A. Ahmad, Xiao Sha, M. Stanaćević, A. Athalye, P. Djurić, Samir R Das
Backscattering tags transmit passively without an on-board active radio transmitter. Almost all present-day backscatter systems, however, rely on active radio receivers. This presents a significant scalability, power and cost challenge for backscatter systems. To overcome this barrier, recent research has empowered these passive tags with the ability to reliably receive backscatter signals from other tags. This forms the building block of passive networks wherein tags talk to each other without an active radio on either the transmit or receive side. For wider functionality, accurate localization of such tags is critical. All known backscatter tag localization techniques rely on active receivers for measuring and characterizing the received signal. As a result, they cannot be directly applied to passive tag-to-tag networks. This paper overcomes the gap by developing a localization technique for such passive networks based on a novel method for phase-based ranging in passive receivers. This method allows pairs of passive tags to collaboratively determine the inter-tag channel phase while effectively minimizing the effects of multipath and noise in the surrounding environment. Building on this, we develop a localization technique that benefits from large link diversity uniquely available in a passive tag-to-tag network. We evaluate the performance of our techniques with extensive micro-benchmarking experiments in an indoor environment using fabricated prototypes of tag hardware. We show that our phase-based ranging performs similar to active receivers, providing median 1D ranging error <1 cm and median localization error also <1 cm. Benefiting from the large-scale link diversity our localization technique outperforms several state-of-the-art techniques that use active receivers.
{"title":"Enabling Passive Backscatter Tag Localization Without Active Receivers","authors":"A. Ahmad, Xiao Sha, M. Stanaćević, A. Athalye, P. Djurić, Samir R Das","doi":"10.1145/3485730.3485950","DOIUrl":"https://doi.org/10.1145/3485730.3485950","url":null,"abstract":"Backscattering tags transmit passively without an on-board active radio transmitter. Almost all present-day backscatter systems, however, rely on active radio receivers. This presents a significant scalability, power and cost challenge for backscatter systems. To overcome this barrier, recent research has empowered these passive tags with the ability to reliably receive backscatter signals from other tags. This forms the building block of passive networks wherein tags talk to each other without an active radio on either the transmit or receive side. For wider functionality, accurate localization of such tags is critical. All known backscatter tag localization techniques rely on active receivers for measuring and characterizing the received signal. As a result, they cannot be directly applied to passive tag-to-tag networks. This paper overcomes the gap by developing a localization technique for such passive networks based on a novel method for phase-based ranging in passive receivers. This method allows pairs of passive tags to collaboratively determine the inter-tag channel phase while effectively minimizing the effects of multipath and noise in the surrounding environment. Building on this, we develop a localization technique that benefits from large link diversity uniquely available in a passive tag-to-tag network. We evaluate the performance of our techniques with extensive micro-benchmarking experiments in an indoor environment using fabricated prototypes of tag hardware. We show that our phase-based ranging performs similar to active receivers, providing median 1D ranging error <1 cm and median localization error also <1 cm. Benefiting from the large-scale link diversity our localization technique outperforms several state-of-the-art techniques that use active receivers.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"24 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":"126295587","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}
Aslan B. Wong, Dongliang Tu, Ziqi Huang, Xia Chen, Lu Wang, Kaishun Wu
Strength training is essential for both physical and mental well-being. Muscular mass and strength gain can help with weight loss, balance improvement, and fall prevention. The neuromuscular connection, or mind-muscle connection, is critical for improving strength training performance. However, many fitness trackers and applications are missing a feature that allows users to track their neuromuscular workout performance. The goal is to immerse the user experience while keeping the cost and size of the healthcare device to a minimum. A wearable EEG hairband and EMG shirt are outfitted with dry and non-invasive bio-signal detecting that securely attaches to the body's surface during exercise. Participants in our study are exposed to five upper-limb free-weight exercises. The result shows that low-intensity exercise can increase upper-limp muscle contraction by over 30%, and individuals with mental effort have an average precision of 81%.
{"title":"Muscle-Mind: towards the Strength Training Monitoring via the Neuro-Muscular Connection Sensing","authors":"Aslan B. Wong, Dongliang Tu, Ziqi Huang, Xia Chen, Lu Wang, Kaishun Wu","doi":"10.1145/3485730.3492875","DOIUrl":"https://doi.org/10.1145/3485730.3492875","url":null,"abstract":"Strength training is essential for both physical and mental well-being. Muscular mass and strength gain can help with weight loss, balance improvement, and fall prevention. The neuromuscular connection, or mind-muscle connection, is critical for improving strength training performance. However, many fitness trackers and applications are missing a feature that allows users to track their neuromuscular workout performance. The goal is to immerse the user experience while keeping the cost and size of the healthcare device to a minimum. A wearable EEG hairband and EMG shirt are outfitted with dry and non-invasive bio-signal detecting that securely attaches to the body's surface during exercise. Participants in our study are exposed to five upper-limb free-weight exercises. The result shows that low-intensity exercise can increase upper-limp muscle contraction by over 30%, and individuals with mental effort have an average precision of 81%.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"359 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":"134227172","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}
Motion tracklets are the basic fragments of the track followed by a moving object and constitute various everyday motion behavior. An accurate estimation of motion tracklets in 3-D space can enable a wide range of applications, ranging from human computer interaction to medical rehabilitation. This paper presents a novel dataset for accurate 6-DoF motion tracklet estimation with the inertial sensors on commodity smartphones. The dataset consists of around 100 minutes of handheld motion with 3 predominant types of motion track-lets and accurate ground truth using the Vicon systems. With the presented dataset, we further benchmarked the trajectory estimation using a lightweight neural odometry model, showcasing how the dataset can be used while providing quantitative performance for downstream tasks. Our dataset, toolkit and source code available at https://github.com/MAPS-Lab/smartphone-tracking-dataset.
{"title":"Motion Tracklet Oriented 6-DoF Inertial Tracking Using Commodity Smartphones","authors":"Peize Li, Chris Xiaoxuan Lu","doi":"10.1145/3485730.3494116","DOIUrl":"https://doi.org/10.1145/3485730.3494116","url":null,"abstract":"Motion tracklets are the basic fragments of the track followed by a moving object and constitute various everyday motion behavior. An accurate estimation of motion tracklets in 3-D space can enable a wide range of applications, ranging from human computer interaction to medical rehabilitation. This paper presents a novel dataset for accurate 6-DoF motion tracklet estimation with the inertial sensors on commodity smartphones. The dataset consists of around 100 minutes of handheld motion with 3 predominant types of motion track-lets and accurate ground truth using the Vicon systems. With the presented dataset, we further benchmarked the trajectory estimation using a lightweight neural odometry model, showcasing how the dataset can be used while providing quantitative performance for downstream tasks. Our dataset, toolkit and source code available at https://github.com/MAPS-Lab/smartphone-tracking-dataset.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"30 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":"121423012","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}
This paper presents the results of research on the identification of moisture inside the walls of buildings with the use of non-invasive electrical impedance tomography (EIT). The novelty and contribution of this research is the development of an original algorithmic method to solve the ill posedness, inverse problem. Since the new algorithm optimizes the method for each pixel of the tomographic image, taking into account a specific measurement vector, regardless of what and how many homogeneous methods are included in the algorithm, the obtained results are more accurate than those obtained with the use of homogeneous methods. As part of the research, prototypes of the EIT tomograph and electrodes for examining walls were designed and manufactured.
{"title":"Cyber-Physical System for Collecting Data on Moisture Inside the Walls of Buildings","authors":"G. Kłosowski, T. Rymarczyk, M. Kowalski","doi":"10.1145/3485730.3492868","DOIUrl":"https://doi.org/10.1145/3485730.3492868","url":null,"abstract":"This paper presents the results of research on the identification of moisture inside the walls of buildings with the use of non-invasive electrical impedance tomography (EIT). The novelty and contribution of this research is the development of an original algorithmic method to solve the ill posedness, inverse problem. Since the new algorithm optimizes the method for each pixel of the tomographic image, taking into account a specific measurement vector, regardless of what and how many homogeneous methods are included in the algorithm, the obtained results are more accurate than those obtained with the use of homogeneous methods. As part of the research, prototypes of the EIT tomograph and electrodes for examining walls were designed and manufactured.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"63 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":"122457769","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}
Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch, Alexander J. Aved
Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.
{"title":"Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus","authors":"Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch, Alexander J. Aved","doi":"10.1145/3485730.3493684","DOIUrl":"https://doi.org/10.1145/3485730.3493684","url":null,"abstract":"Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"1 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":"122642622","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}
WiFi-based Human Gesture Recognition (HGR) becomes increasingly promising for device-free human-computer interaction. However, existing WiFi-based approaches have not been ready for real-world deployment due to the limited scalability, especially for unseen gestures. The reason behind is that when introducing unseen gestures, prior works have to collect a large number of samples and re-train the model. While the recent advance of few-shot learning has brought new opportunities to solve this problem, the overhead has not been effectively reduced. This is because these methods still require enormous data to learn adequate prior knowledge, and their complicated training process intensifies the regular training cost. In this paper, we propose a WiFi-based HGR system, namely OneFi, which can recognize unseen gestures with only one (or few) labeled samples. OneFi fundamentally addresses the challenge of high overhead. On the one hand, OneFi utilizes a virtual gesture generation mechanism such that the massive efforts in prior works can be significantly alleviated in the data collection process. On the other hand, OneFi employs a lightweight one-shot learning framework based on transductive fine-tuning to eliminate model re-training. We additionally design a self-attention based backbone, termed as WiFi Transformer, to minimize the training cost of the proposed framework. We establish a real-world testbed using commodity WiFi devices and perform extensive experiments over it. The evaluation results show that OneFi can recognize unseen gestures with the accuracy of 84.2, 94.2, 95.8, and 98.8% when 1, 3, 5, 7 labeled samples are available, respectively, while the overall training process takes less than two minutes.
{"title":"OneFi: One-Shot Recognition for Unseen Gesture via COTS WiFi","authors":"Rui Xiao, Jianwei Liu, Jinsong Han, K. Ren","doi":"10.1145/3485730.3485936","DOIUrl":"https://doi.org/10.1145/3485730.3485936","url":null,"abstract":"WiFi-based Human Gesture Recognition (HGR) becomes increasingly promising for device-free human-computer interaction. However, existing WiFi-based approaches have not been ready for real-world deployment due to the limited scalability, especially for unseen gestures. The reason behind is that when introducing unseen gestures, prior works have to collect a large number of samples and re-train the model. While the recent advance of few-shot learning has brought new opportunities to solve this problem, the overhead has not been effectively reduced. This is because these methods still require enormous data to learn adequate prior knowledge, and their complicated training process intensifies the regular training cost. In this paper, we propose a WiFi-based HGR system, namely OneFi, which can recognize unseen gestures with only one (or few) labeled samples. OneFi fundamentally addresses the challenge of high overhead. On the one hand, OneFi utilizes a virtual gesture generation mechanism such that the massive efforts in prior works can be significantly alleviated in the data collection process. On the other hand, OneFi employs a lightweight one-shot learning framework based on transductive fine-tuning to eliminate model re-training. We additionally design a self-attention based backbone, termed as WiFi Transformer, to minimize the training cost of the proposed framework. We establish a real-world testbed using commodity WiFi devices and perform extensive experiments over it. The evaluation results show that OneFi can recognize unseen gestures with the accuracy of 84.2, 94.2, 95.8, and 98.8% when 1, 3, 5, 7 labeled samples are available, respectively, while the overall training process takes less than two minutes.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"31 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":"124566290","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}
Radio Frequency (RF) energy transfer is an emerging technology to supply perpetually the new generation of internet of things devices. The proposed work shows the design and implementation of RF power transmission circuits the possible usages as a power source for batteryless devices. The realized circuits can receive power in the order of 1-10 mW depending on the distance from the transmitter, size, and antenna efficiency, allowing the deployment of these rectification circuits in any low power sensing network that requires a reliable and controllable power source. This paper will introduce and illustrate the preliminary results achieve for the work done for in a life-demo.
{"title":"RF Power Transmission: Energy Harvesting for Self-Sustaining Miniaturized Sensor Nodes","authors":"Federico Villani, Philipp Mayer, M. Magno","doi":"10.1145/3485730.3493365","DOIUrl":"https://doi.org/10.1145/3485730.3493365","url":null,"abstract":"Radio Frequency (RF) energy transfer is an emerging technology to supply perpetually the new generation of internet of things devices. The proposed work shows the design and implementation of RF power transmission circuits the possible usages as a power source for batteryless devices. The realized circuits can receive power in the order of 1-10 mW depending on the distance from the transmitter, size, and antenna efficiency, allowing the deployment of these rectification circuits in any low power sensing network that requires a reliable and controllable power source. This paper will introduce and illustrate the preliminary results achieve for the work done for in a life-demo.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"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":"124584551","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}
Range Of Motion (ROM) of joints is a key biomarker in assessing the osteokinematics of muskuloskeletal system. Goniometry of ROM is effected by orthopaedists through manual measurement using medical grade goniometers. Such measurement requires personal presence of a medical expert especially in passive goniometry. For a digital estimation of ROM of shoulder and elbow joints, smart wearables embedded with inertial sensors have been used. Although they cover a wide variety of ROM measurements, but fail for measurements made in certain planes, and require sensor specific calibration. Through this work, we aim to demonstrate a calibration-free solution for ROM estimation of shoulder and elbow joints, 'ROMeasure' which can work in any random plane of measurement with a high accuracy even at extremely slow speed of rotation greatly enhancing its practicality in a medical scenario. The demonstration includes a user wearing a smartwatch, and rotating elbow/shoulder joints. Graphs for real-time angle and rotation speed are displayed on a computer screen in real time and at the end of session, final range of motion is calculated. We believe that such a setup can be extrememly useful in a tele-health scenario, and owing to the pervasiveness of smart devices today, it can prove to be a highly convenient yet accurate solution for self-assessment. Our system has been observed to incur MAE of less than 5 degrees in meticulous experimentation performed on different subjects in multiple planes of rotation, even at a rotational speed of under 10 degrees per second.
{"title":"ROMeasure","authors":"Vivek Chandel, M. Poduval, Avik Ghose","doi":"10.1145/3485730.3492878","DOIUrl":"https://doi.org/10.1145/3485730.3492878","url":null,"abstract":"Range Of Motion (ROM) of joints is a key biomarker in assessing the osteokinematics of muskuloskeletal system. Goniometry of ROM is effected by orthopaedists through manual measurement using medical grade goniometers. Such measurement requires personal presence of a medical expert especially in passive goniometry. For a digital estimation of ROM of shoulder and elbow joints, smart wearables embedded with inertial sensors have been used. Although they cover a wide variety of ROM measurements, but fail for measurements made in certain planes, and require sensor specific calibration. Through this work, we aim to demonstrate a calibration-free solution for ROM estimation of shoulder and elbow joints, 'ROMeasure' which can work in any random plane of measurement with a high accuracy even at extremely slow speed of rotation greatly enhancing its practicality in a medical scenario. The demonstration includes a user wearing a smartwatch, and rotating elbow/shoulder joints. Graphs for real-time angle and rotation speed are displayed on a computer screen in real time and at the end of session, final range of motion is calculated. We believe that such a setup can be extrememly useful in a tele-health scenario, and owing to the pervasiveness of smart devices today, it can prove to be a highly convenient yet accurate solution for self-assessment. Our system has been observed to incur MAE of less than 5 degrees in meticulous experimentation performed on different subjects in multiple planes of rotation, even at a rotational speed of under 10 degrees per second.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"66 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":"123151297","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}
Battery-less energy-harvesting systems have widened the landscape of Internet-of-Things (IoT) applications by taking computation to hard-to-reach places. Energy-harvesting sensors are perpetual, environment-friendly, cost-effective, and maintenance-free. Despite having such lucrative characteristics, battery-powered devices hold majority share of today's IoT market, since developing energy-harvesting applications require more expert knowledge, careful implementation, and rigorous debugging than applications with stable power. In this paper, we argue that development becomes easier, faster, efficient, and scalable with a standard, re-usable, general purpose platform that ensures the platform's versatility across various application with proper balance between abstraction and accessibility in hardware and software. Such platforms would provide flexibility across both hardware and software layers, at the same time, producing reliable performance. However, realizing this design point pose several research challenges that need to be identified and addressed. We identify the limitations in existing systems, articulate the challenges and provide guidelines for the community to work towards a general purpose platform that would enable new diversified battery-less applications in the future.
{"title":"Designing a General Purpose Development Platform for Energy-harvesting Applications","authors":"Nurani Saoda, Md Fazlay Rabbi Masum Billah, Bradford Campbell","doi":"10.1145/3485730.3493366","DOIUrl":"https://doi.org/10.1145/3485730.3493366","url":null,"abstract":"Battery-less energy-harvesting systems have widened the landscape of Internet-of-Things (IoT) applications by taking computation to hard-to-reach places. Energy-harvesting sensors are perpetual, environment-friendly, cost-effective, and maintenance-free. Despite having such lucrative characteristics, battery-powered devices hold majority share of today's IoT market, since developing energy-harvesting applications require more expert knowledge, careful implementation, and rigorous debugging than applications with stable power. In this paper, we argue that development becomes easier, faster, efficient, and scalable with a standard, re-usable, general purpose platform that ensures the platform's versatility across various application with proper balance between abstraction and accessibility in hardware and software. Such platforms would provide flexibility across both hardware and software layers, at the same time, producing reliable performance. However, realizing this design point pose several research challenges that need to be identified and addressed. We identify the limitations in existing systems, articulate the challenges and provide guidelines for the community to work towards a general purpose platform that would enable new diversified battery-less applications in the future.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"1 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":"128905638","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}
The phenomenon of retroactivity describes the impact that a downstream system has on an upstream one when they are connected. From a molecular communication point of view, the effect of the signal that is back propagated between the two systems leads to a reduction of the correct amount of information that can be exchanged between the input and the output of the upstream system. In this work we propose a solution to mitigate such a negative effect. Specifically, a retroactivity suppressor is introduced, which role is that of binding to the downstream system in place of the output of the primary upstream system.
{"title":"Mitigating the Retroactivity Impact on Molecular Communications","authors":"F. Ratti, M. Magarini, Hamdan Awan","doi":"10.1145/3485730.3494044","DOIUrl":"https://doi.org/10.1145/3485730.3494044","url":null,"abstract":"The phenomenon of retroactivity describes the impact that a downstream system has on an upstream one when they are connected. From a molecular communication point of view, the effect of the signal that is back propagated between the two systems leads to a reduction of the correct amount of information that can be exchanged between the input and the output of the upstream system. In this work we propose a solution to mitigate such a negative effect. Specifically, a retroactivity suppressor is introduced, which role is that of binding to the downstream system in place of the output of the primary upstream system.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"12 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":"131224209","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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