Simone Silvestri, Ph.D.


Current Funded Projects

Sustainable precision dairy farming: Bridging animal welfare and stakeholder concerns about the use of precision dairy technologies

Role: Co-PI
Period: Jan 2021 - May 2025
Collaborators: J. Costa, L. Eckelkamp, S. Schexnayder
Amount Awarded: $1,000,000

Project Description:

Good animal welfare is paramount to the dairy industry, including producers, processors, distributors, and cooperatives. The development of a new, accurate, and remote welfare assessment benchmark using validated multi-variable precision dairy technologies (PDTs) has the potential to increase the sustainability of the dairy industry. PDTs allow for real-time, continuous recording of animal behavior and other animal-based outcomes at the individual animal level. Before these technologies can be useful in assessing animal welfare, predictive models and validations must first be done. Additionally, although technology may be useful to identify animal welfare concerns onfarm, dairy producers must be willing to adopt these technologies, see value and trust in these tools, and interpret the data. Concurrently, there is a risk that investment in and adoption of novel technologies may be futile if these technologies are ultimately rejected by society. Therefore, the public must be engaged to establish which aspects of these technologies may generate social acceptance or concern. Thus, our proposed integrated research and extension project aims to bridge the use of PDTs with the social aspects of animal welfare. We will develop models and validate the use of multiple, integrated technologies to predict animal welfare assessment outcomes that can be monitored remotely while simultaneously engaging dairy producers and the public in two-way conversations about the role of these technologies on-farm. Our multidisciplinary project will integrate scientific assessments of animal welfare, artificial intelligence, machine learning, dairy production knowledge, and social science to provide practical recommendations for the sustainable use of PDT on-farm.

SCC-IRG Track 2: Smart Integrated Farm Network for Rural Agricultural Communities (SIRAC)

Role: Co-PI
Funding Agency: NSF SCC
Period: Oct 2020 - Sept 2023
Collaborators: Asheesh Singh, Sajal Das, Corinne Valdivia, Peter Kyveryga
Amount Awarded: $1,515,830 (including $16,000 of REU supplements)

Project Description:

With a goal to form a community of Smart and Connected Farms (SCFs), this innovative project aims to establish a Smart Integrated Farm Network for Rural Agricultural Communities (SIRAC). The goal is to improve timely data sharing and knowledge exchange among farmers community for coordinated responses to production threats (weed, disease, insect, pest, weather), ensuring profitability. The project will develop a flexible, scalable and efficient communication infrastructure for SCFs; provide privacy-preserving data analytics across farms for community-level decision-making; establish community of practice to facilitate learning and feedback among farmers/scientists, trusted data and technology acceptance; and demonstrate economic benefit to SCFs.

The novelty of SIRAC project lies in the holistic integration of multiband dynamic spectrum access (DSA) technology for rural connectivity and community decision-making, with social translational research to address adoptability, trust, and risk preferences, and economics research to benefit farmers. Mobile crowd sensing will improve trustworthiness and decision accuracy of information spread. Fundamental contributions involve the development of novel routing algorithms, privacy-preserving machine learning techniques, and affordable communication infrastructure with unlicensed multiple spectrum bands to create SCF community for efficient data sharing. Translational research model with behavioral experiments will identify social and economic incentives for farmers/stakeholders leading to technological innovations.

The SIRAC framework has the potential to provide tremendous impacts, as it can be adapted and replicated in different rural areas. The proposed (rural) communications technology will apply to a broad range of smart and connected communities. The assessment of social and economic incentives for farmers and other stakeholders will facilitate participation in SCF network. The project will motivate next generation of scientists and farmers to contribute to smart agricultural communities with innovative solutions. Results will be disseminated via web lecture series, invited talks, a dedicated project website, participants and other communities, extension field days, conferences and workshops, and peer-reviewed journals.

The SIRAC repository will maintain computational codes, models, real world and simulation data, experimental results for two years after the project period is over. The project website will provide two levels of access: Public and Login Required. An account can be created via registration with no charge. Additionally, developed codes and simulation models will be available to the community through the Iowa State University?s digital repository. Permission will be granted to freely use and distribute the anonymized data from simulations and field experiments with due acknowledgement of the copyright notice and the authors.

NSF CPS - Faculty Early Career Development Program (CAREER): Energy Management for Smart Residential Environments through Human-in-the-loop Algorithm Design

Role: PI
Funding Agency: NSF CPS
Period: 2020-2025
Amount Awarded: $560,987 - including $44,000 REU supplement

Project Description:

While substantial progress has been made in the control of electric grid considering the cyber and physical characteristics, there has been a gap in the integration of smart grid research as it integrates with human behavior -- especially in interactions with energy management systems. For example residential energy consumption has been rapidly increasing during the last decades, especially in the U.S. where 2.6 trillion kilowatt-hours were consumed during 2015, and an additional 13.5% increase is expected by 2040 . Research efforts such as demand response have been made to reduce this consumption especially in smart residential environments. Concepts such as demand response have largely overlooked the complexity of human behaviors and perceptions, and recent research in the social-science domain and recent experience has challenged the effectiveness of this approach and in some instances led to an abandonment and avoidance of such concepts. The objective of this proposal is to overcome the limitations associated with state-of-the-art energy management systems by designing novel algorithms, machine learning models, and optimization techniques that specifically consider user behaviors, perceptions, and psychological processes. This revolutionary approach will unleash the full potential of smart residential environments in reducing residential energy consumption and has the potential to transform the way in which energy management systems are designed, implemented, and used by people. This project also supports innovative educational activities such as classes, real time demonstrations, coding challenges, and research experiences for high school students. The PI will also lead a cohort of students to the diversity-oriented Grace Hopper conference and teach seminars for Hispanic elementary students. Finally, a new class on Cyber-Physical-Human System will be designed and several graduate and undergraduate students will participate in the research activities.

The proposed research combines novel algorithmic, machine learning, and optimization solutions that consider previously un-examined human behaviors, perceptions, and psychological processes. Specifically, in order to enable fine grained energy monitoring, we propose novel stream-based appliance recognition algorithms for smart outlets. These algorithms learn the appliance consumption signatures and the user engagement with the system to optimize the learning process. In addition, energy saving optimization strategies are designed by considering the user perception through social-behavioral well-being models. These models learned and refined through novel machine learning algorithms based on regressograms, interpolation, and regression using user feedback provided through a smartphone. In addition, we develop optimization algorithms for energy exchange in the context of smart residential environments equipped with renewable energy generation. These algorithms match the users' demand and production, by considering and learning also their availability and preferences in the energy exchange process. The proposed research is validated through real testbeds and large-scale simulations based on real traces.

NSF EPCN - Collaborative Research: Crosslayer Optimization of Energy and Cost through Unified Modeling of User Behavior and Storage in Multiple Buildings

Role: PI
Funding Agency: NSF EPCN
Period: 2019-2024
Amount Awarded: $364,340 - including $31,000 REU supplements

Project Description:

The goal of this collaborative proposal is to develop novel machine learning based algorithms to address the problem of energy optimization at the building and district levels. These algorithms are integrated within a simulation framework that combines user behavior with the collaboration between buildings equipped with photovoltaic arrays, energy storage systems, and smart grid meters. The project proposes and integrates, within the same software tool, novel machine learning models for complex user behavior at the individual building level, for energy load prediction and energy storage systems scheduling at the district level, and for cost reduction via energy peak spreading. These models are used to formulate and construct algorithmic solutions based on reinforcement learning, recurrent and deep neural networks, and deep reinforcement learning suitable for implementation in the future generation Virtual Power Plants. The methodologies employed for energy reduction and cost minimization include: 1) alter user behavior through personalized recommendations regarding changes in the appliance states (e.g., heating and air conditioning settings), 2) district-level scheduling of energy storage systems among buildings equipped with photovoltaic arrays and smart grid meters, and 3) building-level scheduling of energy consumption events for smart appliances equipped with smart Internet-of-Things controllers to take benefit of different energy prices.

NIFA - 2017-67008-26145 - NSF CPS Synergy - Integration of Social Behavioral Modeling for Smart Environments to Improve the Energy Efficiency of Smart Cities

Role: PI
Period: 02/2017-02/2021
Amount Awarded: $802,981

Project Description:

Smart energy management is at the core of future smart cities, since energy profoundly impacts the city's livablity, workability and sustainability. Key building blocks for smart energy management are intelligent residential environments, generally termed smart homes. These homes will include a plethora of smart interconnected appliances, realized through the Internet of Things paradigm, which can improve residential energy efficiency by controlling the energy usage.
This research aims at designing previously unexamined social behavioral models involved in the human interaction with both smart appliances and smart energy management systems. Based on these models, we make use of graph theory to design formal user models that enable algorithm design and optimization. In addition, we propose machine learning techniques to correlate social behavioral dimensions to quantitative metrics observable by smart devices as well as algorithms that use this correlation to refine the user model. The formal models are used to design social-behavioral aware efficient algorithms for energy optimization for individual smart homes, as well as for communities of multiple homes in a microgrid.

NSF 1545037 - CPS: Breakthrough: Collaborative Research: Securing Smart Grid by Understanding Communications Infrastructure Dependencies

Role: Co-PI
Funding Agency: NSF CPS
Period: 09/2015-08/2020
Collaborators: Sajal Das (MS&T), Mariesa Crow (MS&T)
Amount Awarded: $310,476

Project Description:

Smart grid includes two interdependent infrastructures: power transmission and distribution network, and the supporting telecommunications network. Complex interactions among these infrastructures lead to new pathways for attack and failure propagation that are currently not well understood. This innovative project takes a holistic multilevel approach to understand and characterize the interdependencies between these two infrastructures, and devise mechanisms to enhance their robustness.

Specifically, the project has four goals. The first goal is to understand the standardized smart grid communications protocols in depth and examine mechanisms to harden them. This is essential since the current protocols are notoriously easy to attack. The second goal is to ensure robustness in state estimation techniques since they form the basis for much of the analysis of smart grid. In particular, the project shall exploit a steganography-based approach to detect bad data and compromised devices. The third goal is to explore trust-based attack detection strategies that combine the secure state estimation with power flow models and software attestation to detect and isolate compromised components. The final goal is to study reconfiguration strategies that combine light-weight prediction models, stochastic decision processes, intentional islanding, and game theory techniques to mitigate the spreading of failures and the loss of load. A unique aspect of smart grid security that will be studied in this project is the critical importance of timeliness, and thus a tradeoff between effectiveness of the mechanisms and the overhead introduced. The project is expected to provide practical techniques for making the smart grid more robust against failures and attacks, and enable it to recover from large scale failures with less loss of capacity. The project will also train students in the multidisciplinary areas of power systems operation and design, networking protocols, and cyber-physical security.

Past Projects

NATO SPS G4936 - Hybrid sensor networks for emergency critical scenarios

Role: Co-Director
Period: 2015-2019
Collaborators: Novella Bartolini (Sapienza University of Rome), Ala' Khalifah (Jordan German University)
Amount Awarded: 400'000 Euro

Project Description:

This project considers Hybrid Sensor Networks, composed by static, terrestrial an aerial sensors, to address on-demand, event-driven deployments, where complete and prompt coverage of the event area is required. In particular, the project designs new algorithms and protocols for event driven deployment. In addition, it defines new mission assignment algorithms to let devices autonomously coordinate with each other to perform several sensing tasks. Furthermore, the project investigates path planning strategies for unmanned aerial vehicles to recharge static sensors using radio waves. Finally, it designs algorithms for context assessment, situation awareness, and prediction of environmental changes.

Autonomous Monitoring of Large Scale Agricultural Plants Through Unmanned Aerial Vehicles

Role: PI
Period: 2015-2016
Amount Awarded: $71,055

Project Description:

This proposal investigates the use of Unmanned Aerial Vehicles (UAVs) to monitor how crops are impacted by climate change and drought. The use of this technology is often prevented by its high cost. In this proposal, for the first time, we propose a framework to optimize the tradeoff between the monitoring accuracy provided by an UAV network, and its cost. The results of this project will enable the wide spread adoption of UAVs to autonomously and accurately monitor large scale crop fields.

Analysis and recovery of large-scale failures in interdependent networks

Role: PI
Period: 2014-2015
Collaborators: Thomas La Porta (Pennsylvania State University), Ananthram Swami (Army Research Lab)
Amount Awarded: $162,540

Project Description:

This project proposes general models and tools to analyze and predict the spreading of large scale failures in interdependent networks. The proposed models are mapped to real network instances such as the Internet and the Smart grid. The analysis is used to design network recovery strategies which target relevant objectives such as minimal recovery time, maximum network utility and minimum recovery cost.