object detection, generate inference results, and appropriately send intruder alerts when needed. To implement this, we propose a mobile-based over-the-cloud solution that can capitalize on the enormous and encouraging features of the YOLO algorithm and incorporate the functionalities of OpenCV’s DNN module for providing us with fast and correct inferences. Coupled with a good and intuitive UI, we can ensure ease of use of our application.
The rapid growth that has taken place in Computer Vision has been instrumental in driving the advancement of Image processing techniques and drawing inferences from them. Combined with the enormous capabilities that Deep Neural networks bring to the table, computers can be efficiently trained to automate the tasks and yield accurate and robust results quickly thus optimizing the process. Technological growth has enabled us to bring such computationally intensive tasks to lighter and lower-end mobile devices thus opening up a wide range of possibilities. WebRTC-the open-source web standard enables us to send multimedia-based data from peer to peer paving the way for Real-time Communication over the Web. With this project, we aim to build on one such opportunity that can enable us to perform custom object detection through an android based application installed on our mobile phones. Therefore, our problem statement is to be able to capture real-time feeds, perform custom object detection, generate inference results, and appropriately send intruder alerts when needed. To implement this, we propose a mobile-based over-the-cloud solution that can capitalize on the enormous and encouraging features of the YOLO algorithm and incorporate the functionalities of OpenCV’s DNN module for providing us with fast and correct inferences. Coupled with a good and intuitive UI, we can ensure ease of use of our application.
A VNF modeling approach for verification purposesIJECEIAES
Network Function Virtualization (NFV) architectures are emerging to increase networks flexibility. However, this renewed scenario poses new challenges, because virtualized networks, need to be carefully verified before being actually deployed in production environments in order to preserve network coherency (e.g., absence of forwarding loops, preservation of security on network traffic, etc.). Nowadays, model checking tools, SAT solvers, and Theorem Provers are available for formal verification of such properties in virtualized networks. Unfortunately, most of those verification tools accept input descriptions written in specification languages that are difficult to use for people not experienced in formal methods. Also, in order to enable the use of formal verification tools in real scenarios, vendors of Virtual Network Functions (VNFs) should provide abstract mathematical models of their functions, coded in the specific input languages of the verification tools. This process is error-prone, time-consuming, and often outside the VNF developers’ expertise. This paper presents a framework that we designed for automatically extracting verification models starting from a Java-based representation of a given VNF. It comprises a Java library of classes to define VNFs in a more developer-friendly way, and a tool to translate VNF definitions into formal verification models of different verification tools.
Towards internet of things iots integration of wireless sensor network to clo...IJCNCJournal
Cloud computing provides great benefits for applications hosted on the Web that also have special
computational and storage requirements. This paper proposes an extensible and flexible architecture for
integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an
interoperable application layer that can be directly integrated into other application domains for remote
monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof
of concept, we have implemented a REST based Web services on an IP based low power WSN test bed,
which enables data access from anywhere. The alert feature has also been implemented to notify users via
email or tweets for monitoring data when they exceed values and events of interest.
Remote temperature and humidity monitoring system using wireless sensor networkseSAT Journals
Abstract Today’s world has become very advanced with smart appliances and devices like laptops, tablets, televisions. smart phones with different features and their usage has been enormously increasing in our day-to-day life. The technology advancement in Digital Electronics and Micro Electro Mechanical Systems. In this scenario the most important role is played by Wireless Sensor Networks and its development and usage in heterogeneous fields and several contexts. the home automation field and process control systems and health control systems widely uses wireless sensor networks. Moreover with WSN we can monitor environments and its conditions also. We are designing a protocol to monitor the environmental temperature and humidity at different conditions. The architecture is simple to construct and ease to implement and also has an advantage of low power consumption. The aim of our paper to describe and show how to create a simple protocol for environment monitoring using a wireless development kit. we are using advanced technology of crossbow motes and NESC Language Programming. Keywords: Motes, WSN, sensor, TinyOS, Nesc.
The development of embedded applications (such as Wireless Sensor Network protocols) often
requires a shift to formal specifications. To insure the reliability and the performance of the
WSNs, such protocols must be designed following some methods reducing error rate. Formal
methods (as Automata, Petri nets, algebra, logics, etc.) were largely used in the specification of
these protocols, their analysis and their verification. After that, their implementation is an
important phase to deploy, test and use those protocols in real environments. The main
objective of the current paper is to formalize the transformation from high-level specification (in
Timed Automata) to low-level implementation (in NesC language and TinyOs system) and to
automate such transformation. The proposed transformation approach defines a set of rules that
allow the passage between these two levels. We implemented our solution and we illustrated the
proposed approach on a protocol case study for the "humidity" and "temperature" sensing in
WSNs applications.
Advanced Integrated Model-Driven Development Tool for USN Applications in Per...JM code group
Advanced Integrated Model-Driven Development Tool for USN Applications in Pervasive Computing Environment
The 2009 International Conference on Future Generation Communication and Networking
Woojin Lee, Jang-Mook Kang,
Yoon-Seok Heo, Bong-Hwa Hong
Presented by kang, jang mook(sejong Univ.)-mooknc@gmail.com
redsea@sejong.ac.kr
December 10th, 2009
16:30-16:45
Technical Session 15
CAN/FGCN-KIIT #2 401A
DESIGN ISSUES ON SOFTWARE ASPECTS AND SIMULATION TOOLS FOR WIRELESS SENSOR NE...IJNSA Journal
In this paper, various existing simulation environments for general purpose and specific purpose WSNs are discussed. The features of number of different sensor network simulators and operating systems are compared. We have presented an overview of the most commonly used operating systems that can be used in different approaches to address the common problems of WSNs. For different simulation environments there are different layer, components and protocols implemented so that it is difficult to compare them. When same protocol is simulated using two different simulators still each protocol implementation differs, since their functionality is exactly not the same. Selection of simulator is purely based on the application, since each simulator has a varied range of performance depending on application.
Analysis and assessment software for multi-user collaborative cognitive radi...IJECEIAES
Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features.
세상에 정보는 많다. 인터넷만 보아도 시시각각 뉴스가 올라온다. 뉴스 중 특정 단어 중심의 데이터를 텍스트 마이닝 할 수 있다. 메르스가 창궐했다면 뉴스기사 중 '메르스-불안-염려'라는 연관키워드의 빈도를 뽑아 그 해석을 할 수 있을 것이다. 또는 세월호 사건에서 '안타까움 등'의 연관검색어를 통해 세월호는 타자에 대한 아픔이라면 메르스는 나 자신에 대한 염려가 아닐까란 분석도 가능하다.
반면, 공개되지 않는다는 것을 전제로 시작된 폐쇄형 SNS(밴드, 카카오톡 등)은 API로 그 내용을 볼 수 없지만, 그 어느때보다 지금 폐쇄형에서 소통되는 내용이 국민의 진짜 속마음일 것이다.
당분간 폐쇄형과 개방형이 공존할 것이다.
근래들어 웨어러블, 사물인터넷 등 low level에서 솟구쳐 오르는 센싱 값이 늘어날 것이다. 굳이 이 비정형데이터를 분석하는 빅데이터의 프로그래밍 방식과 비주얼라이징 툴 그리고 하둡 등 분산처리 시스템을 논하지 않더라도 분명, 이 자료들이 분석될 것이다.
그렇다면 묻는다. 이들 날것의 정보들을 학생들과 선생님들에게 그냥 확 줄 것인가? 오늘날 큐레이션, 관계 서비스, 로컬라이제이션 서비스 그리고 LBS 등으로 상업적으로 이용될 때, 분명 돈을 벌고 광고를 얻고 플랫폼이 되고자 유인하고 선별하여 개인화시키는 정보 필터링 기술이 있을 것이다. 교육분야는 어떤가? 누가 이 정보를 필터링하여 학생, 선생님, 교육 관계자에게 시의적절하게 문맥을 읽고 추론하여 제공하는가? 큰 플랫폼을 설명하지 않아도 된다. 큰 교육 시스템을 그려내지 않아도 좋다. 아주 구체적이고 무척 작고 섬세한 교육의 한 부분을 그려보자. 예를 들면, '메르스가 창궐하고 학교 정문에서 학생들의 귀에 온도계를 꽃아 일일이 확인하고 있다' ..만약 웨어러블이 일상화된다면 학생 안전과 관련된 정보가 어느 수준에서 어떤 방식으로 어떻게 왜 언제 선생님, 학부모 등에게 전달되거나 공유되거나 저장되거나 때론 짧게 저장된 후 삭제되거나 등이 필요한가? 메르스라는 사태에서 의료정보가 어떤 수준에서 교육정보로 활용될 수 있을까? 학교 안에 안전이라는 측면에서 기존의 모든 이머징 기술을 이야기 구조 식으로 구체적으로 나열하면 어떤 방법으로 학생-선생님-학부모 등이 소통하기에 제일 좋을까? 교육 시스템 중 일부 모듈(안전 부문, 학생 질병 관리, 건강 기록 등)에 정보 공유를 고려할 때 어떻게 이해당사자 간의 거버넌스를 생각해볼 수 있을까? 예를 들어 의료정보와 교육적 가치가 상충할 때, 프라이버시와 교육의 효율성
로봇, 교육에 대한 한 꼭지와 소셜, 지도, 관심지도, 힐링, 의료 정보, 관광 등의 한 꼭지로 나누어져 구성되었습니다.
본 강의는 교육정보특론의 13-14주에 해당하는 뒷부분으로서 그간 배운 소셜 네트워크와 웨어러블 그리고 빅데이터 등의 기반 기술을 연결하여 사고하고 응용하는 능력을 배우는 시간이 될 것입니다.
고려대학교 대학원 교육정보 시스템 특론 과정의 12주차 교안입니다. 주요 내용은 서두에 융합에 내포된 오류를 검토하고 융합 또는 창의적 발상의 사례를 검토합니다.
구체적인 본 강의에서는 융합의 사례로서 인문학적 가치 중 몇몇 역사, 미술 등의 가치를 메타화하는 과정에 설명하고 이를 통해 얻을 수 있는 구체적 서비스 내용까지 검토합니다.
자세한 내용은 고려대학교 Mooc 에서 추후 소개될 예정입니다.
9 주; 2015.4.29. 수
교육정보서비스에서 정형/반정형/비정형 데이터 처리는 어떤 의미를 갖는가?
(교육정보에서 핵심 가중치를 두어야 할 데이터는 무엇인가?
몇 가지 추천하고 그 이유를 논한다.)
키워드 : 교육정보, 교육 데이터 마이닝, 교육 빅데이터
위에 대한 내용으로 고려대 정보대학 컴퓨터학과의 강장묵 교수 (연구)의 정규 교과목 교안 입니다.
'공공정보의 개방과 API'가 의미하는 바와 정책적 함의가 무엇인지에 대하여, 동국대학교 최고위과정 중 '빅데이터와 공공정보'라는 주제로 강장묵 교수(고려대)의 강의 교안입니다.
특강형식을 빌었으나, 본 강의는 2015년 3월에 있었던 경찰본청의 '공공정보 공유' 등에 대한 3일 연속 강의의 내용을 재사용하였음을 밝힙니다.
인용을 달고 PPT를 활용하시기 바랍니다.
고려대학교 교육정보 전공 대학원 수업 7주차
클라우드 기술과 교육정보 특론
2015년 4월 15일 수요일 강장묵 교수 강의 교안
클라우드 기술과 서비스 전반을 사례 중심으로 설명
클라우드의 구현 사례(에어비앤비 등)를 소개
클라우드 교육 시스템 구현 사례와 관련 연구 논문 분석
고려대학교 대학원 교육정보서비스 특론 수업 4-5주 교안
고려대학교 정보대학 컴퓨터학과 강장묵 교수 강의
본 교안은 빅데이터 기술을 활용한 교육정보서비스에 대한 것임
강의는 교육정보에서 활용될 수 있는 여타 ICT 기술의 원리 중 정보공유와 API, 빅데이터 정보처리 과정과 하둡 프로그래밍의 이해, 구글 지도와 크레이그리스트의 매쉬업 사례 등을 다룸
고려대학교 정보대학 컴퓨터학과 강장묵 교수의 '교육정보서비스 특론' 2주차 강의 교안
교육정보에 대한 원리를 이해하고 교육정보 서비스를 학습
강의 내용은 국내 대학들 간의 KOCW(Korea Open Course Ware)조직인 OCW에서 영상 서비스 제공
고려대 OCW 홈페이지(http://ocw.korea.edu)등을 통해 운영
모바일 시대에 민주주의와 저널리즘 가치를 지켜낼 수 있는가? 신자본주의에 저널리즘은 조회수와 광고에 의존하여 황색으로 변색되고 있지는 않는가? 무한경쟁시대에 저널리즘은 효율성이라는 이름으로 중립적 가치보다는 시장가치, 지배자의 가치가 지배적이지 않는가?
국정원 등의 댓글 사건 이후 국내 트위터는 신뢰를 상실하였다. 그러나 한국주류언론의 현주소 역시 신뢰에 금이 가고 있는 작금에, 뉴미디어는 새로운 대안언론인가? 삐딱이들의 대안 채널일 뿐인가? 공론장은 인터넷 즉 PC alone에서 모바일로 진화하고 있다. 반면 숙의모델 등 공론이 이루어질 수 있는 최소한의 글자수, 이미지, 연결, 화면 사이즈 등 인터페이스와 글쓰기는 여전히 쉽지 않은 모바일 환경이다.
이 환경에서 비단 글쓰기를 통한 숙의만 가능한가를 묻고 그 대안적 보조적 또는 전환 기술과 메커니즘을 찾는다.
소셜 미디어의 발전이 저널리즘의 가치를 보장하는가?
소셜 미디어는 다양해진 관계망 서비스를 통해 저널리즘의 영향력을 중앙집중에서 분권화하는가?
소셜 미디어는 저널리즘을 황색저널리즘에서 품격있고 신뢰할 수 있는 저널리즘으로 변화시켰는가?
그렇지 못하였다면, 현재 대한민국의 저널리즘과 소셜 미디어의 관계와 진형은 무엇인가?
그리고 이용자들이 참여율과 보이지 않는 이용자(조직) 등의 영향력은 실제로 존재하는가?
SNA로 이 관계를 어디까지 실증적 조망이 가능한가?
저널리즘의 역사와 맥락 속에서 소셜 미디어를 고찰한다.
오픈 데이터 활용, 소셜 네트워크 서비스 분석, 소셜 네트워크 분석 등 비주얼라이징 기법을 활용하여 데이터 저널리즘 등에 대해 살펴봅니다. 그 중, 공학적 알고리즘에 대한 이해를 곁들여 언론학도들에게 데이터 마이닝에 대한 절차적 방법적 수학적 이론적 내용 역시 학습니다. 이후 원생 개개인의 텀페이퍼 주제 발표가 이어집니다.
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Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment
1. Sensors 2010, 10, 8663-8682; doi:10.3390/s100908663
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Automated Construction of Node Software Using Attributes in a
Ubiquitous Sensor Network Environment
Woojin Lee 1
, Juil Kim 2
and JangMook Kang 3,
*
1
Department of Information and Communication Engineering, Sejong University 98 Gunja-Dong,
Gwangjin-Gu, Seoul 143-747, Korea; E-Mail: woojin@sejong.ac.kr
2
R&D Center, Hunter Technology, 170-5 Guro-3Dong, Guro-Gu, Seoul 152-769, Korea;
E-Mail: sespop@empal.com
3
Electronic Commerce Research Institute, Dongguk University, 707 Seokjang-dong, Gyeongju,
Gyeongsangbuk-do, 780-714, Korea
* Author to whom correspondence should be addressed; E-Mail: mooknc@gmail.com or
redsea@dongguk.ac.kr.
Received: 30 July 2010; in revised form: 6 September 2010 / Accepted: 10 September 2010 /
Published: 17 September 2010
Abstract: In sensor networks, nodes must often operate in a demanding environment
facing restrictions such as restricted computing resources, unreliable wireless
communication and power shortages. Such factors make the development of ubiquitous
sensor network (USN) applications challenging. To help developers construct a large
amount of node software for sensor network applications easily and rapidly, this paper
proposes an approach to the automated construction of node software for USN applications
using attributes. In the proposed technique, application construction proceeds by first
developing a model for the sensor network and then designing node software by setting the
values of the predefined attributes. After that, the sensor network model and the design of
node software are verified. The final source codes of the node software are automatically
generated from the sensor network model. We illustrate the efficiency of the proposed
technique by using a gas/light monitoring application through a case study of a Gas and
Light Monitoring System based on the Nano-Qplus operating system. We evaluate the
technique using a quantitative metric—the memory size of execution code for node
software. Using the proposed approach, developers are able to easily construct sensor
network applications and rapidly generate a large number of node softwares at a time in a
ubiquitous sensor network environment.
OPEN ACCESS
2. Sensors 2010, 10 8664
Keywords: attributes; node software; ubiquitous sensor network application; automated
construction; model-based development
1. Introduction
Recent advances in wireless communications and electronics have enabled the development of
low-cost, low-power, multi-functional sensor nodes. These sensor nodes leverage the idea of sensor
networks [1]. A Ubiquitous Sensor Network [2] is a wireless network which consists of a large number
of lightweight, low-powered sensor nodes. Such sensor nodes consist of sensing, data processing and
communicating components. Sensor networks are drawing a lot of attention as a way of realizing a
ubiquitous society. They collect environmental information to realize a variety of functions through a
lot of wireless nodes that are located everywhere [1,3]. The nodes are connected to a network and
sense geographical and environmental changes of the field. Objects can recognize other objects
through the sensor network and perceive changes in the environment. Users can obtain and use
information at any time in any place through the objects connected to the sensor network. The sensor
networks can be used for various application areas such as health, military, robot, home and so on.
However, the construction of applications is challenging. Node resources in a sensor network are
limited and wireless communication between nodes is unreliable. Nodes should also perform
low-power operations. Accordingly, techniques to help developers easily construct applications, even
if they do not know the low-level information details such as low-level communication, data sharing
and collective operations, are necessary. Moreover, a sensor network consists of a large number of
sensor nodes that have various roles and a large number of node softwares for those roles must be
constructed, so such techniques should also help automatic construction of applications in order to
efficiently generate a large amount of node software.
To satisfy these requirements, this paper proposes a technique for automated construction of node
software using attributes. In the proposed technique, application construction proceeds by first
developing a model for the sensor network. Then design of software for nodes in the sensor network is
achieved by setting the values of the predefined attributes. After that, the sensor network model and
the design of node software are verified. Finally, source codes of node software are automatically
generated from the sensor network model.
In this paper, we describe in Section 2the limits of the existing techniques for USN application
development by comparing them with our approach. In Section 3 we present the USN application
development framework which is the base for the proposed technique. We describe the process and
method for constructing an application based on the proposed framework in Section 4. To make our
illustration more concrete, we describe the proposed technique using a gas/light monitoring application
as an example. In Section 5, we evaluate the proposed technique through a case study with a Gas and
Light Monitoring System based on the Nano-Qplus operating system. We evaluate the proposed
technique using a quantitative metric—the memory size of execution code for node software. Section 6
concludes the paper. The contribution of this paper is to demonstrate that the proposed technique helps
3. Sensors 2010, 10 8665
developers easily construct sensor network applications, generates a large number of node softwares at
a time and provides methods to verify a sensor network model.
2. Related Works
There are currently several techniques for generating USN applications such as Regiment [4],
Kairos [5], SNACK [6], SPIDEY [7], TinyGALS [8] and ATaG [9-11]. SNACK and TinyGALS draw
up the model at the node-level and develop the node software, while Regiment, Kairos, SPIDEY and
ATaG draw up the model at the network-level and develop an application. Techniques at the
node-level provide methods of developing single software to be implemented at each node. On the
other hand, network-level techniques provide methods of designing the model with focus on actions
among multiple nodes at the network-level and, on that basis, developing node software to be
implemented at each node. Therefore, because the network-level method generates node software for
multiple nodes comprising the sensor network on the basis of the single sensor network model, it is
more effective than the method at the node-level that individually designs and generates node software
for each node. Accordingly, this paper will present a method of drawing up the model and developing
an application at the network-level in order to develop sensor network application more effectively.
TinyGALS generates software codes from the model. Therefore, the model should be designed in
detail to include sufficient information to generate software codes. Regiment, Kairos, SNACK,
SPIDEY and ATaG provide high level languages or scripts for application design. If using these
techniques, the programs should be prepared by using the provided languages in order to generate an
application. On the other hand, the method presented in this paper designs an application by setting
values for the pre-defined attributes so that it can design the application more conveniently than
other methods.
The attributes which are mentioned in ATaG [9-11] are similar to our attributes in terms that the
attributes represent capability of a node, but they are used to determine the type of a node. In our
approach, attributes represent functional capability of the node, so developers can program node
software using the attributes. In contrast with our approach, developers must program tasks using the
provided langauge for implementing functional capability of the node in ATaG [9-11].
SNACK conducts model validation by finding errors (for example, unknown component types,
multiple declarations of the same name, missing parameters, connections that join interfaces with
different types and so forth) through static semantic checking. TinyGALS conducts model validation
by finding errors through syntax checking. On the other hand, ATaG conducts model validation in two
stages during compilation. First, it checks whether task/data names are duplicated, whether one data
item has two producers, etc. through a syntax check. Second, it makes sure that application codes will
be generated as intended by the developer by conducting task mapping according to instantiation rules.
Such model validation is to confirm that the application codes will be generated as requested by the
developer. In this paper, validation will be conducted by finding errors through syntax check during
compilation and further, for more accurate model designing, model verification will be conducted
before compilation to find errors that are likely to occur in the model design process. As model design
is conducted by a developer, errors can occur in the design process due to mistakes and faults of the
4. Sensors 2010, 10 8666
developer. In order to check such errors beforehand, this paper conducts model verification at the stage
prior to generation of application codes from a model.
There are also modeling tools that achieve easy and rapid programming such as LabVIEW [12] and
RTDS [13]. However, they are the tools for embedded application development and are not suitable for
sensor network application development. They support embedded operating systems but embedded
operating systems have different characteristics from sensor network operating systems, which are
lightweight and low-power and capable of controlling resource-constrained hardware platform, so such
tools cannot be easily adopted for developing applications for sensor network operating systems.
Moreover, when using these tools node softwares can only be developed one at a time. This contrasts
with our approach, which allows a large number of node softwares to be generated at once from the
same model.
3. USN Application Development Framework
When our approach is used, node software for an application is designed by setting values of the
predefined attributes and then source code for node software is automatically generated by composing
code templates according to the values of these attributes. To support this application development
technique, the USN application development framework should be established. Figure 1 presents
the framework.
Figure 1. USN application development framework.
The USN application development framework consists of Attributes, Code Templates and
Development Tool. To effectively develop USN applications using the method presented in this paper,
a toolkit for application development needs to be established. We call this toolkit “Development Tool.”
The Development Tool is established by an expert developer familiar with the target OS for which
USN application will be executed.
The developer of the Development Tool makes Attributes and Code Template necessary for
designing an application on the basis of functions and modules (or components) provided by the target
Attributes
Code Templates
Development Tool
Function Attributes
Development Attributes
Execution Code Templates
Module Code Templates
Modeler
Model Verifier
Configuration Information
Generator
Source Code Generator
USN Application Development Framework
5. Sensors 2010, 10 8667
OS. The Attributes and Code Templates so made will be included in the Development Tool and used
in developing USN applications.
Attributes are categorized into Function Attributes and Development Attributes. Function Attributes
are used to select the modules which are provided by the target OS. Development Attributes are used
to set the information for development of node software.
Code templates are developed for generating node software for an application according to the
functions of nodes such as data sensing, data transmitting, data collecting, data processing and
actuating. Code templates are developed based on modules provided by target OS. Code Templates are
categorized into Module Code Templates and Execution Code Templates. Module Code Templates are
developed by composing the modules of target OS in order to support the functions of nodes.
Execution Code Templates are templates for core codes to execute node software based on the selected
functions. Code Templates can vary according to the target OS.
Finally, the role of the Development Tool is to support sensor network programming in order to
expedite development of applications. The Development Tool includes core four modules—Modeler,
Model Verifier, Configuration Information Generator and Source Code Generator. Modeler helps
developers to draw sensor network model diagrams and design node software by setting attributes.
Model Verifier checks whether the model of the application is correctly designed without errors. The
Configuration Information Generator creates the configuration information of nodes in the model using
the model information such as attribute values. The Source Code Generator creates software for nodes
using code templates.
4. USN Application Development Based on the Framework
4.1. The Process of USN Application Development
Figure 2 shows the process for constructing a USN application based on the framework proposed in
our approach.
Figure 2. Construction process of USN application.
6. Sensors 2010, 10 8668
In Phase 1 of the process, the developer draws a sensor network model diagram. The model is
described using the UML class diagram notation [17]. In Phase 2, the developer sets attribute values of
classes in the model. Through the setting of attribute values, operating system components to support
the application are selected. In Phase 3, the developer verifies the sensor network model. If the model
is not correct, the construction process should be repeated from Phase 1 or Phase 2. Model verification
of Phase 3 is necessary because the developer manually writes the model and sets attribute values.
After model verification, the developer generates configuration information for each class in the model.
In Phase 4, the developer generates source code for classes using the configuration information and
code templates. Then application development is completed by editing the source code if needed.
4.2. Sensor Network Modeling
A sensor network model is described with three elements: node, node type and association. The
notation of the sensor network model shown in Table 1 was obtained from UML class diagram
notation [17].
Table 1. The notation for the sensor network model.
Name Notation Corresponding UML notation
Node Class
Node Type <<type name>>
Stereotype which represents the type of a class.
<<SENSOR>>, <<ROUTER>>, <<SINK>>,
<<ACTUATOR>>, <<SENSOR_ROUTER>>,
<<ROUTER_SINK>>,
<<ACTUATOR_ROUTER>>, etc. can be
stereotypes of the classes that indicate node types
Association Association between classes
There are four roles which can be performed by nodes in a sensor network: sensor, router, sink and
actuator. In sensor network modeling phase, a developer should draw a sensor network model diagram
by considering the features of nodes according to their roles. Features per node role are as follows.
Sensor: A node which has a sensor role senses data and transmits the data to a coordinator
node.
Router: A node which has a router role plays the coordinator role. It controls a subnetwork. A
router node receives data from other nodes in the subnetwork and transmits the received data
to the Personal Area Network (PAN) coordinator node.
Sink: A node which has a sink role plays the PAN coordinator role. It controls the whole
network. A sink node collects data from other nodes in the sensor network and controls them.
Actuator: A node which has an actuator role controls devices.
7. Sensors 2010, 10 8669
Node Type can be created according to the roles for nodes. The “type name” is defined by
enumerating role names separated by underscore. For example, type of a node is <<SENSOR>> if the
node has a sensor role. And type of a node is <<SENSOR_ROUTER>> if the node has two
roles—sensor and router.
4.3. Attribute Setting
To generate node software from the designed model, the developer should configure the attributes
of each node. The developer should set attribute values of each class in the model. The developer can
set scheduler type, network topology, sensor type, etc. of each class in the model. Figure 3 shows an
example of attribute setting. In the example, one can see attribute values of sink0 in the sensor network
model for Gas and Light Monitoring System. A developer can design node software by setting
attribute values for each node in the sensor network model as in Figure 3.
Figure 3. Attributes values setting for sink0.
4.4. Model Verification
Model verification is conducted to find and correct errors of the designed model. The developer
conduct model verification to check whether the model he designed is accurately designed according
to specifications and assumptions [18]. If the model has errors, the generated application is highly
likely to produce errors during execution. Therefore, model verification is needed to check before
generation of application codes whether there are errors in the designed model.
For this, this paper conduct model verification on the designed sensor network model from the three
viewpoints of commonality verification, association verification and node verification.
Commonality verification: Requirements common to all nodes should be confirmed in order to
ensure that communication between nodes can be performed without any problems. They include
communication protocol compatibility and communication channel compatibility.
<<SINK>>
sink0
8. Sensors 2010, 10 8670
Association verification: Associations in the sensor network model represent the routing paths
between nodes. Through association verification, developers can check whether the model is
properly designed such that data is transmitted to the server through an appropriate routing path.
Association verification is effective in case of designing an application using a static routing table.
A static routing table may be used when the number of nodes comprising the sensor network is
small like in a home network and the application to be designed can determine the routing path
between the nodes beforehand. A static routing table may also be used when designing an
application based on an OS that supports static routing such as Nano-Qplus. In such cases,
conducting association verification is effective in generating an accurate application. If the
operating system supports dynamic routing, association verification of the sensor network model is
not performed.
Node verification: Attribute values of each node should be checked for their correct values.
Through node verification, developers can check whether node software is properly designed such
that they satisfy the constraints imposed by the node type and the constraints imposed by the target
platform.
After model verification, the developer should generate configuration information for each node
before code generation, which is automatically generated from model information. The configuration
information stores attribute values which are set in Phase 2. And it is used to automatically generate
node software for each node.
4.5. Code Generation
Figure 4 presents the algorithm for generating software source codes of each node. Configuration
information is parsed in order to get the attribute values of a node and to select the operating system
components that are necessary to generate the software. According to the values of attributes, module
and execution code templates are selected from the Code Templates Repository and composed into the
node software.
Figure 4. The algorithm for generating node software.
READ the type of the target OS
FOR each node in the sensor network model
INIT template
READ configuration information
FOR each attribute in the configuration information
GET the value of the attribute
OBTAIN module code template according to the value
COMPUTE template = template + module code template
ENDFOR
GET the pattern of the main code
OBTAIN main code template according to the pattern
COMPUTE template = template + main code template
ENDFOR
9. Sensors 2010, 10 8671
In general, even when two nodes have the same node type, their software source codes may differ
from node to node because different components can be selected depending on the specific values of
their attributes.
5. Evaluation
We performed a case study in order to confirm the effectiveness of the proposed approach. In this
section, we apply the proposed technique to Gas and Light Monitoring System (G&LM System) based
on the Nano-Qplus operating system for home environmental monitoring and evaluated the result. A
variety of applications using gas sensor networks are developed for environmental and safety
monitoring [19,20]. A light sensing and actuation application [21] is a well-studied problem in the
home environment. Accordingly, we prototyped G&LM System for home environmental monitoring
as an application example.
Our sample application consists of nodes which have roles such as gas and light sensor, router, sink
and actuator. Sensor nodes sense gas and light data and transmit the sensing data to router nodes. The
router nodes receive the data and transmit it to the sink node. The sink node aggregates the data,
computes it, determines the action commands and transmits the commands to actuator nodes. The
actuator nodes perform the actions, that is, the gas valve and the light lamp operate according to the
actions. Figure 5 presents the structure of the G&LM System. The G&LM System is a simple
environmental monitoring system, but it has all kinds of sensor nodes which are necessary for a sensor
network. So, we think that it is possible to evaluate the proposed technique through the G&LM System.
Figure 5. Structure of the G&LM System.
10. Sensors 2010, 10 8672
Table 2. The list of attribues for the design of node software based on Nano-Qplus.
Attribute Description
Development
Attributes
nodeType Choose one type among Sensor, Router, Sink and Actuator.
applicationAuthorName Write information about application author.
nodeID Write identification number of node.
adjacentActuatorNodeID Write identification number of adjacent actuator node.
PAN_Cordinator_Node_Enable Decide whether the node is PAN coordinator.
NON_BEACON_Enable Decide whether the node uses BEACON.
defaultMACAddr Write MAC address.
Default_Extended_MAC_Addr_
Used
Decide whether the node uses extended MAC address.
defaultExtendedMACAddr
Write extended MAC address if the node uses extended MAC
address.
associationPermitStartNodeID
Write identification number of the first node which is permitted
to associate.
associationPermitEndNodeID
Write identification number of the last node which is permitted
to associate.
nextHopRoutingFirstNodeID Write identification of the next node in routing path.
nextHopRoutingSecondNodeID Write identification of the next alternative node in routing path.
rfChannel
Write RF channel. If nodeType is Router or Sink, rfChannel
should be set.
scanChannel
Write scan channel. If nodeType is Sensor or Actuator, scan
Channel should be set.
Function
Attributes
Scheduler Choose one among none, FIFO and PreemptionRR.
Zigbee RF Choose one among Simple, IEEE802.15.4MAC and StarMesh.
EEPROM_Enable Enable EEPROM module.
Flash_Memory_Enable Enable Flash Memory module.
Timer_Enable Enable timer module.
UART
Choose one among none, printf_module, scanf_module and
printf&scanf.
LED_Enable Enable LED module.
RSSI_Enable Enable RSSI module.
Sensor_Battery_Enable Enable Battery sensor module.
Sensor_Temperature_Enable Enable Temperature sensor module.
Sensor_Light_Enable Enable Light sensor module.
Sensor_Gas_Enable Enable Gas sensor module.
Sensor_Point_infra_red_Enable Enable Point_infra_red sensor module.
Sensor_Humidity_Enable Enable Humidity sensor module.
Sensor_Ultra_sonic_Enable Enable Ultra_sonic sensor module.
Utility_Functions_Enable Enable utility functions.
Kernel_Debug_Functions_Enable Enable kernel debug functions.
System_Log_Functions_Enable Enable system log functions.
11. Sensors 2010, 10 8673
In this paper, development framework is established first in order to develop an application for
G&LM system based on the Nano-Oplus OS. The development framework is established in the order
of attribute design, preparation of code templates and development of Development Tool.
Table 2 is the complete list of attributes made for model design of an application based on
the Nano-Qplus OS. When the values for the attributes in Table 2 are set, the code templates related to
the relevant attributes are selected and combined to generate node software.
Table 3 maps the OS module code files corresponding to each attribute in order to prepare module
code templates necessary to generate node software to be implemented under the Nano-Oplus OS.
Table 3. The list of module code provided by Nano-Qplus corresponding to attributes.
Attribute Module Code Files of Nano-Qplus
Timer_Enable timer.h, timer.c
ADC_Enable adc.h, adc.c
UART printf.h, printf.c, scanf.h, scanf.c
EEPROM_Enable eeprom.h, eeprom.c
LED_Enable led.h, led.c
Scheduler fifo.h, fifo.c, preemption-rr.h, preemption-rr.c
Zigbee_RF rf.h, rf.c, net.h, mac.c, routing-star-mesh.c
Flash_Memory_Enable flashmem.h, flashmem.c
Sensor_Battery_Enable adc_bat.h, adc_bat.c
Sensor_Temperature_Enable adc_temp.h, adc_temp.c
Sensor_Light_Enable adc_light.h, adc_light.c
Sensor_Gas_Enable adc_gas.h, adc_gas.c
Sensor_Point_infra_red_Enable adc_ir.h, adc_ir.c
Sensor_Humidity_Enable adc_humidity.h, adc_humidity.c
Sensor_Ultra_sonic_Enable adc_ultrasonic.h, adc_ultrasonic.c
Utility_Functions_Enable utils.h, utils.c
System_Log_Functions_Enable log.h, log.c
Table 4 shows module code templates and execution code templates drawn up for generation of
node software codes to be implemented under Nano-Oplus OS. Table 5 shows design of the table on
determination of reliance between templates necessary for combination of code templates for
generation of node software based on Nano-Oplus OS.
12. Sensors 2010, 10 8674
Table 4. Code templates for node software based on Nano-Qplus.
Template Type Template
Module
Code
Templates
Module code templates for
attributes without option
#include “.h file name of a module”
#ifdef module name
#include “.c file name of a module”
#endif
Module code templates for
attributes with option
#include “.h file name of a module”
#ifdef module name
#if defined.(name of option)
#include “.c file name of option”
#elif defined (name of option)
#include “.c file name of option”
#endif
…
#endif
Execution
Code
Templates
Codes
included
according to
selection of
the type of
RF module
SimpleZigbee
mlme_start_request(MY_MAC_ADDRESS,
rf_recv_data);
MAC Zigbee
mlme_ll_link_start(NULL, rf_recv_data);
StarMesh
Codes
included
according to
selection of
the type of
scheduler
FIFO (*start)((void *)0);
PreemptionRR
uint8_t int_handle;
int_handle = thread_disable_int();
thread_enable_ints(int_handle);
pthread_create(NULL, rf_recv_data);
start_threads();
Table 5. Table on determination of reliance between templates according to the role of node.
Role of Node Reliance between Code Templates
SENSOR startnet_schedulesense send
ROUTER start net_schedule receive send
ACTUATOR start net_schedule receive actuate
SINK start net_schedule receivecomputesend
Node software is generated by combining code templates in the order presented in Table 5
according to the role of node. The software code is generated based on the templates presented in
Table 4. The templates of Table 4 are generated from the module code files presented in Table 3.
Using the attributes presented in Table 2, module code files are selected and development information
is set to generate node software.
13. Sensors 2010, 10 8675
Figure 6 shows the result of G&LM System modeling using the tool to support application
development based on the Nano-Qplus operating system. It was implemented as a plug-in for an
Eclipse [15] platform and utilized the Eclipse Graphical Modeling Framework (GMF) [16] for the
modeling of the application. Through the tool, developers can perform modeling, design and code
generation of the application for the Nano-Qplus [14] operating system.
Figure 7 shows the result of commonality verification. Through the commonality verification, errors
for communication protocol and communication channel of a USN model were found. Figure 7(a)
shows four errors which were detected in commonality verification. Communication protocols of
actuator16, router5 and sensor10 nodes were not compatible because default MAC addresses of
actuator16, router5 and sensor10 nodes were set to wrong value. Communication channel of router 5
node was also not compatible because the RF channel of router 5 node was set to a wrong value.
Accordingly, default MAC addresses of actuator16, router5 and sensor10 nodes were set to proper
values. RF channel of router 5 node also was set to proper value in order to generate correct
application. After correcting wrong values, communication protocols and communication channels of
nodes were compatible [see Figure 7(b)].
Figure 6. G&LM System modeling using the development tool.
14. Sensors 2010, 10 8676
Figure 7. The result of commonality verification.
Figure 8 shows the result of association verification. Through the association verification, errors for
associations between nodes in a USN model were found. Figure 8(a) shows two errors which were
detected in the association verification. Figure 8(a) presents the mismatched associations. The
association between sensor18 node and router4 node was mismatched. The association between
router4 node and router2 node was also mismatched. Accordingly, the mismatched associations were
corrected. After correcting the mismatched associations, there were no errors in associations between
nodes [See Figure 8(b)]. That means routing path of the sensor network for light & gas monitoring
application is correct.
Figure 8. The result of association verification.
(a) Result of commonality verification before correcting errors
(b) Result of commonality verification after correcting errors
(a) Result of association verification before correcting errors (b) Result of association verification after correcting errors
15. Sensors 2010, 10 8677
Figure 9 shows the result of node verification for sink0 node. Through the node verification, errors
for each node in a USN model were found. Figure 9(a) shows two errors which were detected in node
verification for sink0 node. Sink0 node should be PAN coordinator and it should use timer module.
Accordingly, values of PAN_Cordinator_Node_Enable and Timer_Enable were set to „true‟ in order to
generate correct software codes for sink0 node. After correcting wrong values, all attribute values of
sink0 node were appropriate [see Figure 9(b)].
Figure 9. The result of node verification for sink0.
Figure 10 shows the automatically generated software codes for sink0 node. Sink0 node receives
values of gas and light sensed by the sensor node and compares them with threshold values. If it
determines on comparison that gas valve or light lamp needs to be operated, it transmits through router
node actuation command to actuator node connected to gas valve or light lamp. Figure 10 is part of
software codes implementing such operation, which shows module part that processes transmitted data
and execution code part.
In order to evaluate the efficacy of the proposed development approach, we developed the
application for G&LM System using two approaches and compared the results from the two
approaches. One approach is to develop the application using the proposed technique as you can see in
the previous sections. Another approach is to manually develop the application using a standard set of
APIs which are provided by the target operating system. We developed the application for G&LM
System based on the Nano-Qplus operating system version 1.5.x in two approaches. The evaluation
environment is as follows:
Developer—Expert (Known about Nano-Qplus & USN concept)
System—AMD Athlon XP 2600(CPU), 1Gbytes(RAM), Windows 2000
Nano-Qplus Version—1.5.2e
(a) Result of node verification before correcting errors (b) Result of node verification after correcting errors
16. Sensors 2010, 10 8678
Figure 10. Generated source code for sink0.
We compared the memory size of execution code in the two cases. Table 6 presents the result of
comparison. The first one is the size of node software generated when toolkit is made by the presented
method and the application is established by using the toolkit. The second one is the size of node
software when the developer directly developed the application using APIs provided by the sensor
network OS without using the presented method. From the results presented in Table 6, we find that
the memory size of execution code is approximately the same in the two cases. That is, the execution
code produced by our approach is optimized. In our approach, the attributes are used to compose
modules or components of target operating system, so the execution code sizes are not increased.
#include "config.h"
#include "sig-qplusn.h"
...
#define TURN_ON_DEBUG
#ifdef STAR_MESH_ROUTE
BYTE NEXT_HOP_ROUTING_FIRST_NODE=5;
BYTE NEXT_HOP_ROUTING_SECOND_NODE=1;
#endif
...
void rf_recv_data(ADDRESS *srcAddr, INT8 nbyte, BYTE *data) {
route_t route;
BYTE *org_pkt=NULL, packet_type;
UINT8 i;
packet_type = (BYTE)(data[1]);
if (packet_type == SENSOR_DATA_PACKET) {
org_pkt = data;
}
else if (packet_type == INDIRECT_PACKET_TRANSMIT) {
i = decode_indirect_packet(data,&route);
org_pkt = &data[i];
}
nlde_node_incomming_data_indication(org_pkt);
}
....
int main(void) {
uint8_t int_handle;
int_handle = thread_disable_ints();
initialize_nano_resources();
thread_enable_ints(int_handle);
mlme_ll_link_start(NULL,rf_recv_data);
pthread_create(NULL,NULL,start,(void *)0);
start_threads();
return 0;
}
17. Sensors 2010, 10 8679
Table 6. The memory size of execution code.
Node Type The proposed approach
Manual development
approach
Sensor 102Kbytes 100Kbytes
Router 100Kbytes 99Kbytes
Sink 103Kbytes 101Kbytes
Actuator 101Kbytes 100Kbytes
6. Conclusions
The traditional techniques for generating sensor network applications are limited in that developers
must learn new abstraction mechanisms such as high-level language and APIs and most of the
approaches do not provide methods to verify models of applications. Moreover, a developer can
generate only one node software at a time in some approaches. To complement the existing techniques,
this paper proposed a programming technique that helps users construct a large number of node
softwares for sensor network applications easily and rapidly.
The suggestions in this paper can be divided into two—one that constitutes framework and the other
that develops the sensor network application. In the framework build, the method to construct
development kit on an attribute basis is proposed. The framework is constructed by professionals who
are well aware of the sensor network operating system. The framework provides the infrastructure so
that node software for sensor network application can be created automatically as in SNACK [6]. The
codes of sensor network operating system are modularized in the framework. Therefore, a new
template does not have to be added nor an existing template need be altered unless the operating
system codes are updated. This framework can be used continuously once constructed by the
professionals who know sensor network operating system well, unless the operating system is changed
for a new one.
In the section of sensor network application development, the method to develop node software for
sensor network application through attribute set-up is proposed so developers who do not know the
sensor network operating system can create applications easily. Developers who do not know
operating system code can easily develop sensor network applications because the node software is
created automatically through attribute set-up offered by framework. In other words, sensor network
applications can be developed much easier than with other existing techniques, as node software is
created only by attribute set-up on the basis of framework constructed by specialists, not by each
developer. This way is different from the existing techniques in which each developer must make
codes for the each node software. Of course, existing code is reusable in the techniques but if a node
with new function is added, a new code should be made accordingly. So, developers should be familiar
with code writing. However, if developers follow what we suggest in this paper, they just need to set
up the attribute value for the relevant node even if new nodes are added.
The development using attributes is the key technique that enables automated construction of node
software for USN application. The proposed technique has the following strengths:
18. Sensors 2010, 10 8680
Using attributes, the developer can easily design applications without learning new abstraction
mechanisms.
Since the technique generates code from sensor network model instead of models of node software,
a large number of node softwares can be generated at once. For example, if a developer designs a
model for a sensor network which consists of 50 nodes, node software for the 50 nodes can be
generated from the model at one time. Node software here refers to an image file in binary format
that can be installed in one node and can be composed of multiple source code files.
The technique provides a method to verify the sensor network model so that developers can verify
models of applications in terms of commonality verification, association verification and node
verification.
Table 7 shows the result of comparison and evaluation between the method presented in this paper
and the existing USN application development methods mentioned in relevant studies. As seen in the
Table, the method presented in this paper has the advantages of design facilitation and generation of
more accurate applications as well as all the advantages of the existing methods. As future research
directions, we plan to extend the scope of the verification aspects that can be checked by our
verification module and also plan to build Model Simulator for strengthened verification and validation
of sensor network models based on formal approaches.
Table 7. Comparison of the techniques for USN application development.
Our
approach
Regiment Kairos SNACK SPIDEY
TinyGA
LS
ATaG
Programming
level
Network-
level
Network-
level
Network-
level
Node-
level
Network-
level
Node-
level
Network-
level
Design
method
AVS WP WP WP WP DM WP
Model
verification
Support
Not
Support
Not
Support
Not
Support
Not
Support
Not
Support
Not
Support
Model
validation
Support Uncertain Uncertain Support Uncertain Support Support
Code
generation
method
Auto Auto Auto Auto Auto Auto Auto
No. of node
software
generated
from model
multiple multiple multiple multiple multiple single multiple
Convenience
of design
H M M M M MH M
Accuracy of
application
MH M M M M M M
*WP: write program using high-level language or script; DM: detailed model design;
AVS: attribute value setting; Auto: automatically; M: medium; MH: medium-high; H: high.
19. Sensors 2010, 10 8681
Acknowledgments
This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the
national HRD support program for convergence information technology supervised by the NIPA
(National IT Industry Promotion Agency; NIPA-2010-C6150-1001-0013).
References
1. Akyildiz, I.F.; Su, W.L.; Sankarasubramaniam, Y; Cayirci, E. A survey on sensor networks. IEEE
Commun. Mag. 2002, 40, 102-114.
2. Chong, C.Y.; Kumar, S.P. Sensor networks: Evolution, opportunities and challenges. IEEE 2003,
91, 1247-1256.
3. Fukunaga, S.; Tagawa, T.; Fukui, K.; Tanimoto, K.; Kanno, H. Development of ubiquitous sensor
network. Oki Tekunikaru Rebyu 2004, 71, 24-29.
4. Newton, R.; Welsh, M. Region streams: Functional macroprogramming for sensor networks. In
Proceedings of the First Workshop on Data Management for Sensor Networks, Toronto, ON,
Canada, August 2004; pp. 78-87.
5. Gummadi, R.; Gnawali, O.; Govindan, R. Macro-programming wireless sensor networks using
Kairos. In Proceedings of the 1st International Conference on Distributed Computing in Sensor
Systems, Marina del Rey, CA, USA, June 30-July 1; 2005; pp. 126-140.
6. Greenstein, B.; Kohler, E.; Estrin, D. A sensor network application construction kit (SNACK). In
Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems,
Baltimore, MD, USA, November 2004; pp. 69-80.
7. Mottola, L.; Picco, G.P. Logical Neighborhoods: A Programming Abstraction for Wireless Sensor
Networks. In Proceedings of the 2006 International Conference on Distributed Computing in
Sensor Systems, San Francisco, CA, USA, June 2006; pp. 150-168.
8. Cheong, E.; Liebman, J; Liu, J; Zhao, F. Tinygals: A programming model for event-driven
embedded systems. In Proceedings of the 18th Annual ACM Symposium on Applied Computing,
Melbourne, FL, USA, March 2003; pp. 698-704.
9. Bakshi, A.; Prasanna, V.K.; Reich, J.; Larner, D. The abstract task graph: A methodology for
architecture-independent programming of networked sensor systems. In Proceedings of the 2005
Workshop End-to-end, Sense-and-Respond Systems, Applications and Services, Seattle, WA, USA,
June 2005; pp. 19-24.
10. Pathak, A.; Mottola, L.; Bakshi, A.; Prasanna, V.K.; Picco, G.P. Expressing Sensor Network
Interaction Patterns using Data-Driven Macroprogramming. In Proceedings of the 5th IEEE
International Conference on Pervasive Computing and Communications Workshops, White Plains,
NY, USA, March 2007; pp. 255-260.
11. Pathak, A.; Mottola, L.; Bakshi, A.; Prasanna, V.K.; Picco, G.P. A compilation framework for
macroprogramming networked sensors. In Proceedings of the 3rd International Conference on
Distributed Computing in Sensor Systems, Santa Fe, NM, USA, June 2007; pp. 189-204.
12. LabVIEW. Available online: http:// www.ni.com/pdf/products/us/2005-5554-821-101-LO.pdf/
(accessed on 20 August 2010).