Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595460
Oluwatobi Oyinlola
People around the world are trending to the Internet of Things (IoT) technologies. A large number of IoT devices are installed every day to enhance the sophistication and sustainability of smart cities. Besides, a smart city needs a smart energy management system including a smart grid, smart building. Also, a smart energy distribution system is important to reduce energy and manage it efficiently. The IoT devices are installed in various buildings in the city, they use a lot of energy, and produce energy usage information. In the existing cloud system, it is difficult to analyze and transfer the data quickly, similarly impossible to receive the analysis result immediately. However, edge computing has the advantage of fast data analysis and supply analyzed results to the field. In this process, data is processed in the edge environment, where data has been collected, analyzed, and processed in the edge nodes. In this study, we presented an energy prediction model based on the edge computing technique. We used a dataset where various environmental and energy use information has been considered. Also, we have used five different Machine Learning (ML) classifiers to classify the prediction model and assess the prediction performance. This study presents an energy prediction model using various ML classifiers in an edge computing environment.
{"title":"Energy Prediction in Edge Environment for Smart Cities","authors":"Oluwatobi Oyinlola","doi":"10.1109/WF-IoT51360.2021.9595460","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595460","url":null,"abstract":"People around the world are trending to the Internet of Things (IoT) technologies. A large number of IoT devices are installed every day to enhance the sophistication and sustainability of smart cities. Besides, a smart city needs a smart energy management system including a smart grid, smart building. Also, a smart energy distribution system is important to reduce energy and manage it efficiently. The IoT devices are installed in various buildings in the city, they use a lot of energy, and produce energy usage information. In the existing cloud system, it is difficult to analyze and transfer the data quickly, similarly impossible to receive the analysis result immediately. However, edge computing has the advantage of fast data analysis and supply analyzed results to the field. In this process, data is processed in the edge environment, where data has been collected, analyzed, and processed in the edge nodes. In this study, we presented an energy prediction model based on the edge computing technique. We used a dataset where various environmental and energy use information has been considered. Also, we have used five different Machine Learning (ML) classifiers to classify the prediction model and assess the prediction performance. This study presents an energy prediction model using various ML classifiers in an edge computing environment.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123081348","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}
Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595396
Sebastián Eraso-Misnaza, Juan G. Eraso-Trejo, Darío F. Fajardo-Fajardo, C. A. Viteri-Mera
The Internet of Things (IoT) has evolved over the past decade and is now deployed in a myriad of applications such as environmental variable sensing, agriculture, healthcare, smart homes, and smart cities, to name just a few. In this paper, we leverage a bike-share IoT network to measure RF path-loss over a wide area within a city. Compact sensors are attached to the bikes, providing real-time measurements of several variables, including received RF power. Our methodology can be replicated to measure air quality indicators, traffic, or any other variables that can be sensed in compact modules mounted on bikes. Using this system, we perform a statistical characterization of path-loss for LoRa at a frequency of 905.3 MHz. The measurement campaign took place in Pasto, Colombia, a mountainous city in the Andes mountains with medium-size buildings and narrow streets. We found that the path-loss exponent in this environment is 2.4, with a standard deviation of 7.9 dB. Cell radius for LoRa gateways under this propagation conditions is 410 m for 5% outage and 729 m for 10% outage. This path-loss characterization can be used for initial design and for optimization of LoRa IoT networks.
{"title":"Statistical Characterization of Path-Loss for 900 MHz LoRa Using a Bike-Share IoT Network","authors":"Sebastián Eraso-Misnaza, Juan G. Eraso-Trejo, Darío F. Fajardo-Fajardo, C. A. Viteri-Mera","doi":"10.1109/WF-IoT51360.2021.9595396","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595396","url":null,"abstract":"The Internet of Things (IoT) has evolved over the past decade and is now deployed in a myriad of applications such as environmental variable sensing, agriculture, healthcare, smart homes, and smart cities, to name just a few. In this paper, we leverage a bike-share IoT network to measure RF path-loss over a wide area within a city. Compact sensors are attached to the bikes, providing real-time measurements of several variables, including received RF power. Our methodology can be replicated to measure air quality indicators, traffic, or any other variables that can be sensed in compact modules mounted on bikes. Using this system, we perform a statistical characterization of path-loss for LoRa at a frequency of 905.3 MHz. The measurement campaign took place in Pasto, Colombia, a mountainous city in the Andes mountains with medium-size buildings and narrow streets. We found that the path-loss exponent in this environment is 2.4, with a standard deviation of 7.9 dB. Cell radius for LoRa gateways under this propagation conditions is 410 m for 5% outage and 729 m for 10% outage. This path-loss characterization can be used for initial design and for optimization of LoRa IoT networks.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115211663","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}
Pub Date : 2021-06-14DOI: 10.1109/wf-iot51360.2021.9595622
Ozen Ozkaya, Berna Ors
{"title":"System-Level, Model-Based Power Estimation of IoT Nodes","authors":"Ozen Ozkaya, Berna Ors","doi":"10.1109/wf-iot51360.2021.9595622","DOIUrl":"https://doi.org/10.1109/wf-iot51360.2021.9595622","url":null,"abstract":"","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115221852","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 plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%.
{"title":"Time-Distributed Feature Learning in Network Traffic Classification for Internet of Things","authors":"Yoga Suhas Kuruba Manjunath, Sihao Zhao, Xiao-Ping Zhang","doi":"10.1109/WF-IoT51360.2021.9595307","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595307","url":null,"abstract":"The plethora of Internet of Things (IoT) devices leads to explosive network traffic. The network traffic classification (NTC) is an essential tool to explore behaviours of network flows, and NTC is required for Internet service providers (ISPs) to manage the performance of the IoT network. We propose a novel network data representation, treating the traffic data as a series of images. Thus, the network data is realized as a video stream to employ time-distributed (TD) feature learning. The intra-temporal information within the network statistical data is learned using convolutional neural networks (CNN) and long short-term memory (LSTM), and the inter pseudo-temporal feature among the flows is learned by TD multi-layer perceptron (MLP). We conduct experiments using a large data-set with more number of classes. The experimental result shows that the TD feature learning elevates the network classification performance by 10%.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116043176","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}
Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595145
Julian Dreyer, Marten Fischer, R. Tönjes
The Near Field Communication (NFC) technology has experienced a steep rise in popularity due to new advances in contactless payment or virtual public transport tickets on mobile devices. Though, NFC can also be used to exchange arbitrary data between two devices within close distance. This aspect is inherently useful to prove physical access, e.g. during authentication. Modern wireless technologies such as Wi-Fi or Bluetooth 5.0 also use NFC for their pairing schemes. However, there does not exist any approach towards an NFC supported authentication scheme for digital signatures. This paper proposes a novel approach to authentically exchange public keys with the aid of NFC. Using said technique allows the key exchanging parties to prove their authenticity to each other, by exploiting the close and limited wireless communication distance of NFC. Using the proposed algorithm scalable, authentic and cost-effective sensor networks can be built, without compromising the security of the exchanged keys. With the proposed NFC challenge-response scheme, the public key of the sender can be transferred without any third party being able to smuggle in their own public key. Following the proposed scheme, any attempts to exchange unauthentic keys can be directly identified and consequently rejected. The proof-of-concept example shows, that the algorithm allows for dynamically adding of new sensors as well as an authentic communication between the gateway and the sensor devices.
{"title":"NFC Key Exchange - A light-weight approach to authentic Public Key Exchange for IoT devices","authors":"Julian Dreyer, Marten Fischer, R. Tönjes","doi":"10.1109/WF-IoT51360.2021.9595145","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595145","url":null,"abstract":"The Near Field Communication (NFC) technology has experienced a steep rise in popularity due to new advances in contactless payment or virtual public transport tickets on mobile devices. Though, NFC can also be used to exchange arbitrary data between two devices within close distance. This aspect is inherently useful to prove physical access, e.g. during authentication. Modern wireless technologies such as Wi-Fi or Bluetooth 5.0 also use NFC for their pairing schemes. However, there does not exist any approach towards an NFC supported authentication scheme for digital signatures. This paper proposes a novel approach to authentically exchange public keys with the aid of NFC. Using said technique allows the key exchanging parties to prove their authenticity to each other, by exploiting the close and limited wireless communication distance of NFC. Using the proposed algorithm scalable, authentic and cost-effective sensor networks can be built, without compromising the security of the exchanged keys. With the proposed NFC challenge-response scheme, the public key of the sender can be transferred without any third party being able to smuggle in their own public key. Following the proposed scheme, any attempts to exchange unauthentic keys can be directly identified and consequently rejected. The proof-of-concept example shows, that the algorithm allows for dynamically adding of new sensors as well as an authentic communication between the gateway and the sensor devices.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122817268","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}
Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595651
Dylan Wheeler, B. Natarajan
With millions of connected devices expected to proliferate across multiple application domains, energy efficiency is a critical factor in IoT solutions. This paper aims to enhance the energy efficiency of networked IoT sensors by transitioning to a header-free communication framework. Novel enhancements to the reception technique based on the stochastic expectation maximization algorithm are proposed. Specifically, in contrast to prior efforts, a combination of compressive sensing principles along with deep learning methodologies are used to improve the performance of header-free sensor communications. Using simulation results, performance & complexity gains relative to the classic approach of up to 95% and 99%, respectively, are achieved.
{"title":"Enabling Energy-Efficient IoT via Learning Assisted Header-Free Communication","authors":"Dylan Wheeler, B. Natarajan","doi":"10.1109/WF-IoT51360.2021.9595651","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595651","url":null,"abstract":"With millions of connected devices expected to proliferate across multiple application domains, energy efficiency is a critical factor in IoT solutions. This paper aims to enhance the energy efficiency of networked IoT sensors by transitioning to a header-free communication framework. Novel enhancements to the reception technique based on the stochastic expectation maximization algorithm are proposed. Specifically, in contrast to prior efforts, a combination of compressive sensing principles along with deep learning methodologies are used to improve the performance of header-free sensor communications. Using simulation results, performance & complexity gains relative to the classic approach of up to 95% and 99%, respectively, are achieved.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116752920","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}
Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9596024
Jiye Park, Dongha Lee, G. Maierbacher
The possibility to perform remote, yet secure firmware updates in Internet of Things (IoT) scenarios is relevant for a broad spectrum of applications. Secure multicast communication is of particular relevance as a large firmware file needs to be distributed over a constrained, unreliable and potentially insecure network to a large number of constrained devices. In this work, we propose a lightweight method for source authentication that is suitable for such scenarios. The main idea is to use a reverse sequence hash chain of the entire packets which only the legitimated sender knows. With one time signature verification, receivers in the group can authenticate the origin of each packet and can check the integrity. We show how the proposed scheme can be integrated with the Constrained Application Protocol (CoAP). In order to underline the capabilities of our proposed solution, we provide security evaluation results, and we demonstrate its practicability and effectiveness by means of hardware experiments.
{"title":"Reverse Sequence Hash Chain based Multicast Authentication for IoT Firmware Updates","authors":"Jiye Park, Dongha Lee, G. Maierbacher","doi":"10.1109/WF-IoT51360.2021.9596024","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9596024","url":null,"abstract":"The possibility to perform remote, yet secure firmware updates in Internet of Things (IoT) scenarios is relevant for a broad spectrum of applications. Secure multicast communication is of particular relevance as a large firmware file needs to be distributed over a constrained, unreliable and potentially insecure network to a large number of constrained devices. In this work, we propose a lightweight method for source authentication that is suitable for such scenarios. The main idea is to use a reverse sequence hash chain of the entire packets which only the legitimated sender knows. With one time signature verification, receivers in the group can authenticate the origin of each packet and can check the integrity. We show how the proposed scheme can be integrated with the Constrained Application Protocol (CoAP). In order to underline the capabilities of our proposed solution, we provide security evaluation results, and we demonstrate its practicability and effectiveness by means of hardware experiments.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117084161","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}
Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595575
Iliar Rabet, H. Fotouhi, M. Vahabi, M. Alves, M. Björkman
Routing Protocol for Low-Power and Lossy Networks (RPL) as the most widely used routing protocol for constrained Internet of Things (IoT) devices optimizes the number of routing states that nodes maintain to minimize resource consumption. Given that the routes are optimized for data collection, this leads to selecting sub-optimal routes, particularly in case of east-west or ”transversal” traffic. Additionally, RPL neglects interactions with a central entity in the network for monitoring or managing routes and enabling more flexibility and responsiveness to the system.In this paper, we present RPL with Route Projection (RPL-RP) that enables collecting siblings’ relations at the root node in order to inject routing states to the routers. This backward-compatible RPL extension still favors collection-based traffic patterns but it enriches the way routing protocol handles other flow directions. We address different advantages of RPL-RP in contrast to standard RPL and evaluate its overhead and improvements in terms of end-to-end delay, control overhead and packet delivery ratio. Overall, RPL-RP halves the end-to-end delay and increases network reliability by 5% while increasing network overhead by only 3%.
低功耗损耗网络路由协议(Routing Protocol for Low-Power and Lossy Networks, RPL)是用于受限物联网设备的最广泛的路由协议，它可以优化节点保持的路由状态数量，以最大限度地减少资源消耗。考虑到路线是为数据收集而优化的，这导致选择次优路线，特别是在东西向或“横向”交通的情况下。此外，RPL忽略了与网络中的中心实体的交互，以监视或管理路由，并为系统提供更大的灵活性和响应性。在本文中，我们提出了具有路由投影(Route Projection, RPL- rp)的RPL，它能够在根节点收集兄弟关系，从而向路由器注入路由状态。这种向后兼容的RPL扩展仍然支持基于集合的流量模式，但它丰富了路由协议处理其他流方向的方式。我们讨论了与标准RPL相比，RPL- rp的不同优势，并评估了它在端到端延迟、控制开销和数据包传送率方面的开销和改进。总体而言，RPL-RP将端到端延迟减半，将网络可靠性提高5%，而网络开销仅增加3%。
{"title":"RPL-RP: RPL with Route Projection for Transversal Routing","authors":"Iliar Rabet, H. Fotouhi, M. Vahabi, M. Alves, M. Björkman","doi":"10.1109/WF-IoT51360.2021.9595575","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595575","url":null,"abstract":"Routing Protocol for Low-Power and Lossy Networks (RPL) as the most widely used routing protocol for constrained Internet of Things (IoT) devices optimizes the number of routing states that nodes maintain to minimize resource consumption. Given that the routes are optimized for data collection, this leads to selecting sub-optimal routes, particularly in case of east-west or ”transversal” traffic. Additionally, RPL neglects interactions with a central entity in the network for monitoring or managing routes and enabling more flexibility and responsiveness to the system.In this paper, we present RPL with Route Projection (RPL-RP) that enables collecting siblings’ relations at the root node in order to inject routing states to the routers. This backward-compatible RPL extension still favors collection-based traffic patterns but it enriches the way routing protocol handles other flow directions. We address different advantages of RPL-RP in contrast to standard RPL and evaluate its overhead and improvements in terms of end-to-end delay, control overhead and packet delivery ratio. Overall, RPL-RP halves the end-to-end delay and increases network reliability by 5% while increasing network overhead by only 3%.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129768630","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}
Regression-based traffic modelling can estimate traffic congestion as a response variable by incorporating explanatory spatiotemporal components. Bayesian inference is widely used in traffic modelling as it has advantages over a frequentist approach. Previous approaches mainly focused on offsetting Bayesian inference by incorporating supervised feature extraction, data redistribution and competitive expectation-maximization techniques to achieve better accuracy in traffic forecasting. Unlike the frequentist approach, these combined Bayesian inference approaches lack interpretability. This paper proposes a simple Bayesian Linear Regression approach for spatiotemporal traffic congestion prediction that leverages Bayesian inference to facilitate model interpretability and quantify model uncertainty. The model is evaluated in terms of mean absolute error (MAE) and root mean squared error (RMSE). The experiment shows that Bayesian linear regression modelling can be trained on small data observations to quantify model uncertainty and predict traffic congestion without sacrificing interpretability and accuracy in comparison with the frequentist approach.
{"title":"A Bayesian Linear Regression Approach to Predict Traffic Congestion","authors":"Sifatul Mostafi, Taghreed Alghamdi, Khalid Elgazzar","doi":"10.1109/WF-IoT51360.2021.9595298","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595298","url":null,"abstract":"Regression-based traffic modelling can estimate traffic congestion as a response variable by incorporating explanatory spatiotemporal components. Bayesian inference is widely used in traffic modelling as it has advantages over a frequentist approach. Previous approaches mainly focused on offsetting Bayesian inference by incorporating supervised feature extraction, data redistribution and competitive expectation-maximization techniques to achieve better accuracy in traffic forecasting. Unlike the frequentist approach, these combined Bayesian inference approaches lack interpretability. This paper proposes a simple Bayesian Linear Regression approach for spatiotemporal traffic congestion prediction that leverages Bayesian inference to facilitate model interpretability and quantify model uncertainty. The model is evaluated in terms of mean absolute error (MAE) and root mean squared error (RMSE). The experiment shows that Bayesian linear regression modelling can be trained on small data observations to quantify model uncertainty and predict traffic congestion without sacrificing interpretability and accuracy in comparison with the frequentist approach.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124683014","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}
Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595931
A. Shukla, P. K. Upadhyay, Abhishek Srivastava, J. M. Moualeu
This paper aims to propose and analyze an energy-and spectrum-efficient wireless body area network (WBAN) for smart healthcare applications. On one hand, we focus on improving the spectrum utilization in WBANs by incorporating an overlay cognitive radio (CR) paradigm that allows the co-existence of various sensor nodes as primary and secondary users based on their applications i.e., medical or non-medical. On the other hand, we employ an energy harvesting (EH) based time-switching cooperation protocol through the secondary device to improve energy efficiency. We evaluate the performance of the proposed overlay CR WBAN in terms of outage probability, throughput and energy efficiency, and thereby provide practical design guidelines in IoT-based e-healthcare framework. Above all, we assess the correctness of the proposed analytical framework when compared to Monte Carlo simulations.
{"title":"Energy Harvesting-Assisted Cognitive Sensor Nodes in Wireless Body Area Networks","authors":"A. Shukla, P. K. Upadhyay, Abhishek Srivastava, J. M. Moualeu","doi":"10.1109/WF-IoT51360.2021.9595931","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595931","url":null,"abstract":"This paper aims to propose and analyze an energy-and spectrum-efficient wireless body area network (WBAN) for smart healthcare applications. On one hand, we focus on improving the spectrum utilization in WBANs by incorporating an overlay cognitive radio (CR) paradigm that allows the co-existence of various sensor nodes as primary and secondary users based on their applications i.e., medical or non-medical. On the other hand, we employ an energy harvesting (EH) based time-switching cooperation protocol through the secondary device to improve energy efficiency. We evaluate the performance of the proposed overlay CR WBAN in terms of outage probability, throughput and energy efficiency, and thereby provide practical design guidelines in IoT-based e-healthcare framework. Above all, we assess the correctness of the proposed analytical framework when compared to Monte Carlo simulations.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121711988","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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