IEEE Communications Society

(COMSOC) Resources

Technical Conferences


VPKI Hits the Highway

Dine and Learn: 5/9 Registration

With the increasing prospects of deploying vehicular networks there are challenges and debates. Viable deployment models, different air interfaces, spectrum sharing issues and security and privacy concerns are among the most topical issues of industry debate. This talks present a condensed account of the 10-year effort to develop and deploy vehicular public-key infrastructure (VPKI) as a security infrastructure for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) intelligent transportations systems (ITS).   An examination of the Secure Credential Management System (SCMS) will highlight the ways in which government, industry, and academia have converged to secure the promise of vehicular networks as ITS emerges as a reality of the 21st century.  A case study of the current US Department of Transportation’s Connected Vehicle Pilot will be the focus of the presentation.

Tim Weil is a Senior Member of the IEEE and Security Editor for IT Professional magazine (IEEE).

In the areas of Vehicular Networks his work includes the IEEE 1609 (WAVE) standards, US DOT VII/Intellidrive and Connected Vehicle programs, author and speaker on topics in Security for Vehicular Networks. His interests include “Service Management for Vehicular Networks Using WAVE.  (IEEE 1609) Protocols” and topics related to the PKI models for implementing IEEE 1609.2 (WAVE Security). Mr. Weil is an industry-certified security professional (CISSP/CCSP, CISA, PMP), past chair of the IEEE Denver Communication Society Chapter and current candidate for Director of IEEE Region 5.

Rocky Mountain IPv6 Task Force


Internet Protocol version 6 (IPv6).


Dear Colleagues


Similar to previous summers, this June and July we will be offering short courses at Colorado School of Mines focusing on communication networks and data analytics for the Smart Grid. Mines will award the participants Continuing Education Units (CEU) upon completion of each course. This year, we are excited to offer a third course: a one-day general introduction to data science. More details appear below:


A Crash Course on Data Science – June 16, 2017

This one-day course will cover:

·  Introduction to Big Data: Challenges and Opportunities

·  Data Collection and Pre-Processing (Data collection, Data cleaning, Visualization, Exploratory statistical analysis, Dealing with outliers, Dealing with missing values)

·  Overview of Descriptive Analytics Techniques (Descriptive clustering, Association rules)

·  Overview of Predictive Analytics Techniques (Regression, Decision trees, Support vector machines, Artificial neural networks)

·  An Overview of Hadoop


For more information, please visit:


Communication Networks for the Smart Grid – July 13-14, 2017

This 2-day course will cover:

·  Communication requirements for main power system applications

·  Fundamentals of communication engineering (analog and digital modulation, multiple access schemes, spread spectrum techniques)

·  Communication media for the Smart Grid (wired versus wireless)

·  Overview of layered communication architecture (OSI and TCP/IP, functions and design issues related to data link layer, MAC sub-layer, network layer and transport layer); review of Ethernet, IEEE 802.11 family, WiMAX, Bluetooth and ZigBee; step by step design of a communication network

·  Overview of main communication protocols used in power and energy systems (DNP3, IEC 61850, TASE.2/ICCP, different protocols for home area networks, e.g. OpenHAN, BACnet, LonWorks, ZigBee)

·  Overview of time synchronization protocols (NTP, IRIG-B, PTP)


For more information, please visit:


Smart Grid Data Analytics – July 26-28, 2017

This 2.5 day course will cover:

·  Smart Grid and Big Data (Sources and types of data, Challenges with big data in utility applications, Data analytics for utility operations, Data analytics for customer interactions, Data analytics for cyber-security, Data analytics for utility business intelligence)

·  Data Collection and Pre-Processing (Data collection, Data cleaning, Visualization, Exploratory statistical analysis, Discretization, aggregation, and standardization of data, Dealing with outliers, Dealing with missing values)

·  Descriptive Analytics (Descriptive clustering, Association rules, Self-organizing maps)

·  Predictive Analytics (Regression: linear vs. nonlinear; univariate vs. multivariate, Decision trees, Support vector machines, Artificial neural networks, Multiclass classification techniques, Model evaluation)

·  Prescriptive Analytics and Case Studies (From situational intelligence to intelligent decision making, Decision making under uncertainty, Case studies on integration of renewable generation with the grid, power grid risk management against natural hazards, demand response customization, cyber-intrusion and cyber-security, etc.)

·  Introduction to Hadoop


For more information, please visit: 


If you have any questions, please feel free to contact me at