0000000000063964
AUTHOR
Simone Silvestri
A Reinforcement Learning Approach for User Preference-aware Energy Sharing Systems
Energy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: 1) a Reinforcement Learning-based algorithm, which provides a bounded regret and 2) a more computationally efficient heuristic, named BPT- ${K}…
A Network Tomography Approach for Traffic Monitoring in Smart Cities
Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. We model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. We specifically deal with the potential presence of noisy measurements, and t…
Perceived-Value-driven Optimization of Energy Consumption in Smart Homes
Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the us…
Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning
Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a …
FIRST
Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliabil…
Social-Behavioral Aware Optimization of Energy Consumption in Smart Homes
Residential energy consumption is skyrocketing, as residential customers in the U.S. alone used 1.4 trillion kilowatt-hours in 2014 and the consumption is expected to increase in the next years. Previous efforts to limit such consumption have included demand response and smart residential environments. However, recent research has shown that such approaches can actually increase the overall energy consumption due to the numerous complex human psychological processes that take place when interacting with electrical appliances. In this paper we propose a social-behavioral aware framework for energy management in smart residential environments. We envision a smart home where appliances are int…
Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces
AbstractRecognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of su…
A Web Application for the Remote Control of Multiple Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs) are receiving an increasing attention from the research and industry community, and today they are adopted for several civilian and military applications. However, state of the art technologies are still based on a single UAV either directly controlled by the human operator or supervised through the manual definition of a flight plan. As a result, scalability is still a significant limitation for such systems, especially when large areas need to be monitored. In this paper we propose a web based application for the control of multiple UAVs. The application has three layers. The first layer allows the user to remotely submit a monitoring mission through a web …
IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility
Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction …