PROC. 26th INTERNATIONAL CONFERENCE ON MICROELECTRONICS (MIEL 2008), NIŠ, SERBIA , 11-14 MAY, 2008 Cognitive Sensor Networks K. Shenai and S. Mukhopadhyay Abstract - A smart sensor network that integrates latest e.g., robotics, intelligent networks, smart buildings, advances in cognitive science and artificial intelligence with low- intelligent traffic management systems, etc. We investigate power wireless sensors is presented for emerging applications in in this paper what the advances in cognitive sciences can energy, agriculture, environment, healthcare, medicine, do to improve the sorry state of affairs that is the current transportation, and defense and space. power grid. By a cognitive power grid we mean an electric power I. INTRODUCTION system in which generation, transmission, and distribution are controlled intelligently and adaptively by smart Wall street guru George Gilder says “Information distributed communication networks whose elements may quality power is one of the greatest business opportunities consist of wireless or wired sensors (e.g., power factor of our time”. However, over the last thirty years, neglect as meters, thermometers, etc.), actuators (e.g., static VAR well as lack of investment has resulted in the sorry state of compensators, variable voltage transformers, etc.), our current infrastructure for generating and delivering simulators, and other services orchestrated by a network power. Currently, over 60% of the equipments need software layer. Such grids are modulated by a distributed replacement [1]. Despite significant advances in electronics closed loop control system; apart from transforming our and information technology during this time, the North obsolete power grid into a self-managing, self-protecting, American power grid (as well as power grids in other self-healing, and survivable one, such control systems continents) still continue to use technology from the attempt to optimize the operation of a power grid with nineteenth and the early twentieth century. objectives including but not limited to minimization of line Electromechanical relays (e.g., Buchholz relays) and loss, voltage fluctuations, illegal usage, peak time load equipments are still the order of the day. On one hand, such imbalance, etc. Today cognitive power grids are still at an obsolete grid presents significant challenges in their infancy. According to Morgan Stanley [1], “Silicon transmitting power reliably and efficiently, on the other will reconfigure the grid. … will make the electric grid a hand the demands of the consumers have kept on smart, efficient, and adaptive system – just as Web and increasing for providing larger amount of energy more telecommunication systems are”. In North America alone, efficiently and reliably at a lower cost. It is to be noted that the power system market is $275 billion [1]; out of that the reliability is a main concern in managing a expansive market for electronics, communication networks, and power grid (consider the 2003 blackout). Today, energy is software for intelligently controlling the grid amounts to one of the principal driving factors of the global $81 billion. Power grids today are characterized by information economy; failure to deliver power reliably and distributed generation; one of the main reasons for this efficiently can have disastrous consequences. With the being the increasing incorporation of renewable sources of proliferation of digital and analog devices being used in energy into the grid such as (photovoltaic, biogas, etc.). mission-critical environments, the need for delivery of high Today’s power system is thus a hybrid one (see Fig. 1) quality power becomes more critical. consisting of different types of sources of energy, different Cognitive sciences will play the same role in the types of transmission systems, along with different types of future that computer and information sciences have played customers. for the last fifty years. Just as an computer system Merely replacing vintage equipment with new ones processes information, a cognitive system processes without embracing modern technology will not help meet knowledge and makes decisions based on it. A cognitive the energy challenge of today. Of course, advances in system is one that can perceive the environment and adapts power electronics over the last thirty years have resulted in to it, can make intelligent decisions based on its knowledge smart equipments that are cheaper, smaller, and more that effect changes in the environment, can self-manage, efficient than previous generation counterparts. For and self-heal. During the past twenty years the artificial example, power electronics-based transformers today are intelligence community has done a tremendous amount of not only more efficient than their previous generation research in providing cognitive capabilities to computer counterparts with copper winding, they can be programmed and communication systems. The results of that research to step up or step down with different ratios. However, are bearing fruit today in diverse areas of human endeavor, installing such next generation equipments is not only K. Shenai is with EECS Department, University of Toledo, extremely capital intensive, it also solves only a part of the OH 43606-3390, USA, E-mail: [email protected] problem. Even with such costly state-of-the-art equipment, S. Mukhopadhyay is with Utah State University, Logan, UT the grid will continue to be inefficient, unless it is 84322-4205, USA intelligently managed. Managing smartly a grid spatially as 978-1-4244-1882-4/08/$25.00 © 2008 IEEE Authorized licensed use limited to: UNIVERSITY OF NEW MEXICO. Downloaded on February 4, 2009 at 16:49 from IEEE Xplore. Restrictions apply. Cognitive Distributed Power Grid under rapidly changing environments. It is amenable to dynamic reconfiguration in response to changing requirements without incurring any grid downtime. It is integrated with state-of-the-art provenance management techniques to prevent false triggers of actuating devices. It Biomass uses state-of-the-art data structures from distributed Electrical computing to ensure scalability over an expansive grid. Our Energy Renewable/ Load- Storage approach drastically reduces the hardware cost almost by a Alternate Regulated factor of 10. Energy Electrical Wind to Power & Figure 2 describes a sample scenario for controlling Electric Power Residential the power factor and voltage fluctuation in a power grid. Energy Management Commercial Low power factors can result in increased losses while Conversion Automotive Military voltage fluctuations can damage equipments. Voltage and Solar Space … power factor across the power grid are sampled by installing power factor meters and voltmeters distributed Unregulated Electrical Power spatially across the grid. These meters locally report to motes running cognitive agents that make intelligent Fig. 1. The Cognitive Power Grid control decisions based on the data. Motes can communicate among each other as well as with control expansive as the North American power grid calls for stations. In case the power factor is significantly below integration of state-of-the-art sensing and networking unity in a particular area, the control action might be technologies with cognitive capabilities. switching on a static VAR compensator (capacitive or We present a novel distributed wireless sensor inductive depending on whether the power factor is network-based control system to intelligently and reliably lagging or leading) to correct the power factor. This action manage the operation of large power grids. Our controller is actuated through a driver. Switching on a static VAR combines techniques from cognitive sciences with state-of- compensator may result in voltage fluctuations (with the-art distributed information fusion and networking voltage increasing for a capacitive compensator and technologies. It integrates intelligent sensor coordination decreasing for an inductive compensator). The cognitive and data fusion techniques to access, retrieve, process, and agent, in response to such data from the local voltmeter, communicate with disparate wireless sensors in an ad-hoc will order automatic switching on of a transformer (step up manner to deliver dynamic decisions and provide adequate or step down) depending on the situation. Depending on the information management. It provides formal guarantees stability characteristics of the grid, the transient effects will that the policies and requirements of the customer will be wear down over time resulting in stabilization of voltage met and QoS (Quality of Service) guarantees such as and power factor at the respective set points. security, fault-tolerance, timeliness, etc. be respected even Mote Intelligent Agent Power Factor Meter Volt Meter Balance Load to Minimize Loss Power Grid Fig. 2. A Power Factor and Voltage Control Scenario Authorized licensed use limited to: UNIVERSITY OF NEW MEXICO. Downloaded on February 4, 2009 at 16:49 from IEEE Xplore. Restrictions apply. II. RELATED WORK magnetic induction receiver chip as shown in Figure 3. The net result is a cost reduction from $94 to $10 per mote Significant research has been performed in massively and subsequent high-level affordability. The Java Smart distributed environment-aware computing (also known as Card [7] can be thought of as a programmable intelligent “swarm computing” [2, 3]), in particular for creating and “plastic card” with an embedded processor on which it is reasoning about swarm