This demo showcases the power delivery potential of soil-based microbial fuel cells. We build a prototype energy harvesting setup for a soil microbial fuel cell, measure the amount of power that we can harvest, and use that energy to drive an e-ink display. Microbial fuel cells are highly sensitive to environmental conditions, especially soil moisture. In near-optimal, super moist conditions our cell provides approximately 100 &mgr;W of power at around 500 mV, which is ample power over time to power our system several times a day. In sum, we find that the confluence of ever lower-power electronics and new understanding of microbial fuel cell design means that "soil-powered sensors" are now feasible. There remains, however, significant future work to make these systems reliable and maximally performant. This demo is a working copy of the system presented at LP-IoT'21 [6].
{"title":"Powering an E-Ink Display from Soil Bacteria","authors":"Gabriela Marcano, P. Pannuto","doi":"10.1145/3485730.3493363","DOIUrl":"https://doi.org/10.1145/3485730.3493363","url":null,"abstract":"This demo showcases the power delivery potential of soil-based microbial fuel cells. We build a prototype energy harvesting setup for a soil microbial fuel cell, measure the amount of power that we can harvest, and use that energy to drive an e-ink display. Microbial fuel cells are highly sensitive to environmental conditions, especially soil moisture. In near-optimal, super moist conditions our cell provides approximately 100 &mgr;W of power at around 500 mV, which is ample power over time to power our system several times a day. In sum, we find that the confluence of ever lower-power electronics and new understanding of microbial fuel cell design means that \"soil-powered sensors\" are now feasible. There remains, however, significant future work to make these systems reliable and maximally performant. This demo is a working copy of the system presented at LP-IoT'21 [6].","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123543910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile sensing applications, including human activity recognition, human-computer interaction, localization and tracking, and many more. Most existing works require substantial amounts of well-curated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. In this work, we present LIMU-BERT, a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. LIMU-BERT adopts the principle of self-supervised training of the natural language model BERT to effectively capture temporal relations and feature distributions in IMU sensor measurements. However, the original BERT is not adaptive to mobile IMU data. By meticulously observing the characteristics of IMU sensors, we propose a series of techniques and accordingly adapt LIMU-BERT to IMU sensing tasks. The designed models are lightweight and easily deployable on mobile devices. With the representations learned via LIMU-BERT, task-specific models trained with limited labeled samples can achieve superior performances. We extensively evaluate LIMU-BERT with four open datasets. The results show that the LIMU-BERT enhanced models significantly outperform existing approaches in two typical IMU sensing applications.
{"title":"LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications","authors":"Huatao Xu, Pengfei Zhou, Rui Tan, Mo Li, G. Shen","doi":"10.1145/3485730.3485937","DOIUrl":"https://doi.org/10.1145/3485730.3485937","url":null,"abstract":"Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile sensing applications, including human activity recognition, human-computer interaction, localization and tracking, and many more. Most existing works require substantial amounts of well-curated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. In this work, we present LIMU-BERT, a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. LIMU-BERT adopts the principle of self-supervised training of the natural language model BERT to effectively capture temporal relations and feature distributions in IMU sensor measurements. However, the original BERT is not adaptive to mobile IMU data. By meticulously observing the characteristics of IMU sensors, we propose a series of techniques and accordingly adapt LIMU-BERT to IMU sensing tasks. The designed models are lightweight and easily deployable on mobile devices. With the representations learned via LIMU-BERT, task-specific models trained with limited labeled samples can achieve superior performances. We extensively evaluate LIMU-BERT with four open datasets. The results show that the LIMU-BERT enhanced models significantly outperform existing approaches in two typical IMU sensing applications.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123527410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jung Wook Park, A. Hassan, Tingyu Cheng, R. Arriaga, G. Abowd
Airflow energy harvesting has attracted much attention in the community of energy harvesting, but the efforts to make it accessible for individual users have been limited. In this paper, we address this issue and demonstrate a comprehensive toolkit, Exergy, which can help and support novice users to simulate, design, and manufacture an airflow energy harvester for vehicles.
{"title":"A Simulation and Prototyping Toolkit for Airflow Energy Harvesting in Vehicles","authors":"Jung Wook Park, A. Hassan, Tingyu Cheng, R. Arriaga, G. Abowd","doi":"10.1145/3485730.3493359","DOIUrl":"https://doi.org/10.1145/3485730.3493359","url":null,"abstract":"Airflow energy harvesting has attracted much attention in the community of energy harvesting, but the efforts to make it accessible for individual users have been limited. In this paper, we address this issue and demonstrate a comprehensive toolkit, Exergy, which can help and support novice users to simulate, design, and manufacture an airflow energy harvester for vehicles.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121034893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smartphone indoor location awareness is increasingly demanded by a variety of mobile applications. The existing solutions for accurate smartphone indoor localization rely on additional devices or pre-installed infrastructure (e.g., dense WiFi access points, Bluetooth beacons). In this demo, we present EchoLoc, an infrastructure-free smartphone indoor localization system using room acoustic response to a chirp emitted by the phone. EchoLoc consists of a mobile client for echo data collection and a cloud server hosting a deep neural network for location inference. EchoLoc achieves 95% accuracy in recognizing 101 locations in a large public indoor space and a median localization error of 0.5 m in a typical lab area. Demo video is available at https://youtu.be/5si0Cq6LzT4.
{"title":"Infrastructure-Free Smartphone Indoor Localization Using Room Acoustic Responses","authors":"Dongfang Guo, Wenjie Luo, Chaojie Gu, Yuting Wu, Qun Song, Zhenyu Yan, Rui Tan","doi":"10.1145/3485730.3492877","DOIUrl":"https://doi.org/10.1145/3485730.3492877","url":null,"abstract":"Smartphone indoor location awareness is increasingly demanded by a variety of mobile applications. The existing solutions for accurate smartphone indoor localization rely on additional devices or pre-installed infrastructure (e.g., dense WiFi access points, Bluetooth beacons). In this demo, we present EchoLoc, an infrastructure-free smartphone indoor localization system using room acoustic response to a chirp emitted by the phone. EchoLoc consists of a mobile client for echo data collection and a cloud server hosting a deep neural network for location inference. EchoLoc achieves 95% accuracy in recognizing 101 locations in a large public indoor space and a median localization error of 0.5 m in a typical lab area. Demo video is available at https://youtu.be/5si0Cq6LzT4.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"168 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bluetooth Low Energy (BLE) is emerging as an Internet of Things (IoT) technology that effectively connects small devices and sensors. It can enable many smart building use cases such as automation and control, environmental condition monitoring, and indoor location services. The BLE mesh standard provides a friendship feature to support Low Power Nodes (LPNs). We demonstrate how these BLE LPNs can support communication (uplink, downlink, or bidirectional) when powered by ambient indoor light using a mini solar panel and a small capacitor for energy storage. Being batteryless, it can exhibit intermittent behaviour with periodic ON and OFF states. However, with the knowledge of the capacitor voltage, an energy-aware LPN can try to avoid the OFF state. It can delay the execution of upcoming tasks by switching to an SLEEP state (consuming minimum energy) and provide some time to recharge the capacitor. We consider an example use case of monitoring temperature and room occupancy. The mesh nodes in the network can send instructions (such as turn-on an LED or a buzzer) to the batteryless LPN that should be executed by it. We study the use-case with real experiments on the communication feasibility of an energy-aware BLE LPN in a network and characterize the capacitance behaviour by placing a 6 W light bulb at 120 cm from the solar panel.
{"title":"Energy-Aware Battery-Less Bluetooth Low Energy Device Prototype Powered By Ambient Light","authors":"A. Sultania, J. Famaey","doi":"10.1145/3485730.3493357","DOIUrl":"https://doi.org/10.1145/3485730.3493357","url":null,"abstract":"Bluetooth Low Energy (BLE) is emerging as an Internet of Things (IoT) technology that effectively connects small devices and sensors. It can enable many smart building use cases such as automation and control, environmental condition monitoring, and indoor location services. The BLE mesh standard provides a friendship feature to support Low Power Nodes (LPNs). We demonstrate how these BLE LPNs can support communication (uplink, downlink, or bidirectional) when powered by ambient indoor light using a mini solar panel and a small capacitor for energy storage. Being batteryless, it can exhibit intermittent behaviour with periodic ON and OFF states. However, with the knowledge of the capacitor voltage, an energy-aware LPN can try to avoid the OFF state. It can delay the execution of upcoming tasks by switching to an SLEEP state (consuming minimum energy) and provide some time to recharge the capacitor. We consider an example use case of monitoring temperature and room occupancy. The mesh nodes in the network can send instructions (such as turn-on an LED or a buzzer) to the batteryless LPN that should be executed by it. We study the use-case with real experiments on the communication feasibility of an energy-aware BLE LPN in a network and characterize the capacitance behaviour by placing a 6 W light bulb at 120 cm from the solar panel.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126683484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Venkata Sai Praneeth Karempudi, Shreyan Datta, Ishan G. Thakkar
Over the past two decades, the clock speed, and hence, the singlecore performance of microprocessors has already stagnated. Following this, the recent faltering of Moore's law due to the CMOS fabrication technology reaching its unavoidable physical limit has presaged daunting challenges for designing power-efficient and ultrafast microprocessors. To overcome these challenges, vigorous efforts have been made to develop new more-than-Moore technologies and architectures for computing. Among these, nanophotonic integrated circuits based computing architectures have shown revolutionary potential. Among recent demonstrations of nanophotonic circuits for computing, a polymorphic, nanophotonic ALU (PoN-ALU) carries a notable importance since it has shown very high flexibility, high speed, and low power consumption for computing. In this paper, we carry out a design space exploration of this PoN-ALU to derive new design guidelines that can help scale the speed and energy efficiency of PoNALU even further.
{"title":"Design Exploration and Scalability Analysis of a CMOS-Integrated, Polymorphic, Nanophotonic Arithmetic-Logic Unit","authors":"Venkata Sai Praneeth Karempudi, Shreyan Datta, Ishan G. Thakkar","doi":"10.1145/3485730.3494042","DOIUrl":"https://doi.org/10.1145/3485730.3494042","url":null,"abstract":"Over the past two decades, the clock speed, and hence, the singlecore performance of microprocessors has already stagnated. Following this, the recent faltering of Moore's law due to the CMOS fabrication technology reaching its unavoidable physical limit has presaged daunting challenges for designing power-efficient and ultrafast microprocessors. To overcome these challenges, vigorous efforts have been made to develop new more-than-Moore technologies and architectures for computing. Among these, nanophotonic integrated circuits based computing architectures have shown revolutionary potential. Among recent demonstrations of nanophotonic circuits for computing, a polymorphic, nanophotonic ALU (PoN-ALU) carries a notable importance since it has shown very high flexibility, high speed, and low power consumption for computing. In this paper, we carry out a design space exploration of this PoN-ALU to derive new design guidelines that can help scale the speed and energy efficiency of PoNALU even further.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131738879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Several engineering applications require reliable rotation speed measurement for their correct functioning. The rotation speed measurements can be used to enhance the machines' vibration signal analysis and can also elicit faults undetectable by vibration monitoring alone. The current state of the art sensors for rotation speed measurement are optical, magnetic and mechanical tachometers. These sensors require line-of-sight and direct access to the machine which limits their use-cases. In this demo, we showcase Cognisense, an RF-based hardware-software sensing system that uses Orbital Angular Momentum (OAM) waves to accurately measure a machine's rotation speed. Cognisense uses a novel compact patch antenna in a monostatic radar configuration capable of transmitting and receiving OAM waves in the 5GHz license-exempt band. The demo will show Cognisense working on machines with varied numbers of blades, sizes and materials. We will also present how Cognisense operates reliably in non-line-of-sight scenarios where traditional tachometers fail. We demonstrate how Cognisense works well in high-scattering scenarios and is not impacted by the material of rotor blades. Unlike optical tachometers that require one to face the machine head-on, Our demo will also show Cognisense performing reliably in the presence of a tilt angle between the system and the machine which is not possible with optical tachometers.
{"title":"Cognisense","authors":"M. Heggo, Laksh Bhatia, J. Mccann","doi":"10.1145/3485730.3492879","DOIUrl":"https://doi.org/10.1145/3485730.3492879","url":null,"abstract":"Several engineering applications require reliable rotation speed measurement for their correct functioning. The rotation speed measurements can be used to enhance the machines' vibration signal analysis and can also elicit faults undetectable by vibration monitoring alone. The current state of the art sensors for rotation speed measurement are optical, magnetic and mechanical tachometers. These sensors require line-of-sight and direct access to the machine which limits their use-cases. In this demo, we showcase Cognisense, an RF-based hardware-software sensing system that uses Orbital Angular Momentum (OAM) waves to accurately measure a machine's rotation speed. Cognisense uses a novel compact patch antenna in a monostatic radar configuration capable of transmitting and receiving OAM waves in the 5GHz license-exempt band. The demo will show Cognisense working on machines with varied numbers of blades, sizes and materials. We will also present how Cognisense operates reliably in non-line-of-sight scenarios where traditional tachometers fail. We demonstrate how Cognisense works well in high-scattering scenarios and is not impacted by the material of rotor blades. Unlike optical tachometers that require one to face the machine head-on, Our demo will also show Cognisense performing reliably in the presence of a tilt angle between the system and the machine which is not possible with optical tachometers.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123339609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hemant Rathore, Taeeb Bandwala, S. Sahay, Mohit Sewak
The tremendous increase of malicious applications in the android ecosystem has prompted researchers to explore deep learning based malware detection models. However, research in other domains suggests that deep learning models are adversarially vulnerable, and thus we aim to investigate the robustness of deep learning based malware detection models. We first developed two image-based E-CNN malware detection models based on android permission and intent. We then acted as an adversary and designed the ECO-FGSM evasion attack against the above models, which achieved more than 50% fooling rate with limited perturbations. The evasion attack converts maximum malware samples into adversarial samples while minimizing the perturbations and maintaining the sample's syntactical, functional, and behavioral integrity. Later, we used adversarial retraining to counter the evasion attack and develop adversarially superior malware detection models, which should be an essential step before any real-world deployment.
{"title":"Are CNN based Malware Detection Models Robust?: Developing Superior Models using Adversarial Attack and Defense","authors":"Hemant Rathore, Taeeb Bandwala, S. Sahay, Mohit Sewak","doi":"10.1145/3485730.3492867","DOIUrl":"https://doi.org/10.1145/3485730.3492867","url":null,"abstract":"The tremendous increase of malicious applications in the android ecosystem has prompted researchers to explore deep learning based malware detection models. However, research in other domains suggests that deep learning models are adversarially vulnerable, and thus we aim to investigate the robustness of deep learning based malware detection models. We first developed two image-based E-CNN malware detection models based on android permission and intent. We then acted as an adversary and designed the ECO-FGSM evasion attack against the above models, which achieved more than 50% fooling rate with limited perturbations. The evasion attack converts maximum malware samples into adversarial samples while minimizing the perturbations and maintaining the sample's syntactical, functional, and behavioral integrity. Later, we used adversarial retraining to counter the evasion attack and develop adversarially superior malware detection models, which should be an essential step before any real-world deployment.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122611706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, Jun Han
Tiny spy cameras hidden in everyday objects are continuing to pose severe privacy threats to the general public as these cameras are often placed in sensitive locations such as hotels and restroom stalls. Commercially available "hidden camera detectors" have high false positive rates, and existing academic works detect (but cannot localize) only a subset of hidden cameras with wireless capabilities. We overcome these limitations by proposing LAPD, a novel hidden camera detection and localization system that leverages time-of-flight (ToF) sensors on commodity smartphones. LAPD is a smartphone app that detects hidden cameras in real-time by transmitting laser signals from the ToF sensor and searching for unique signatures representing reflections from hidden camera lenses. Using computer vision and machine learning techniques, LAPD achieves significantly higher hidden camera detection rates compared to the naked eye and hidden camera detectors.
{"title":"On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras","authors":"Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, Jun Han","doi":"10.1145/3485730.3493371","DOIUrl":"https://doi.org/10.1145/3485730.3493371","url":null,"abstract":"Tiny spy cameras hidden in everyday objects are continuing to pose severe privacy threats to the general public as these cameras are often placed in sensitive locations such as hotels and restroom stalls. Commercially available \"hidden camera detectors\" have high false positive rates, and existing academic works detect (but cannot localize) only a subset of hidden cameras with wireless capabilities. We overcome these limitations by proposing LAPD, a novel hidden camera detection and localization system that leverages time-of-flight (ToF) sensors on commodity smartphones. LAPD is a smartphone app that detects hidden cameras in real-time by transmitting laser signals from the ToF sensor and searching for unique signatures representing reflections from hidden camera lenses. Using computer vision and machine learning techniques, LAPD achieves significantly higher hidden camera detection rates compared to the naked eye and hidden camera detectors.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125851206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naomi Stricker, Yingzhao Lian, Yuning Jiang, Colin N. Jones, L. Thiele
Employing energy harvesting to power the Internet of Things supports their long-term, self-sustainable, and maintenance-free operation. These energy harvesting systems have an energy management subsystem to orchestrate the flow of energy and optimize their achievable system performance. Numerous such algorithms for a single harvesting-based system have been proposed. When envisioning the joint use of multiple distributed energy harvesting nodes in a single application, the performance and behavior of the distributed system depends on the mutual energy availability and therefore energy management of all nodes. We propose to perform the energy management of multiple distributed energy harvesting nodes jointly and thus, optimize the distributed system's performance as opposed to the performance of each energy harvesting node individually. We demonstrate the novel joint optimization in a scenario with multiple energy harvesting nodes and observe that the distributed system's performance improves by 28 % compared to when each node's energy is managed individually.
{"title":"Joint Energy Management for Distributed Energy Harvesting Systems","authors":"Naomi Stricker, Yingzhao Lian, Yuning Jiang, Colin N. Jones, L. Thiele","doi":"10.1145/3485730.3493362","DOIUrl":"https://doi.org/10.1145/3485730.3493362","url":null,"abstract":"Employing energy harvesting to power the Internet of Things supports their long-term, self-sustainable, and maintenance-free operation. These energy harvesting systems have an energy management subsystem to orchestrate the flow of energy and optimize their achievable system performance. Numerous such algorithms for a single harvesting-based system have been proposed. When envisioning the joint use of multiple distributed energy harvesting nodes in a single application, the performance and behavior of the distributed system depends on the mutual energy availability and therefore energy management of all nodes. We propose to perform the energy management of multiple distributed energy harvesting nodes jointly and thus, optimize the distributed system's performance as opposed to the performance of each energy harvesting node individually. We demonstrate the novel joint optimization in a scenario with multiple energy harvesting nodes and observe that the distributed system's performance improves by 28 % compared to when each node's energy is managed individually.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122050915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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