Internet of Things Data Analytics (IoTDA)

Introduction to Workshop

The Internet of Things (IoT) is not only about connecting billions of devices to the Internet, but also about connecting people, services, applications, businesses, infrastructures, among many others. What makes the IoT even more interesting is how data and technology can be blended together to build sustainable IoT Data Analytics (IoTDA) applications. With billions of devices embedded in environments, buildings, vehicles, or products continuously generating huge amounts of real-time data in many different formats, building sustainable IoTDA applications is becoming more challenging. One of the main hurdles is determining a suitable environment for processing IoT data. While it is envisioned that IoT applications would typically perform data processing on the cloud, a growing number of limitations in meeting applications’ demands is prompting researchers to investigate more efficient ways for processing data near IoT devices particularly for applications that require low-latency response.

With the growing number of IoT devices that have very limited computational abilities, the emergence micro-clouds (or fog nodes) near data sources to create sustainable IoTDA applications makes this very timely. This workshop will provide researchers and practitioners a venue to discuss possible new methods for building sustainable IoTDA applications and develop methods and techniques to investigate existing IoTDA limitations. In this context, the workshop’s ambition is to help in shaping a community of interest on the existing research opportunities and challenges resulting from performing data analytics for IoT applications. In addition, the workshop will help in bringing researchers and practitioners together to investigate innovative ideas or approaches to this new research challenge with main focus on developing sustainable IoTDA applications, foster collaborations and exchange points of view.

Research Topics

• Distributed Intelligence for IoT Data Analytics
• Heterogeneous IoT Data Analytics for Fog Computing
• Data Quality Metrics for IoT Applications
• Distributed Data Analytics in Fog Computing
• Big Data IoT Applications (e.g. smart city, manufacturing, e-health, etc.)
• Visual Analytics Algorithms for IoT Applications
• Middleware for IoT Applications
• Mobility and Context-Awareness for IoT Applications
• Process Modelling for IoT Applications
• Storage, Querying and Diffusion of IoT Data
• Data Compression for Constrained IoT Devices
• QoS Guarantee for IoT Applications
• Privacy, Security and Trust Issues in IoT Applications
• Recovery Schemes for IoT Applications
• Internet of Things as a Service (IoTaaS)
• IoT Data Centers' Data Analytics
• IoT Management Capabilities for Data Centers

Important Dates

• Nov 3, 2018: Due date for full workshop paper submissions
• Nov 9, 2018: Notification of paper acceptance to authors
• Nov 20, 2018: Camera-ready of accepted papers (firm date)
• Dec 11, 2018: Workshop Day

Keynote Speaker: Dr. Di Wang (Microsoft)

Abstract

Recent breakthroughs in deep learning have made Deep Neural Network (DNN) models a key component of many AI applications ranging from speech recognition and translation, face recognition, and object/human detection and tracking, etc. These DNN models are very resource demanding in terms of computation cycles, memory footprint, power and energy consumption, etc. and are mostly trained and deployed in the cloud/datacenters. However, these is a growing demand on pushing the deployment of these AI applications from cloud to a wide variety of edge and IoT devices that are closer to data and information generation sources for reasons such as better user experience (latency and throughput critical/sensitive apps), data privacy and security, limited/intermittent network bandwidth, etc. Compared to datacenters, these edges and IoT devices are very resource constrained and may not be able to host these compute expensive DNN models. Great efforts have been made to optimize the serving/inference of these DNN models to enable their deployment on edge and IoT devices and to even reduce resource consumption/cost in datacenters.

We will talk about a few research and product work at Microsoft on optimizing DNN inference pipeline that touch upon hardware accelerator, compiler, model architecture, application requirements and system dynamics. We will discuss how these works optimize different layers of the DNN system stack. Moreover, we will show the importance of looking at the DNN system stack holistically in order to achieve better model performance and resource constraints tradeoffs for edge and IoT enabled applications.

Biography

Dr. Di Wang is currently a researcher of Microsoft Ambient Intelligent Team at Microsoft AI Perception and Mixed Reality.

His research interests span the areas of computer systems, computer architecture, applied machine learning, VLSI design, energy-efficient systems design and sustainable computing. Specifically, he has applied his expertise on these topics to the areas of datacenters, IoT, storage systems, fault tolerant systems, EDA tools and recommendation systems. Wang has authored over 30 publications in top conferences and journals and has received 4 best paper awards and 1 best paper nomination. His work has also been featured in the CACM news and was chosen as IEEE sustainable computing register’s pick of the month.

Wang received his Ph.D. in Computer Science and Engineering from Penn State University, M.S. in Computer Systems Engineering from Technical University of Denmark (DTU) and B.E. in Computer Science and Technology from Zhejiang University.

Workshop Program Schedule

Tuesday – December 11, 2018 - Location: St. Helens (Floor 2)

10:05 am – 10:15 am Opening Remarks and Welcome

10:15 am– 11:00 am Keynote Presentation: DNN Inference Optimization Across the System Stack for Edge and IoT Enabled Applications

Dr. Di Wang (Microsoft)

11:10 am– 11:30 am An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning

Nathalia Nascimento, Paulo Alencar, Carlos Lucena, and Donald Cowan

11:30 am– 11:50 am Efficient Data Compression for IoT Devices Using Huffman Coding Based Techniques

Amlan Chatterjee, Rushabh Jitendrakumar Shah, and Khondker Hasan

11:50 am– 12:10 pm Governance in Adaptive Normative Multiagent Systems for the Internet of Smart Things: Challenges & Future Directions

Marx Viana, Lauro Caetano, Francisco Cunha, Paulo Alencar, and Carlos Lucena

12:10 pm – 1:30 pm Lunch

1:40 pm– 2:00 pm Internet of Things Big Data Analytics: The Case of Noise Level Measurements at the Roskilde Music Festival

Tor Morten Groenli, Benjamin Flesch, Raghava Mukkamala, Ravi Vatrapu, Sindre Klavestad, and Herman Bergner

2:00 pm– 2:20 pm Metric Indexing for Efficient Data Access in the Internet of Things

Christian Beecks, Alexander Grass, and Shreekantha Devasya

2:20 pm– 2:40 pm Continuous Location Statistics Sharing Algorithm with Local Differential Privacy

Fatima Zahra Errounda and Yan Liu

2:40 pm– 3:00 pm Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources

Gerrit Janßen , Ilya Verbitskiy, Thomas Renner, and Lauritz Thamsen

3:00 pm– 3:20 pm File-system Front-end for Seamless Job Management in Sensitive Data e-Infrastructures and Cloud Federation

Abdulrahman Azab, Hein Meling, Eivind Hovig, and Antti Pursula

3:20 pm– 3:40 pm IoT Devices Recognition Through Network Traffic Analysis

Mustafizur Rahman Shahid, Hervé Debar, Gregory Blanc, and ZonghuaZhang

4:10 pm – 4:30 pm Coffee Break GRAND FOYER (Floor 4)

4:40 pm– 5:00 pm Intelligence Retrieval from a Centralized IoT Network

Dave Poortvliet and Xinli Wang

5:00 pm– 5:20 pm Utilizing Twitter Data for Early Flood Warning in Thailand

Kulsawasd Jitkajornwanich, Chanwit Kongthong, NattayaKhongsoontornjaroen, Jeedapa Kaiyasuan, Siam Lawawirojwong,

PanuSrestasathiern, Siwapon Srisonphan, and Peerapon Vateekul

5:20 pm– 5:40 pm Audio IoT Analytics for Home Automation Safety

Sayed Khushal Shah, Zeenat Tariq, and Yugyung Lee

5:40 pm– 6:00 pm A Framework for IoT Data Acquisition and Forensics Analysis

Hongmei Chi, Temilola aderibigbe, and Bobby Granville

6:00 pm– 6:20 pm Enhancing the Microservices Architecture for the Internet of Things

Eyhab Al-Masri

6:20 pm – 6:30 pm Closing Remarks

Workshop Team

Chair:

• Eyhab Al-Masri (University of Washington, USA)
• Yan Bai (University of Washington, USA)

Program Committee Members:

• Vikas Agarwal (IBM)
• Xuan-Hong Dang (IBM)
• Mohamed Hamdi (University of Toronto)
• Amit Nanavati (IBM)
• Mena Olyan (University of Waterloo)
• Phillip Yelland (Google)
• Luis Garcia Pueyo (Facebook)
• Lida Ghahremanlou (Coventry University)
• Gargi B. Dasgupta (IBM)
• Michiaki Tatsubori (IBM)
• Ziawasch Abedjan (MIT)

Submission Instructions

We welcome contributions describing original ideas, experiments and applications relevant to the workshop theme which have not been published earlier or are not currently pending submission at any other venue. All submitted papers must include the names and affiliations of all authors. Submitted papers will be peer-reviewed by members of the Workshop Program Committee. All accepted papers will be included in the main conference proceedings (see Proceedings section below).

Submission Categories:

• Long Papers: 6 pages (research at a mature stage)
• Short/Work-in-Progress Papers: 4 pages (early or intermediate stage)

Paper Submission Link:

• Please submit your paper via this link (select IoTDA 2018).

Templates:

• https://www.ieee.org/conferences/publishing/templates.html

Camera Ready Instructions:

• https://wi-lab.com/cyberchair/2018/bigdata18/scripts/BigData_2018_Camera_ready_instruction.php?subarea=S

Proceedings:

• All papers accepted will be included in the IEEE Big Data Conference Proceedings published by the IEEE Computer Society Press. At least one author of each accepted paper must register for the conference and present the paper at the workshop for the paper to be included in the conference proceedings. Details on the registration will be posted on the main conference's page.