A new generation of Human Machine Interfaces (HMI) for building automation systems is needed to allow facility managers to leverage the potential of advanced controls and diagnostics. In this paper we will describe a design process and the end product, a novel HMI prototype. This novel system is an integration of advanced algorithms, an underlying software architecture, building equipment, and the human operators that use it. Recent developments in building controls and diagnostics techniques promise to improve occupants comfort while minimizing energy consumption. Advanced diagnostics algorithms can not only detect equipment failures and anomalous behaviors but also estimate the energy and comfort impact of faults. New sophisticated control schemes regulate a building based on past and future conditions rather than a static model. They can also automatically adapt to equipment failures to maintain the highest comfort given the available resources. There are several hurdles that must be overcome to effectively deploy these techniques. The perceived algorithmic difficulty of these approaches and the absence of proper tools to leverage them create a gap between what we know is computationally possible and operators in the field. One of the biggest problems is that current Building Management Systems (BMS) are not designed to natively support these advanced capabilities. As a part of the Department of Energy (DoE) sponsored Energy Efficient Building Hub (EEBHub), a team led by UTRC prototyped a new HMI that natively supports a variety of advance features. Within the EEBHub, several academic and industrial teams are experimenting with new technologies to reduce the energy footprint of buildings. In collaboration with these teams, UTRC integrated novel diagnostic and control techniques with building automation infrastructure to better understand the possibilities of a new HMI for building applications.
Research Focus: His research expertise includes building energy efficiency, HVAC control, digital twins for modern integrated energy systems and urban building energy analysis. To complement his research, he has four years of industrial experience in a vertically-integrated electricity distribution company, demonstrating excellent project management and interpersonal skills.
What is a network today ? Easier to identify perhaps of what is not a network. From electronic communication to transportation to disease propagation to finance to social and professional connections, there is always an underlying network. Networks propagate information, spread diseases, transfer commodities, help individuals communicate and build opinions and coalitions. Some networks remain stationary for a long time, while others rapidly evolve. Multiple processes may propagate over shared networks. My research seeks to understand the science underlying various networks and design novel strategies for their utilization in various fields. Motivated by the overarching impact of networks in every aspect of our lives today, my research has focused on science, economics and security of diverse classes of networks (e.g., communication, social, transportation,power, economic) with emphasis on pricing and market economics,security, resource allocation,optimization and control of stochastic systems, distributed systemsand algorithms. A brief description of my research areas and my most recent projects follow.
Economics of resource allocation in wirelessnetworksThe advent of cognitive radio technology promises to effectively addressthe limited availability of spectrum that has been deterring the proliferation of wireless services. This is because such limitation is in fact artificial - large swaths of spectrum are under-utilized as revealed by recent measurements. Cognitive radio networks are invaluable in the elimination of this artificial shortage by providing the flexibility to users to access licensed parts of the spectrum. But, economic incentives must be in place to incentivize the license holders (primaries) to use the spectrum they have licensed in an intelligent manner, and thereby facilitate access by the rest (secondaries). More specifically, license-holders should be allowed to sell their white spaces (unused spectrum bands) in an open spectrum market, which needs to be designed while remaining cognizant of the features that distinguish spectrum trade from that of any other commodity. Radio spectrum has the distinctive feature that transmissions at neighboring locations on the same channel interfere with each other, whereas the same channel can be used at far-off locations without mutual interference. In addition, each primary who has an available white-space is uncertain about the competition he faces since he does not know the spectrum usage patterns (and therefore white-space availability) of other primaries. So, during a white-space sale, each primary must jointly select a set of mutually non-interfering locations within the region (which corresponds to an independent set in the conflict graph representing the region) at which to sell unused spectrum and the price at each location considering uncertainties in competition. We have developed pricing strategies for primaries that consider the above distinctive features. The price competition scenario turned out to be a game over graphs where classical results in game theory did not guarantee the existence of Nash equilibrium let alone its uniqueness and characterization. Yet, exploiting properties specific to spectrum, we could show that the Nash equilibrium exists, is unique, and more importantly can be explicitly characterized. We subsequently provided a framework for designing contracts for spectrum trade among primaries and secondaries. We developed ``spectrum portfolio optimization'' techniques that allow primaries to package their available spectrum in an adequate mix of spectrum stocks (contracts that provide bandwidth guarantees to secondaries) and spectrum bonds (contracts that promise only best effort service) so as to attain desired tradeoffs between risk management and profit expectancy. Moving out of the realm of competition to cooperation among wireless providers, which will be a realityprovided individual providers can significantly enhance their incentives through cooperation, we provided a framework to evaluate economic incentives for cooperation among wireless service providers. Specifically, if providers cooperateby jointly deploying and poolingtheir resources, such as spectrum and infrastructure (e.g., basestations), and agree to serve each others' customers, theiraggregate payoffs, and individual shares, maysubstantially increase through opportunistic utilization of resources.The potential of such cooperation can, however, be realized only if each provider intelligently determines who it would cooperate with, when it would cooperate, and how it would deploy and share its resources during such cooperation, so as to maximize its individual incentive.Also, developing a rational basis forsharing the aggregate payoffs is imperative for the stability of the coalitions. We showed usingcoalitional game theory that the optimum cooperation strategy,which involves the acquisition, deployment and allocation of the channels and base stations (to customers), can be computed as the solution of aconcave optimization. We next showed that the grand coalition is stable in many different settings, i.e., if all providers cooperate, there isalways an operating point that maximizes the providers' aggregatepayoff, while offering each a share that removes any incentive tosplit from the coalition. The optimal cooperation strategy and thestabilizing payoff shares can be obtained in polynomial time, byrespectively solving the primals and the duals of the above optimizations, using distributed computations and limited exchange of confidentialinformation among the providers.Foundations for malware control in mobile wireless networksMalicious self-replicating codes, known as malware, pose substantial threat to wireless networks, and the economic viability of the investments directed towards wireless infrastructure is contingent on the design of effective countermeasures. We have obtained fundamental bounds on the damages inflicted by the attack and also on the efficacy of the counter-measures, and subsequently show that the bounds in each case may be attained by distributed and easy-to-implement strategies. Our first step has been to anticipate malware hazards, and understand the threat before the attacks are actually launched. We have quantified the fundamental limits on the damage that the malware can inflict by optimally choosing its actions. Such limitations arise because the capabilities of the malware are limited by the resource constraints of wireless networks which the malware utilizes as well to propagate the contagion. Specifically, malware needs to decide how best to utilize the limited battery reserves of the host in scanning the media and transmitting packets that carry the contagion, as once the host's battery is depleted, the network incurs a cost, but so does the malware as the host can no longer be utilized to propagate the contagion and also in fulfilling its subversive ends. We formulated the maximization of the overall damage inflicted on the network as an optimal control problem, and subsequently identified structural properties of the optimal actions of the malware using Pontryagin's Maximum Principle. We showed that the malware can inflict the maximum damage by choosing simple bang-bang control functions with at most two jumps. The network can launch counter-measures through power control based quarantining strategies and also by fetching security patches that immunize the vulnerable and heal the infected hosts. Such power control strategies however deteriorate the quality of service in the network, and the transmission of patches consume valuable transmission resources such as spectrum and energy. Our work has been the first to formulate the optimal countermeasure selection as optimal control problems and identify structural properties of the optimal solutions. The optimal strategies again turn out to be bang-bang functions with at most two jumps, and should therefore be readily implementable in resource constrained wireless devices. We formulated the interactions between the attack and the defense as a dynamic game and proved that the robust defense retains its simple structures.
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