TrustDataIoT 2014 : Future Generation Computer Systems Special Issue on Trustworthy Data Fusion and Mining in Internet of Things
Call For Papers
Future Generation Computer Systems (FGCS, IF: 1.86)
Special Issue on Trustworthy Data Fusion and Mining in Internet of Things
Call for Papers
The Internet of Things (IoT) is going to create a world where physical objects are seamlessly integrated into information networks in order to provide advanced and intelligent services for human-beings. The number of interconnected “things” such as sensors or mobile devices had overtaken the actual number of people population in the world since 2011, and it is expected to reach 24 billion by 2020. Various applications and services of IoT have been emerging into market, e.g. surveillance, health care, transport, food safety, and distant object monitor and control. The future of IoT is promising.
As involved with a huge number of wireless sensor devices, IoT produces large volumes of data. Data fusion and mining present an efficient way to manipulate, integrate, manage and preserve mass data collected from various “things”. They have showed significance to extract and gain useful information based on the data collected from sensor networks, mobile devices or RFID streams, and have been playing an indispensible role in IoT services and applications.
However, data in IoT are massive, multi-sourced, heterogeneous, redundant, dynamic and sparse. In order to guarantee a sensible thing-to-thing interaction, an effective aggregation of various data should be carried out to obtain holography of objects. Moreover, IoT is usually sensitive to information security, to assure the authenticity and reliability of the data resulting from aggregation and extraction is a challenge. On the other hand, the data collected often contain private information reflecting users' daily activities. Secure multi-party computations (SMC) is expected for preserving user privacy. But intelligently providing context-aware and personalized services based on data fusion and mining and at the same time preserving user privacy to an expected level causes a big challenge in current IoT research and practice.
Trustworthy data fusion and mining concern efficient, accurate, secure, privacy-preserved, reliable and holographic data process and analysis in a holistic manner. It has become crucial of importance for the future success of IoT. In recent years, trustworthy data mining and fusion in IoT have been paid an increasing attention and gained extensive studies towards being successfully applied into the practice of IoT applications and services. This special issue aims at presenting advanced academic and industrial research results related to Trustworthy Data Fusion and Mining in Internet of Things.
Topics of interest include, but are not limited to:
• Reliable data collection and abstraction
• Trusted data synchronization and aggregation
• Data clustering and classification
• Holographic data fusion
• Trust, security and privacy of data in IoT
• Efficient and trustworthy huge data process
• Trustworthy data fusion and mining algorithms
• Trusted pervasive data computing
• Modeling related to trustworthy data fusion and mining
• Trust issues of data fusion and mining in social networking and cloud computing
• Trust issues of smart grid and smart metering
• IoT applications and services based on data mining and fusion
All manuscripts and any supplementary material should be submitted via the journal’s online submission and peer-review systems at http://ees.elsevier.com/fgcs/. Please follow the paper format and instructions given on this website and indicate that the submission is to the special issue ‘TrustDataIoT’. Each paper will go through a rigorous peer-review process by at least three international researchers.
Paper submission due: Extended to April 15th, 2014
Acceptance notification: October 1st, 2014
Approximate publication date: TBD
Zheng Yan, Xidian University/Aalto University, email@example.com
Jun Liu, Xi’an Jiaotong University, firstname.lastname@example.org
Athanasios V. Vasilakos, University of Western Macedonia, email@example.com
Laurence T. Yang, St. Francis Xavier University, firstname.lastname@example.org
Please email inquiries concerning this special issue to:
Prof. Zheng Yan, Email: email@example.com
Prof. Jun Liu, Email: firstname.lastname@example.org
Prof. Athanasios V. Vasilakos, Email: email@example.com
Prof. Laurence T. Yang, Email: firstname.lastname@example.org