Sustainable IT Ecosystems: Enabling Next-Generation Cities

In this paper, we describe an integrated design and management approach for building next-generation cities. This approach leverages IT technology in both the design and operational phases to optimize sustainability over a broad set of metrics while lowering costs. We call this approach a Sustainable IT Ecosystem. Our approach is based on five principles: ecosystem-scale life-cycle design; scalable and configurable infrastructure building blocks; pervasive sensing; data analytics and visualization; and autonomous control. Application of the approach is demonstrated for two case studies: an urban water infrastructure and an urban power microgrid. We conclude by discussing future opportunities to co-design and integrate these independent infrastructures, gaining further efficiencies.Copyright © 2011 by ASME

[1]  B. Kehrli,et al.  Secure super grids™: A new solution for secure power in critical urban centers , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[2]  M. Goedkoop,et al.  The Eco-indicator 99, A damage oriented method for Life Cycle Impact Assessment , 1999 .

[3]  J. R. McDonald,et al.  Intelligent knowledge based systems in electrical power engineering , 2012 .

[4]  M. Merenda,et al.  Fully-integrated wireless temperature sensor with on-chip antenna , 2008, 2008 IEEE Sensors.

[5]  Tsung-Han Lee,et al.  Novel Wireless Mesh Networking Architectures for Future Smart Homes , 2007, Future Generation Communication and Networking (FGCN 2007).

[6]  Dragan Savic,et al.  Trade-off between Total Cost and Reliability for Anytown Water Distribution Network , 2005 .

[7]  Kevin Mills,et al.  A brief survey of self-organization in wireless sensor networks , 2007, Wirel. Commun. Mob. Comput..

[8]  Gerald Rebitzer,et al.  IMPACT 2002+: A new life cycle impact assessment methodology , 2003 .

[9]  Volker Tresp,et al.  Fraud detection in communication networks using neural and probabilistic methods , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[10]  Daniel S. Kirschen,et al.  Intelligent alarm processing in power systems , 1992, Proc. IEEE.

[11]  J. Ross,et al.  Whole-House Measurements of Standby Power Consumption , 2000 .

[12]  Jaideep Srivastava,et al.  A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection , 2003, SDM.

[13]  Amip J. Shah,et al.  Thermodynamics-Based Object-Oriented Environmental Design of an Industrial Air Conditioner , 2009 .

[14]  Thomas E. Graedel,et al.  Streamlined Life-Cycle Assessment , 1998 .

[15]  Sascha Ossowski,et al.  ExperNet: An Intelligent Multiagent System for WAN Management , 2002, IEEE Intell. Syst..

[16]  Yan Yu,et al.  Development of a kind of multi-variable wireless sensor for structural health monitoring in civil engineering , 2005, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[17]  Ronald J. Brachman,et al.  Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud , 2004, Data Mining and Knowledge Discovery.

[18]  Manish Marwah,et al.  Integrated Design and Management of a Sustainable Data Center , 2009 .

[19]  Fei-Yue Wang.  Driving into the Future with ITS , 2006, IEEE Intelligent Systems.

[20]  Gaurav S. Sukhatme,et al.  Connecting the Physical World with Pervasive Networks , 2002, IEEE Pervasive Comput..

[21]  Marius Portmann,et al.  Wireless Mesh Networks for Public Safety and Crisis Management Applications , 2008, IEEE Internet Computing.

[22]  Volker Tresp,et al.  Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model , 1998, NIPS.

[23]  Edgar G. Hertwich,et al.  Evaluating the environmental impact of products and production processes: a comparison of six methods , 1997 .

[24]  HyungSeok Kim,et al.  AirScope: A Micro-Scale Urban Air Quality Management System , 2008, eScience.

[25]  Lian Yan,et al.  Predicting customer behavior in telecommunications , 2004, IEEE Intelligent Systems.

[26]  Volume Assp,et al.  ACOUSTICS. SPEECH. AND SIGNAL PROCESSING , 1983 .

[27]  Leonie Kohl,et al.  Fundamental Concepts in the Design of Experiments , 2000 .

[28]  Goldie Nejat,et al.  Self-Powered Wireless Sensor Balls for Homeland Security , 2007 .

[29]  Symeon Papavassiliou,et al.  Network intrusion and fault detection: a statistical anomaly approach , 2002, IEEE Commun. Mag..

[30]  Reinout Heijungs,et al.  The computational structure of life cycle assessment , 2002 .

[31]  T. O'Donnell,et al.  Energy scavenging for long-term deployable wireless sensor networks. , 2008, Talanta.

[32]  Daniela Rus,et al.  Model-based monitoring for early warning flood detection , 2008, SenSys '08.

[33]  C. Ireland.  Fundamental concepts in the design of experiments , 1964 .

[34]  Qi Qi,et al.  Wireless sensor networks in intelligent transportation systems , 2009, Wirel. Commun. Mob. Comput..

[35]  Insup Lee,et al.  Opportunities and Obligations for Physical Computing Systems , 2005, Computer.

[36]  Shuming Tang,et al.  ITSC 05: current issues and research trends , 2006, IEEE Intelligent Systems.

[37]  Kenneth C. Budka,et al.  Mobile responder communication networks for public safety , 2006, IEEE Communications Magazine.

[38]  Mik Lamming,et al.  SPECx: Another Approach to Human Context and Activity Sensing Research, Using Tiny Peer-to-Peer Wireless Computers , 2003, UbiComp.

[39]  Steve Wright,et al.  Intelligent spaces : the application of pervasive ICT , 2006 .