Pub Date : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342839
S. Benedict, Bill Jose Sibi, Vinaya Balakrishnan
IoT, Blockchain, Cloud, and other ICT technologies have more to offer to overcome the ongoing economic crisis of the rubber industry and reflect the adequate regard for boosting the economy of manufacturers. This paper proposes an IoT-Blockchain enabled Yield Advisory System (IBEYAS) for natural rubber manufacturers. IBEYAS connects IoT-enabled sensors of agricultural land, assesses the yield value of rubber trees at different time intervals, and notifies the anomalies to rubber manufacturers and the associated involving participants. The anomaly record of IBEYAS advises manufacturers to opt for appropriate rubber yielding procedures. For experiments, sensors were mounted on three different agricultural locations and the blockchain network was set up at the IoT cloud research laboratory. Experimental results revealed how IBEYAS recorded the anomalies after the entries consented from i) rubber manufacturers, ii) landowners, iii) rubber board authorities, and iv) rubber tappers; the results showcased the yield opportunities suggested by IBEYAS to the rubber manufacturers.
{"title":"IoT-Blockchain Enabled Yield Advisory System (IBEYAS) for Rubber Manufacturers","authors":"S. Benedict, Bill Jose Sibi, Vinaya Balakrishnan","doi":"10.1109/ANTS50601.2020.9342839","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342839","url":null,"abstract":"IoT, Blockchain, Cloud, and other ICT technologies have more to offer to overcome the ongoing economic crisis of the rubber industry and reflect the adequate regard for boosting the economy of manufacturers. This paper proposes an IoT-Blockchain enabled Yield Advisory System (IBEYAS) for natural rubber manufacturers. IBEYAS connects IoT-enabled sensors of agricultural land, assesses the yield value of rubber trees at different time intervals, and notifies the anomalies to rubber manufacturers and the associated involving participants. The anomaly record of IBEYAS advises manufacturers to opt for appropriate rubber yielding procedures. For experiments, sensors were mounted on three different agricultural locations and the blockchain network was set up at the IoT cloud research laboratory. Experimental results revealed how IBEYAS recorded the anomalies after the entries consented from i) rubber manufacturers, ii) landowners, iii) rubber board authorities, and iv) rubber tappers; the results showcased the yield opportunities suggested by IBEYAS to the rubber manufacturers.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115565652","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342782
S. Sharma, Dharmendra Dixit, K. Deka
Molecular communication (MC) can play an indispensable role in nanonetworks and Internet of Bio-nano Things based applications. However, inter-symbol interference (ISI), due to slow diffusion of molecules can severely degrade system’s performance. In this paper, we propose a deep learning (DL)-based receiver design to decode the data symbols in MC. The proposed DL-based receiver (DLR) does not require the channel state information and threshold value(s) implicitly to decode the data symbols. The DLR is trained offline by applying the data symbols generated from simulation based on diffusion channel statistics, then it is used for recovering the online transmitted data symbols directly. Impact of various system parameters such as diffusion coefficient, noise and ISI level, and frame duration are analyzed for DLR. DLR’s performance is also compared to conventional detection methods. Results show that DLR can be a viable and practical choice in MC system design.
{"title":"Deep Learning based Symbol Detection for Molecular Communications","authors":"S. Sharma, Dharmendra Dixit, K. Deka","doi":"10.1109/ANTS50601.2020.9342782","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342782","url":null,"abstract":"Molecular communication (MC) can play an indispensable role in nanonetworks and Internet of Bio-nano Things based applications. However, inter-symbol interference (ISI), due to slow diffusion of molecules can severely degrade system’s performance. In this paper, we propose a deep learning (DL)-based receiver design to decode the data symbols in MC. The proposed DL-based receiver (DLR) does not require the channel state information and threshold value(s) implicitly to decode the data symbols. The DLR is trained offline by applying the data symbols generated from simulation based on diffusion channel statistics, then it is used for recovering the online transmitted data symbols directly. Impact of various system parameters such as diffusion coefficient, noise and ISI level, and frame duration are analyzed for DLR. DLR’s performance is also compared to conventional detection methods. Results show that DLR can be a viable and practical choice in MC system design.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130181306","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 : 2020-12-14DOI: 10.1109/ants50601.2020.9342812
{"title":"ANTS 2020 Copyright Page","authors":"","doi":"10.1109/ants50601.2020.9342812","DOIUrl":"https://doi.org/10.1109/ants50601.2020.9342812","url":null,"abstract":"","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124453986","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342791
Jerin Sunny, S. Sankaran, V. Saraswat
Recent advancements in the field of in-vehicle network and wireless communication, has been steadily progressing. Also, the advent of technologies such as Vehicular Adhoc Networks (VANET) and Intelligent Transportation System (ITS), has transformed modern automobiles into a sophisticated cyber-physical system rather than just a isolated mechanical device. Modern automobiles rely on many electronic control units communicating over the Controller Area Network (CAN) bus. Although protecting the car’s external interfaces is an vital part of preventing attacks, detecting malicious activity on the CAN bus is an effective second line of defense against attacks. This paper proposes a hybrid anomaly detection system for CAN bus based on patterns of recurring messages and time interval of messages. The proposed method does not require modifications in CAN bus. The proposed system is evaluated on real CAN bus traffic with simulated attack scenarios. Results obtained show that our proposed system achieved a good detection rate with fast response times.
{"title":"A Hybrid Approach for Fast Anomaly Detection in Controller Area Networks","authors":"Jerin Sunny, S. Sankaran, V. Saraswat","doi":"10.1109/ANTS50601.2020.9342791","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342791","url":null,"abstract":"Recent advancements in the field of in-vehicle network and wireless communication, has been steadily progressing. Also, the advent of technologies such as Vehicular Adhoc Networks (VANET) and Intelligent Transportation System (ITS), has transformed modern automobiles into a sophisticated cyber-physical system rather than just a isolated mechanical device. Modern automobiles rely on many electronic control units communicating over the Controller Area Network (CAN) bus. Although protecting the car’s external interfaces is an vital part of preventing attacks, detecting malicious activity on the CAN bus is an effective second line of defense against attacks. This paper proposes a hybrid anomaly detection system for CAN bus based on patterns of recurring messages and time interval of messages. The proposed method does not require modifications in CAN bus. The proposed system is evaluated on real CAN bus traffic with simulated attack scenarios. Results obtained show that our proposed system achieved a good detection rate with fast response times.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488250","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342828
G. Rajalakshmy, Arpita Thakre
A simple configuration of defected ground structure is proposed for suppressing the H plane cross polarization in a line fed planar array. A 1x2 and a $2times 2$ array having defects in ground plane have been designed in X-band. The designs show around 10-14dB suppression in the H-plane cross polarization level with improved gain. The design and simulation process is carried out by using AWR simulator.
{"title":"Design of Defected Ground Structure in Planar Array for Cross Polarization Reduction","authors":"G. Rajalakshmy, Arpita Thakre","doi":"10.1109/ANTS50601.2020.9342828","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342828","url":null,"abstract":"A simple configuration of defected ground structure is proposed for suppressing the H plane cross polarization in a line fed planar array. A 1x2 and a $2times 2$ array having defects in ground plane have been designed in X-band. The designs show around 10-14dB suppression in the H-plane cross polarization level with improved gain. The design and simulation process is carried out by using AWR simulator.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231404","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342820
Mukesh Madanan, A. Venugopal, Nitha C. Velayudhan
The Network Intrusion Detection based on Anomaly is one of the best ways to identify the spam users and activities in cyber security. In present era, the Intrusion Detection System resources are increased due to inappropriate features that effect the detection rate of systems. To ensure better detection rate, a feature selection approach is utilized for the elimination of dissimilar and unemployable features in Intrusion Detection Systems. In addition, the time-consuming for the detection process also needs to be augmented for the process of classification. The paper introduces a method that avails the IWD algorithm for the feature subset selection in conjunction with LSTM to predict the malicious activity on that network. KDD CUP’99 dataset is employed for the judgement of performance on the intrusion detection in comparison with extant techniques. The performance estimate of the proposed model with previous methodologies depicts that the intended model is prominent by means of Higher Detection Rate, Low False Alarm Rate, and time consumption.
基于异常的网络入侵检测是网络安全中识别垃圾用户和活动的最佳方法之一。当今时代，入侵检测系统的资源越来越多，由于不合适的特征影响了系统的检测率。为了保证更好的检测率，在入侵检测系统中采用特征选择的方法来消除不相似和不可使用的特征。此外，在分类过程中，检测过程的耗时也需要增加。本文介绍了一种利用IWD算法进行特征子集选择，并结合LSTM进行网络恶意活动预测的方法。采用KDD CUP ' 99数据集对入侵检测的性能进行判断，并与现有技术进行比较。使用先前的方法对所提出的模型进行性能评估，表明预期模型具有较高的检测率、较低的误报率和较低的时间消耗。
{"title":"A Hybrid Anomaly Based Intrusion Detection Methodology Using IWD for LSTM Classification","authors":"Mukesh Madanan, A. Venugopal, Nitha C. Velayudhan","doi":"10.1109/ANTS50601.2020.9342820","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342820","url":null,"abstract":"The Network Intrusion Detection based on Anomaly is one of the best ways to identify the spam users and activities in cyber security. In present era, the Intrusion Detection System resources are increased due to inappropriate features that effect the detection rate of systems. To ensure better detection rate, a feature selection approach is utilized for the elimination of dissimilar and unemployable features in Intrusion Detection Systems. In addition, the time-consuming for the detection process also needs to be augmented for the process of classification. The paper introduces a method that avails the IWD algorithm for the feature subset selection in conjunction with LSTM to predict the malicious activity on that network. KDD CUP’99 dataset is employed for the judgement of performance on the intrusion detection in comparison with extant techniques. The performance estimate of the proposed model with previous methodologies depicts that the intended model is prominent by means of Higher Detection Rate, Low False Alarm Rate, and time consumption.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116383258","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342752
Akshita Gupta, Hritik Goel, V. Bohara, A. Srivastava
The increase in demand for multimedia services and real-time applications has led to the increasing deployment of telecommunication services all over the world. However, this also necessitates the requirement for the enhanced performance in the quality of service (QoS) parameters of the network, such as throughput, packet loss rate (PLR), and end-to-end delay. In this paper, we investigate an integrated fiber-wireless (FiWi) network composed of a 10-Gigabit-capable passive optical network (XG-PON) and IEEE 802.11ac based wireless local area network (WLAN). The paper aims to enhance the throughput of the FiWi network such that each user is granted an uplink bandwidth of 100 Mbps. At the optical line terminal (OLT), the deficit dynamic bandwidth allocation (DBA) algorithm is incorporated to provide the necessary QoS at the users. It has been shown through intensive simulations that the proposed work is able to achieve an improvement in the QoS parameters like average throughput, end-to-end delay, PLR, and aggregate throughput within an acceptable range of International Telecom Union-Telecommunication Standardization Sector (ITU-T) standards.
对多媒体业务和实时应用需求的增加导致了全球范围内电信业务部署的增加。但是，这也要求提高网络的QoS (quality of service)参数的性能，如吞吐量、丢包率、端到端时延等。在本文中，我们研究了一个集成的光纤无线(FiWi)网络，该网络由一个10千兆无源光网络(XG-PON)和基于IEEE 802.11ac的无线局域网(WLAN)组成。本文旨在提高FiWi网络的吞吐量，使每个用户获得100 Mbps的上行带宽。在光线路终端(OLT)中，引入赤字动态带宽分配(DBA)算法，为用户提供必要的QoS。通过密集的模拟表明，提议的工作能够在国际电信联盟-电信标准化部门(ITU-T)标准的可接受范围内实现QoS参数(如平均吞吐量、端到端延迟、PLR和总吞吐量)的改进。
{"title":"Performance Evaluation of Integrated XG-PON and IEEE 802.11ac based EDCA Networks","authors":"Akshita Gupta, Hritik Goel, V. Bohara, A. Srivastava","doi":"10.1109/ANTS50601.2020.9342752","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342752","url":null,"abstract":"The increase in demand for multimedia services and real-time applications has led to the increasing deployment of telecommunication services all over the world. However, this also necessitates the requirement for the enhanced performance in the quality of service (QoS) parameters of the network, such as throughput, packet loss rate (PLR), and end-to-end delay. In this paper, we investigate an integrated fiber-wireless (FiWi) network composed of a 10-Gigabit-capable passive optical network (XG-PON) and IEEE 802.11ac based wireless local area network (WLAN). The paper aims to enhance the throughput of the FiWi network such that each user is granted an uplink bandwidth of 100 Mbps. At the optical line terminal (OLT), the deficit dynamic bandwidth allocation (DBA) algorithm is incorporated to provide the necessary QoS at the users. It has been shown through intensive simulations that the proposed work is able to achieve an improvement in the QoS parameters like average throughput, end-to-end delay, PLR, and aggregate throughput within an acceptable range of International Telecom Union-Telecommunication Standardization Sector (ITU-T) standards.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132196342","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342759
Pooja Singh, K. Chaitanya, Sonali, A. Dixit, V. Jain
Underwater optical wireless communication has many plausible applications, but the biggest challenge is the communication medium that is water. Water characteristic changes from place to place. Due to high absorption and scattering, link length is limited to a few meters. In this work, we increase the link length under different channel conditions. For achieving this, we use a semiconductor optical amplifier (SOA) at the receiver and error-correcting codes (ECCs), namely convolution code and turbo code, in various water types to mitigate the channel effects. SOA decreases the power required at the receiver, thus, increasing the link length for the same bit error rate (BER). However, SOA fails to perform as water quality degrades too much, such as in a turbid harbor.In contrast to this, the use of ECCs can significantly reduce the power requirement and increase the link length. We have also employed both the models to achieve the desired performance at lower power and attain more link length. Simulation is done using MATLABO. Analytical results for SOA assisted system is also plotted along with the simulation.
{"title":"Study of Performance Enhancement in Underwater Optical Wireless Communication System","authors":"Pooja Singh, K. Chaitanya, Sonali, A. Dixit, V. Jain","doi":"10.1109/ANTS50601.2020.9342759","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342759","url":null,"abstract":"Underwater optical wireless communication has many plausible applications, but the biggest challenge is the communication medium that is water. Water characteristic changes from place to place. Due to high absorption and scattering, link length is limited to a few meters. In this work, we increase the link length under different channel conditions. For achieving this, we use a semiconductor optical amplifier (SOA) at the receiver and error-correcting codes (ECCs), namely convolution code and turbo code, in various water types to mitigate the channel effects. SOA decreases the power required at the receiver, thus, increasing the link length for the same bit error rate (BER). However, SOA fails to perform as water quality degrades too much, such as in a turbid harbor.In contrast to this, the use of ECCs can significantly reduce the power requirement and increase the link length. We have also employed both the models to achieve the desired performance at lower power and attain more link length. Simulation is done using MATLABO. Analytical results for SOA assisted system is also plotted along with the simulation.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134068575","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342762
Farid Naït-Abdesselam, C. Titouna
The Internet of Things has gained considerable attention due to its potential applications in multiple domains. However, some deployment environments may be hostile and this may affect the quality of data (QoD) and alter its accuracy. In order to ensure a high level of reliability, an IoT system should be able to clean its own sensed data by discarding those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper suggests a new approach based on Artificial Neural Network (ANN). The proposed scheme can prematurely and efficiently detect outliers before forwarding them to a central processing unit. The performance of this proposed solution is validated through simulations, using a real dataset, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.
{"title":"Data Quality Improvements for Internet of Things Using Artificial Neural Networks","authors":"Farid Naït-Abdesselam, C. Titouna","doi":"10.1109/ANTS50601.2020.9342762","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342762","url":null,"abstract":"The Internet of Things has gained considerable attention due to its potential applications in multiple domains. However, some deployment environments may be hostile and this may affect the quality of data (QoD) and alter its accuracy. In order to ensure a high level of reliability, an IoT system should be able to clean its own sensed data by discarding those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper suggests a new approach based on Artificial Neural Network (ANN). The proposed scheme can prematurely and efficiently detect outliers before forwarding them to a central processing unit. The performance of this proposed solution is validated through simulations, using a real dataset, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132348858","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 : 2020-12-14DOI: 10.1109/ANTS50601.2020.9342750
N. Raj, D. Teja, B. S. Vineeth
We consider the feasibility of predicting received signal strength indicator (RSSI) map for a room environment from a single 360° RGB panoramic image of the room using deep learning (DL). We are motivated by significant applications in rapid and automated deployment of indoor wireless sensor networks. In our knowledge, this is the first work that addresses the feasibility of RSSI prediction from visual input using DL. As a first step towards this, we propose a system, Pano2RSSI, that consists of two deep neural network (DNN) based subsystems in cascade. A single RGB panoramic image of the room environment is fed as input to the first subsystem (Pano2Layout). Pano2Layout predicts the layout of the room as well as detects objects and their sizes within. This layout information is the input to the second subsystem (RSSI-Net) which predicts a 2D RSSI map for a given 2D transmitter location within the room. In this initial proposal of the system, RSSI-Net assumes that some parameters about the wireless propagation environment are fixed (such as antenna gains, path loss exponent, material permittivities.) We illustrate the end-to-end performance of Pano2RSSI and identify several challenges and possible improvements for this problem.
{"title":"Pano2RSSI: Generation of RSSI maps for a room environment from a single panoramic image","authors":"N. Raj, D. Teja, B. S. Vineeth","doi":"10.1109/ANTS50601.2020.9342750","DOIUrl":"https://doi.org/10.1109/ANTS50601.2020.9342750","url":null,"abstract":"We consider the feasibility of predicting received signal strength indicator (RSSI) map for a room environment from a single 360° RGB panoramic image of the room using deep learning (DL). We are motivated by significant applications in rapid and automated deployment of indoor wireless sensor networks. In our knowledge, this is the first work that addresses the feasibility of RSSI prediction from visual input using DL. As a first step towards this, we propose a system, Pano2RSSI, that consists of two deep neural network (DNN) based subsystems in cascade. A single RGB panoramic image of the room environment is fed as input to the first subsystem (Pano2Layout). Pano2Layout predicts the layout of the room as well as detects objects and their sizes within. This layout information is the input to the second subsystem (RSSI-Net) which predicts a 2D RSSI map for a given 2D transmitter location within the room. In this initial proposal of the system, RSSI-Net assumes that some parameters about the wireless propagation environment are fixed (such as antenna gains, path loss exponent, material permittivities.) We illustrate the end-to-end performance of Pano2RSSI and identify several challenges and possible improvements for this problem.","PeriodicalId":426651,"journal":{"name":"2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115296144","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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