Search arXivSearch

arXiv · 2309.11310

Social interactions for a sustainable lifestyle: The design of an experimental case study

Abstract

Every day we face numerous lifestyle decisions, some dictated by habits and some more conscious, which may or may not promote sustainable living. Aided by digital technology, sustainable behaviors can diffuse within social groups and inclusive communities. This paper outlines a longitudinal experimental study of social influence in behavioral changes toward sustainability, in the context of smart residential homes. Participants are residing in the housing on campus referred to as KTH Live-In Lab, whose behaviors are observed w.r.t. key lifestyle choices, such as food, resources, mobility, consumption, and environmental citizenship. The focus is on the preparatory phase of the case study and the challenges and limitations encountered during its setup. In particular, this work proposes a definition of sustainability indicators for environmentally significant behaviors, and hypothesizes that, through digitalization of a household into a social network of interacting tenants, sustainable living can be promoted. Preliminary results confirm the feasibility of the proposed experimental methodology.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Angela Fontan, Mahsa Farjadnia, Joe Llewellyn, Cecilia Katzeff, Marco Molinari, Vladimir Cvetkovic, Karl Henrik Johansson. 2023-09-20. Social interactions for a sustainable lifestyle: The design of an experimental case study. https://arxiv.org/abs/2309.11310

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Safe learning-based control via function-based uncertainty quantification

Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by constructing uncertainty tubes that enclose the unknown function of interest, e.g., the reward and constraint functions or the underlying dynamics model, with high probability. However, existing approaches for uncertainty quantification typically rely on restrictive assumptions that encode smoothness properties of the unknown function, such as a known norm in a function space. Moreover, these methods usually struggle with discontinuities. In this paper, we model the unknown function as a random function from which independent and identically distributed realizations can be generated. We then construct uncertainty tubes via the scenario approach that hold with high probability. Our uncertainty tubes rely solely on sampled realizations and can therefore accommodate discontinuities represented by the sampling model. We integrate these uncertainty tubes into a safe Bayesian optimization algorithm with which we safely tune control parameters on a real Furuta pendulum.

eess.SY

Enhanced ShockBurst for Ultra Low-Power On-Demand Sensing

On-demand sensing requires battery-powered Internet-of-Things (IoT) and implantable medical devices to remain in deep sleep and activate wireless communication only when data transmission is required. In such systems, battery lifetime depends strongly on radio active time. This work investigates how communication architecture and physical layer (PHY) configuration influence radio active time by comparing connection-oriented Bluetooth Low Energy (BLE) with connectionless Enhanced ShockBurst (ESB) on identical BLE-compatible hardware. Under identical 2 Mbps PHY configurations, ESB reduces wake-up latency and energy consumption to approximately one-twentieth of BLE by eliminating connection establishment and maintenance overhead. Increasing the ESB PHY rate from 2 to 4 Mbps further shortens packet airtime by approximately 52% and reduces transmission energy by approximately 43%. Finally, a first-in, first-out (FIFO)-triggered implantable loop recorder prototype demonstrates that jointly optimizing communication architecture, PHY configuration, and buffered transmission enables sleep-wake operation and reduces total system power consumption by approximately 60% compared with conventional BLE operation. These results identify minimizing radio active time as a key design principle for ultra-low-power on-demand sensing and provide practical guidance for battery-powered sensing systems.

eess.SY

Receding Horizon Multi-Agent Deceptive Path Planner

Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full-horizon, end-to-end optimization for single agents, which is expensive to recompute online and difficult to scale or adapt en route. We propose a unified framework for deceptive path planning using a Boltzmann distribution, computing over short-horizon candidate trajectories within a receding-horizon loop. By param- By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation. Policies are updated locally and do not require training. The level of deception and adherence to constraints can be dynamically tuned, enabling online adaptation to changes in goals and constraints such as obstacles. This step-by-step tuning opens the door to new forms of dynamic deception. Simulation studies demonstrate the flexibility of our approach, maintaining deception while adapting to environmental and constraint updates, avoiding the recomputation required by full-horizon methods, and supporting intuitive tuning via a small set of parameters

eess.SY