Title: Performance of the Latest Generation Powerline Networking for Green Building Applications
Green building applications need to efficiently communicate fine-grained power consumption patterns of a wide variety of consumer-grade appliances for an effective adaptation and percolation of demand response models in the home environment. A key hurdle to the widespread adoption of such demand response policies in these appliances is the lack of efficient connectivity to a local area network. One solution is delivering telemetry data over existing electrical infrastructure to which the devices are already connected. The use of existing wiring produces a simple and cost-effective solution, avoiding many issues observed with wireless mesh networks (such as islands and bottlenecks), while helping to vacate increasingly congested spectrum. In this paper we explore the feasibility and efficacy of Power-line Communications (PLC) as a backbone of wireless communications in a home environment. We evaluate the behavior of several state-of-the art PLC modems using end-to-end measurements to establish their performance and throughput characteristics. Our preliminary results suggest that PLC is a promising technology for low-bandwidth hungry green building applications but more in depth study is required before making large-scale smart grid deployment. more »« less
Future healthcare systems require smart hospitals with system-wide wireless communications and positioning functions, which cannot be facilitated by existing radio-frequency (RF) wireless technologies. In this paper, we present integrated design of a novel low-complexity received signal strength (RSS) based hybrid visible light communication (VLC) and indoor positioning (VLP) system. This VLC/VLP tracking system consist of host optical transceivers embedded in existing light-emitting diode (LED) bulbs and user-end optical tags, which interface with the existing 120AVC power wiring in a building. The new hybrid VLC/PLC tracking system was validated by simulation and experimentation. This LED VLC tracking system will enable smart hospital operations to modernize next-generation intelligent healthcare systems
Green wireless networks Wake-up radio
Energy harvesting Routing
Markov decision process Reinforcement learning
1. Introduction
With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite.
Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption
∗ Corresponding author.
E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria).
https://doi.org/10.1016/j.comcom.2020.05.046
(sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12].
This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a
ABSTRACT
This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively.
Smart appliances’ run schedule and electric vehicles charging can be managed over a smart grid enabled home area network (HAN) to reduce electricity demand at critical times and add more plug-in electric vehicles to the grid, which eventually lower customers’ energy bills and reduce greenhouse gas emissions. Short range radio-based wireless communication technologies commonly adopted in a HAN are vulnerable to cyber attacks due to their wide interception range. In this work, a low-cost solution is proposed for securing the low-volume data exchange of sensitive tasks (e.g., key management and mutual authentication). Our approach utilizes the emerging concept of retro-reflector based visible light communication (Retro-VLC), where smart appliances, IoT sensors and other electric devices perform the sensitive data exchange with the HAN gateway via the secure Retro-VLC channel. To conduct the feasibility study, a multi-pixel Retro-VLC link is prototyped to enable quadrature amplitude modulation. The bit error rate of Retro-VLC is studied analytically, numerically and experimentally. A heterogeneous Retro-VLC + WLAN connection is implemented by socket programming. In addition, the working range, sniffing range, and key exchange latency are measured. The results validate the applicability of the Retro-VLC based solution.
Internet of Things (IoT) is driving the development of new generation of sensors, communication components, and power sources. Ideally, IoT sensors and communication components are expected to be powered by sustainable energy source freely available in the environment. Here, a breakthrough in this direction is provided by demonstrating high output power energy harvesting from very low amplitude stray magnetic fields, which exist everywhere, through magnetoelectric (ME) coupled magneto‐mechano‐electric (MME) energy conversion. ME coupled MME harvester comprised of multiple layers of amorphous magnetostrictive material, piezoelectric macrofiber composite, and magnetic tip mass, interacts with an external magnetic field to generate electrical energy. Comprehensive experimental investigation and a theoretical model reveal that both the magnetic torque generated through magnetic loading and amplification of magneto‐mechanical vibration by ME coupling contributes toward the generation of high electrical power from the stray magnetic field around power cables of common home appliances. The generated electrical power from the harvester is sufficient for operating microsensors (gyro, temperature, and humidity sensing) and wireless data transmission systems. These results will facilitate the deployment of IoT devices in emerging intelligent infrastructures.
Jia, Yiling; Batra, Nipun; Wang, Hongning; Whitehouse, Kamin(
, Proceedings of the 28th ACM International Conference on Information and Knowledge Management)
null
(Ed.)
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.
Roy, Nirmalya, Kleinschmidt, David, Taylor, Joseph, and Shirazi, Behrooz. Performance of the Latest Generation Powerline Networking for Green Building Applications. Retrieved from https://par.nsf.gov/biblio/10073262. BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings . Web. doi:10.1145/2528282.2528298.
Roy, Nirmalya, Kleinschmidt, David, Taylor, Joseph, & Shirazi, Behrooz. Performance of the Latest Generation Powerline Networking for Green Building Applications. BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, (). Retrieved from https://par.nsf.gov/biblio/10073262. https://doi.org/10.1145/2528282.2528298
Roy, Nirmalya, Kleinschmidt, David, Taylor, Joseph, and Shirazi, Behrooz.
"Performance of the Latest Generation Powerline Networking for Green Building Applications". BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings (). Country unknown/Code not available. https://doi.org/10.1145/2528282.2528298.https://par.nsf.gov/biblio/10073262.
@article{osti_10073262,
place = {Country unknown/Code not available},
title = {Performance of the Latest Generation Powerline Networking for Green Building Applications},
url = {https://par.nsf.gov/biblio/10073262},
DOI = {10.1145/2528282.2528298},
abstractNote = {Green building applications need to efficiently communicate fine-grained power consumption patterns of a wide variety of consumer-grade appliances for an effective adaptation and percolation of demand response models in the home environment. A key hurdle to the widespread adoption of such demand response policies in these appliances is the lack of efficient connectivity to a local area network. One solution is delivering telemetry data over existing electrical infrastructure to which the devices are already connected. The use of existing wiring produces a simple and cost-effective solution, avoiding many issues observed with wireless mesh networks (such as islands and bottlenecks), while helping to vacate increasingly congested spectrum. In this paper we explore the feasibility and efficacy of Power-line Communications (PLC) as a backbone of wireless communications in a home environment. We evaluate the behavior of several state-of-the art PLC modems using end-to-end measurements to establish their performance and throughput characteristics. Our preliminary results suggest that PLC is a promising technology for low-bandwidth hungry green building applications but more in depth study is required before making large-scale smart grid deployment.},
journal = {BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings},
author = {Roy, Nirmalya and Kleinschmidt, David and Taylor, Joseph and Shirazi, Behrooz},
}
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