International Conference on Omni-layer INtelligent Systems | Call for Papers 2019
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Call for Papers 2019

SCOPE

The Intelligent Internet of Things (IoT) tsunami is affecting every aspect of our daily lives, ranging from smart cars, smart homes, smart cities, smart factories to smart health, and smart environments. Although IoT vastly expanded the possibilities to fulfill many of our needs, numerous challenges still should to be addressed in order to develop smart, consistent, suitable, safe, flexible and power-efficient systems. To enable this transformation and to better understand the untapped opportunities, the interdisciplinary landscape of IoT demands a large number of significant technological advancements in the hardware and software communities to come together and to synergize their efforts. COINS focuses on novel omni-layer techniques for smart IoT systems, by identifying new perspectives and highlighting impending research issues and challenges. In particular, this conference addresses all-important aspects of novel IoT technologies from Connected Devices, to Edge/Fog, Cloud, and application, covering manufacturing, materials, CMOS and beyond-CMOS devices, architecture, embedded systems, reconfigurable hardware, network, cloud, Omni-layer artificial intelligence and machine learning, big data, emerging applications, as well as human-machine interaction. COINS also discusses the associated challenges that need to be overcome for achieving the goal of accuracy, privacy, reliability and security. The three-day COINS event consists of a conference with plenary keynote speakers, invited papers, regular papers, panels, special hot-topic sessions, and workshops.

 

TRACKS and TOPICS

Topics of interest include, but are not limited to

  • Track 1: Internet of Things: From Device, to Edge, and Cloud
    • Network Design and Architecture
    • Software Architecture and Middleware
    • Mobile Services
    • Data and Knowledge Management
    • Context-awareness and Location-awareness
    • Security, Privacy and Trust
    • Performance Evaluation and Modeling
    • Networking and Communication Protocols
    • Machine to Machine Communications
    • Intelligent Systems for IoT and Services Computing
    • Energy Efficiency
    • Social Implications for IoT
    • Future of IoT and Big Data
    • Technological focus for Smart Environments
    • Next Generation Networks
    • Smart City Examples and Case Studies
    • Data Analysis and Visualization for Smart City, Green Systems and Transport Systems
    • Architecture for secure and interactive IoT
    • Intelligent Infrastructure and Guidance Systems
    • Traffic Theory, Modeling and Simulation
    • Sensor Networks, Remote Diagnosis and Development
    • Transportation Management
    • Pattern Recognition and Behavioral Investigations for Vehicles, Green Systems and Smart City
  • Track 2: Omni-Layer Security, Privacy, and Trust
    • Algorithms, software engineering and development
    • System design and implementation
    • Testing (software engineering; penetration; product development)
    • Encryption (all aspects)
    • Firewall, access control, identity management
    • Experiments of using security solutions and proof-of-concepts
    • Large-scale simulations in the Cloud, Big Data and Internet of Things
    • Intrusion and detection techniques
    • Social engineering and ethical hacking: techniques and case studies
    • Software engineering for security modeling, business process modeling and analytics
    • Trust and privacy
    • Location-based privacy
    • Data security, data recovery, disaster recovery
    • Adoption challenges and recommendation
    • Information systems related issues
    • Conceptual frameworks and models
    • Emerging issues and recommendations for organizational security
    • E-Commerce and online banking
    • Social network analysis, emerging issues in social networks
    • Education and e-Learning
    • Surveys and their quantitative analysis
    • Architecture (technical or organizational)
    • Case Studies
  • Track 3: Omni-layer Reliability in IoT Era
    • Reliability physics of materials, CMOS and beyond-CMOS devices, and circuits
    • Reliability and security of specialized cache/memory/storage systems, under stringent energy constraints
    • Reliability and security of circuits including digital, mixed-signal, and RF
    • Omni-layer dependability validation, failure/security analysis techniques, modeling methodologies, and simulation techniques
    • Design for security (e.g., hardware Trojans, interaction between test, security, and reliability, etc.)
    • Adaptive, reconfigurable designs, enabling runtime selection of operation mode and reliability/security
    • Application-specific accelerators designs
    • High-performance, low power designs for secure and reliable applications
    • Architectural approaches for security and reliability
    • Algorithm and software (e.g. applications; middleware; and operating systems)
    • Reliability and security clouds systems in IoT era
    • Case studies for reliable and secure IoT systems (e.g., aerospace, aerial, medical, automobile, communications, energy, smart grids, etc.)
  • Track 4: VLSI, EDA, Embedded Systems, and Computer Architecture
    • System Specification and Modeling
    • System Design, High-Level Synthesis and Optimization
    • High-Level, Behavioral, and Logic Synthesis and Optimization
    • Low Power and Approximate Computing in System Design
    • System Design Issues for Heterogeneous Computing
    • System Simulation and Validation
    • Formal Methods and System Verification
    • Cell-Library Design, Partitioning, Floor-planning, Placement
    • Clock Network Synthesis, Routing, and Post-Layout Optimization and Verification
    • Design and Test for Analog and Mixed-Signal Systems and Circuits
    • Power Modeling, Optimization and Low-Power Design
    • Temperature and Variability Aware Design and Optimization
    • Embedded System Design Methodologies
    • Reconfigurable Computing
    • Domain and Application-specific Design
    • Logical and Physical Analysis and Design
    • Architectural and Micro-architectural Design
    • System-on-chip Design and HW/SW Co-design
    • Processor, Memory, Storage, Interconnect Designs
    • Architectures for Instruction-level, Thread-level and Memory-level Parallelism
    • Large-scale System Architecture
    • Compilers and Embedded Systems Software
    • Performance Evaluation and Measurement of Real Systems
    • Operating Systems and Middleware
    • QoS Management and Performance Analysis
    • Design for Manufacturability
    • Timing, Power and Signal Integrity Analysis and Optimization
    • CAD for Analog/Mixed-Signal/RF and Multi-Domain Modeling
    • Design for Reliability
    • CAD for Cyber-Physical Systems
    • Architectural Support for Programming Languages and Operating Systems
    • Emerging devices, circuits, and architectures for IoT, Big Data, machine learning, and approximate computing
    • Biological Systems and Electronics, Brain Inspired Computing, and New Computing Paradigms
    • New and Emerging Design Technologies
    • Emerging Technologies for Future Memories
    • Application areas, e.g., automotive, avionics, energy, health care, mobile devices, multimedia and autonomous systems
  • Track 5: Real-time Systems
    • Scheduling Design and Analysis
    • Real-Time Operating Systems
    • Hypervisors and Middle-wares
    • Virtualization and Timing Isolation
    • Mixed-Criticality Design & Assurance
    • Worst-Case Execution Time Analysis
    • Real-Time Networks and Communication Protocols
    • Processor Design for Real-Time Systems
    • Power/Energy/Thermal-aware Algorithms
    • Modeling and/or Formal Methods
    • Industrial Use-Cases and Real-Time Applications
    • Tools, Compilers and Benchmarks for Embedded System
  • Track 6: Alternative and Approximate Computing
    • IoT-aware nano-CMOS and beyond-CMOS devices, sensors, and circuits
    • Specialized and modern memory systems for IoT (e.g.,  Memristor, STT-RAM, FeRAM, etc.)
    • Sub-and near-threshold computing in the IoT regime
    • Reconfigurable embedded sensing and actuating, enabling runtime selection of quality, operation mode and parameter settings of IoT devices
    • Alternative architectures for IoT-specific Big Data search, predictive analytics, deep learning, high dimensional data, feature selection, and feature transformation
    • IoT-specific approximate design, exploration and optimization
    • Accelerators for IoT (e.g., learning, neuromorphic and cognitive computing)
    • Brain-inspired and neuromorphic components, circuits, and systems for IoT
    • Case studies for alternative computing in the IoT era
  • Track 7: Cloud Computing
    • Cloud computing fundamentals e.g., Application Portability, privacy and security, Interoperability, Standards, QoS, and Resource Management
    • Cloud operations, management Platforms, Reliability, Cloud Automation, Hybrid Clouds and Integration
    • Edge an fog computing
    • Service modeling and analytics e.g., Service Modeling and Specification, Service Simulation, Cost Analysis, Service Performance Analytics
    • Mobile cloud computing
    • Mobile Cloud Architectures and Models
    • Mobile Commerce, Handheld Commerce and e-markets on Cloud
    • Cloud computing platforms and applications such as Access Control, Design Patterns, Data Centers, Storage and Networking Technologies, Middleware Frameworks, and Development Methods
    • Cloud computing enabling technologies such as API Management, Virtualization Technologies, Application Containers, Microservices and Lambda Functions, Cloud Optimization and Automation, Cloud Migration, Monitoring of Services, Quality of Service, Service Level Agreements
  • Track 8: Programming Language and Software Engineering
    • Agile software development – Apps and app store analysis
    • Autonomic and (self-) adaptive systems – Cloud computing
    • Component-based software engineering – Configuration management and deployment
    • Crowd sourced software engineering – Cyber physical systems
    • Debugging, fault localization, and repair – Dependability, safety, and reliability
    • Distributed and collaborative software engineering – Embedded software
    • Empirical software engineering – End-user software engineering
    • Formal methods – Green and sustainable technologies
    • Human and social aspects of software engineering – Human-computer interaction
    • Middleware, frameworks, and APIs – Mining software engineering repositories
    • Mobile applications – Model-driven engineering
    • Parallel, distributed, and concurrent systems – Performance
    • Program analysis – Program comprehension
    • Program synthesis – Programming languages
    • Recommendation systems – Refactoring
    • Requirements engineering – Reverse engineering
    • Search-based software engineering – Security, privacy and trust
    • Software architecture – Software economics and metrics
    • Software evolution and maintenance – Software modeling and design
    • Software performance – Software process
    • Software product lines – Software reuse
    • Software services – Software testing
    • Software visualization – Specification and modeling languages
    • Tools and environments – Traceability
    • Ubiquitous/pervasive software systems – Validation and verification
  • Track 9: Big Data
    • Big Data fundamentals – Services Computing, Techniques, Recommendations and Framework
    • Modeling, Experiments, Sharing Technologies & Platforms
    • SQL/NoSQL databases, Data Processing Techniques, Visualization and Modern Technologies
    • Data Center Enabled Technologies
    • Sensor, Wireless Technologies, APIs
    • Networking and Social Networks
    • Data Management for Large Data
    • Security, Privacy and Risk
    • Software Frameworks (MapReduce, Spark etc) and Simulations
    • Modern Architecture
    • Volume, Velocity, Variety, Veracity and Value
    • Social Science and Implications for Big Data
  • Track 10: Artificial Intelligence, Machine Learning, Cognitive Computing, and Advanced Analytics
    • Mobile Data analysis, management and processing for IoT
    • Information fusion for mobile data for IoT
    • Deep learning models, architectures and algorithms for Big Data for IoT
    • Brain-inspired representations learning of Big Data for IoT
    • Edge/for/cloud computing for Big Data and Smart Data for IoT
    • Security, privacy and trust in Big Data and Smart Data for IoT
    • Security, privacy and trust in Internet of Things
    • Streaming data learning algorithms for IoT
    • Intelligent decision-making systems for Big Data and Smart Data in IoT
    • Prediction methods for Big Data and Smart Data applications in IoT
    • Evolutionary computing in Big Data in IoT
    • Swarm Intelligence and Big data for IoT
    • Handling uncertainty and incompleteness in Big Data and Smart Data for IoT
    • Applications of Fuzzy Set theory, Rough Set theory, and Soft Set theory in Smart Data for IoT
    • Open issues for Smart Data in IoT
    • Swarm Intelligence algorithms for cloud-based Internet of Things
    • Machine learning for cloud-based Internet of Things
    • Multi-agent systems for cloud-based Internet of Things
    • Natural language processing for cloud-based Internet of Things
    • Cognitive aspects of AI in cloud-based Internet of Things
    • Intelligent interfaces for cloud-based Internet of Things
    • Fuzzy systems for cloud-based Internet of Things
    • Neural networks for cloud-based Internet of Things
    • Genetic algorithms for cloud-based Internet of Things
    • Deep learning for cloud-based Internet of Things
    • Heterogeneous memory systems design for AI in cloud-based Internet of Things
  • Track 11: Evolutionary Computer Vision, Image Processing and Pattern Recognition
    • 3D Reconstruction
    • 3D Models processing
    • Augmented and Virtual Reality applications
    • Robotic applications
    • Serious Game for Cultural Heritage
    • Advanced Image enhancement and de-noising
    • Advanced Image classification and retrieval
    • Semantic segmentation
    • Virtual restoration
    • Image processing
    • Edge detection
    • Image segmentation
    • Automatic feature extraction and construction in complex images
    • Object identification and scene analysis
    • Object detection and classification
    • Handwritten digit recognition and detection
    • Vehicle plate detection
    • Face detection and recognition
    • Texture image analysis
    • Automatic target recognition
    • Gesture identification and recognition
    • Robot vision
    • Signal Processing
    • Typical pattern recognition tasks such as classification, regression and clustering
    • Feature selection/construction and dimensionality reduction
    • Generalization, transfer learning, and domain adaptation
    • Medical and bio-medical data analysis
  • Track 12: Automation Systems
    • Automated Guided Vehicles
    • Embedded Systems
    • Factory Modeling and Automation
    • Flexible Manufacturing Systems
    • Integrated Manufacturing
    • Interfaces and Human Computer Interaction
    • Learning Systems, Manufacturing Systems
    • Monitoring and Supervision
    • Process Automation
    • Robotics
    • Smart Structures
    • Factory modeling, analysis and performance evaluation
    • Industry 4.0 and Cyber-Physical Systems
    • Intelligent cooperation to resource planning & allocation
    • Service innovation
    • Product and process optimization based on IoT and big data analytics
  • Track 13: Automotive Systems
    • Automotive Design Tools and Methodologies
    • Automotive Systems & Software Architectures
    • Safety, Security, and Reliability
    • Overview of current efforts to build self-driving vehicles;
    • Historical perspective with an emphasis on trends, and technological and societal impacts;
    • Scaling the technology to real-world situations
    • Cloud-based vehicle autonomy
    • Learning technology’s role in self-driving vehicles
    • Generalizing from small numbers of examples or small amounts of data
    • Sensing systems’ and perception algorithms’ successes and limitations
    • The role of and technology for machine reasoning and decision making
    • Simulation models for sensors, vehicles, environments, and other elements
    • Self-driving systems’ computational and power requirements
    • How to verify and validate autonomous functions, measure performance, and build trust
    • The role of maps and vehicle-to-vehicle infrastructure
    • Ethical and legal considerations from a technology perspective
  • Track 14: Intelligent IoT eHealth
    • Internet of things for medical and healthcare applications
    • Novel devices and circuits, and architectural support for healthcare-aware IoT
    • Nano-CMOS and Post-CMOS based sensors, circuits, and controller
    • Wearable and implantable computing and biosensors
    • Cloud-enabled body sensor networks
    • Secure middleware for eHealth and IoT
    • Energy-efficient PHY/MAC and networking protocols for eHealth applications
    • Reprogrammable and reconfigurable embedded systems for eHealth
    • eHealth traffic characterization
    • eHealth oriented software architectures (Agent, SOA, Middleware, etc.)
    • Big-data analytics, machine learning algorithms and scalable/parallel/distributed algorithms
    • Theory and practice of engineering semantic e-health systems, especially methods, means and best cases
    • Fog computing/Edge clouds for health care cloud resource allocation and monitoring
    • Privacy preserving and Security approaches for large scale analytics
    • Fault tolerance, reliability and scalability
    • Case studies of smart eHealth architectures (telemedicine applications, health management applications, etc.)
    • Autonomic analysis, monitoring and situation alertness
  • Track 15: Enterprise Architecture
    • Enterprise Engineering
    • Enterprise Architecture Models and Frameworks
    • Enterprise Knowledge Engineering and Management
    • Business Modelling and Business Process Management
    • Enterprise Architecture Adoption and Governance
    • Enterprise Architecture and Organizational Theory and System Development
    • Enterprise Architecture and Service Oriented Architecture (SOA)
    • Methods, Processes and Patterns for Enterprise Architecture Development
    • Measurements, Metrics and Evaluation of Enterprise Architecture Plan
    • Business-Information Technology (IT) Alignment
    • Architectures and Design Principles for Enterprise Repositories
    • Agile Enterprise Architecture
    • Maturity Models for Enterprise Architecture Artifacts and Processes
    • Evolution of Enterprise Architecture

 

SPECIAL TRACKS 

 

 

Doctoral Symposium 

COINS 2019 (Sponsored by IEEE) Doctoral Symposium provides doctoral students at different stages in their research an opportunity to present their problem statement, goals, methods, and results to receive constructive feedback on their current research and future research directions.

The Ph.D. symposium also provides an opportunity for student participants to interact with established researchers and practitioners in the IoT, AI, ML, Big Data, VLSI, EDA, and Cloud computing community

 

Finally, the symposium seeks to motivate students in the development of their scientific curiosity and facilitate their networking within the research community.

Ph.D. Symposium attendance is open to all COINS registrants.

The manuscript (2-4 pages) should conform to the ACM or IEEE format

 

IMPORTANT DATES

  • Deadline for regular paper submission: December 1, 2018  (December 20, 2018)
  • Notification of acceptance of regular papers: January 15, 2019 (January 31, 2019)
  • Camera-ready: January 31, 2019 (February 10, 2019)

 

  • Doctoral Symposium: January 15, 2019

 

SUBMISSION GUIDELINES

Authors are invited to submit their original manuscripts written in English via online submission platform of Easychair. Each manuscript should include the complete paper text, all illustrations, and references. The manuscript should conform to the ACM or IEEE format: single spaced, double column, US letter page size, 10-point size Times Roman font, up to 6 pages. In order to conduct a blind review, no indication of the authors’ names should appear in the manuscript, references included.

Every accepted paper MUST have at least one author registered to the Conference by the time the camera-ready paper is submitted; at least one of the authors is also expected to attend the conference and present the paper.

 

PUBLICATION AND PROCEEDING

Conference proceedings will be published in ACM. Accepted papers are allowed six pages in the conference proceedings free of charge. Each additional page beyond six pages is subject to the page charge at 150 Euro per page up to the eight-page limit. ACM will hold the copyright for COINS proceedings. Authors of accepted papers must sign an ACM copyright release form for their paper.

Extended versions of selected best papers will be published in a special issue of the ISI indexed Euromicro/Elsevier journal “Microprocessors and Microsystems: Embedded Hardware Design” (MICPRO) having the 2016 Impact Factor as high as 1.025

 

REVIEW PROCESS

All regular papers go through a double-blind peer review process. Submitted manuscripts s are carefully reviewed by at least two reviewers and rated considering Relevance, Originality, Technical Quality, Significance and Presentation. Based on the corresponding score/comments given by reviewers, program and track chairs will decide if a paper can be accepted as a full regular paper (with 20 minutes oral presentation) or as a short paper (with poster presentation). It should be noted that only original papers should be submitted. Authors are highly encouraged to carefully study ACM/IEEE ethical norms regarding plagiarism and self-plagiarism thoroughly before submitting and must make sure that their submissions do not substantially overlap work which has been published elsewhere or simultaneously submitted to a journal or another conference with proceedings. All the submitted papers will be checked by iThenticate Platform (Plagiarism Detection Software) and those papers that contain any form of plagiarism will be rejected without reviews.