Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840848
Priyadarshini Dwivedi, Gyanajyoti Routray, R. Hegde
Direction of arrival (DOA) estimation is still a challenging and fundamental problem in acoustic signal processing. This paper proposes a new method for DOA estimation that utilizes the support vector machine (SVM) based classification. The source signal is recorded by the spherical microphone array (SMA) and decomposed into the spherical harmonics domain. The phase and the magnitude features are calculated from the spherical harmonics (SH) decomposed signals. A multiclass support vector machine (M-SVM) algorithm is implemented to classify these phase and magnitude features to the DOA classes. Since the SVM is a non-probabilistic and deterministic model, it is computationally faster and highly reduced complexity than the neural network-based learning models. Extensive simulations are conducted for the performance evaluation of the proposed method. It is observed that the proposed model provides robust DOA estimates at various signal-to-noise ratios (SNR) and reverberation time. Performance evaluated in terms of the root mean square error (RMSE) provides interesting results motivating the use of the proposed model in practical applications.
{"title":"DOA Estimation using Multiclass-SVM in Spherical Harmonics Domain","authors":"Priyadarshini Dwivedi, Gyanajyoti Routray, R. Hegde","doi":"10.1109/SPCOM55316.2022.9840848","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840848","url":null,"abstract":"Direction of arrival (DOA) estimation is still a challenging and fundamental problem in acoustic signal processing. This paper proposes a new method for DOA estimation that utilizes the support vector machine (SVM) based classification. The source signal is recorded by the spherical microphone array (SMA) and decomposed into the spherical harmonics domain. The phase and the magnitude features are calculated from the spherical harmonics (SH) decomposed signals. A multiclass support vector machine (M-SVM) algorithm is implemented to classify these phase and magnitude features to the DOA classes. Since the SVM is a non-probabilistic and deterministic model, it is computationally faster and highly reduced complexity than the neural network-based learning models. Extensive simulations are conducted for the performance evaluation of the proposed method. It is observed that the proposed model provides robust DOA estimates at various signal-to-noise ratios (SNR) and reverberation time. Performance evaluated in terms of the root mean square error (RMSE) provides interesting results motivating the use of the proposed model in practical applications.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134167149","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840786
S. Kirubakaran, M. D. Selvaraj
Free Space Optics (FSO) is a promising choice for dealing with high data-rate applications, and it improves reliability by using RF communication as a backup link. In a practical scenario, the RF link is affected by co-channel interference. We investigate the performance of an interference-limited hybrid FSO/RF system. We consider a transmitter $(T_{X})$ and a receiver $(R_{X})$ with threshold-based switching selection at the RX. Atmospheric turbulence, path loss, and pointing errors influence the FSO link, which is modelled using the gamma-gamma distribution, whereas the RF link is modelled using the $kappa-mu$ distribution. We have derived the end-to-end symbol error rate and the outage probability for the interference limited system. Results show that interference increases the error rate and degrades the system performance. The impact of interference is high in the lower SNR’s and it is controlled by increasing the Signal to Interference Ratio (SIR) of RF link. To verify the results, Monte carlo simulations are used.
{"title":"Performance Analysis of Interference Limited Hybrid FSO/RF Systems","authors":"S. Kirubakaran, M. D. Selvaraj","doi":"10.1109/SPCOM55316.2022.9840786","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840786","url":null,"abstract":"Free Space Optics (FSO) is a promising choice for dealing with high data-rate applications, and it improves reliability by using RF communication as a backup link. In a practical scenario, the RF link is affected by co-channel interference. We investigate the performance of an interference-limited hybrid FSO/RF system. We consider a transmitter $(T_{X})$ and a receiver $(R_{X})$ with threshold-based switching selection at the RX. Atmospheric turbulence, path loss, and pointing errors influence the FSO link, which is modelled using the gamma-gamma distribution, whereas the RF link is modelled using the $kappa-mu$ distribution. We have derived the end-to-end symbol error rate and the outage probability for the interference limited system. Results show that interference increases the error rate and degrades the system performance. The impact of interference is high in the lower SNR’s and it is controlled by increasing the Signal to Interference Ratio (SIR) of RF link. To verify the results, Monte carlo simulations are used.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"503 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756190","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840829
Raju D. Kamble, K. Appaiah
This paper proposes the use of an analog biquad filter as an equalizer for PMMA based step index plastic optical fiber (POF) links. We show both theoretically and experimentally that this approach achieves 100 Mbit/s over for 100 m of fiber. The material properties of the POF channel causes significant intersymbol interference SNR degradation for long link lengths. The use of DSP based equalization, while effective, imposes significant additional complexity. We propose the design and implementation of an analog biquad filter that is tuned using fiber modeling to effectively compensate the dispersive limitations. We show experimentally that the designed filter is able to successfully overcome the dispersion limits over a large range of fiber lengths and data rates.
{"title":"Biquad filter based equalization for PMMA SI-POF links","authors":"Raju D. Kamble, K. Appaiah","doi":"10.1109/SPCOM55316.2022.9840829","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840829","url":null,"abstract":"This paper proposes the use of an analog biquad filter as an equalizer for PMMA based step index plastic optical fiber (POF) links. We show both theoretically and experimentally that this approach achieves 100 Mbit/s over for 100 m of fiber. The material properties of the POF channel causes significant intersymbol interference SNR degradation for long link lengths. The use of DSP based equalization, while effective, imposes significant additional complexity. We propose the design and implementation of an analog biquad filter that is tuned using fiber modeling to effectively compensate the dispersive limitations. We show experimentally that the designed filter is able to successfully overcome the dispersion limits over a large range of fiber lengths and data rates.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"79 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126054230","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840799
A. Sarkar, B. Dey, S. R. Pillai
We investigate the achievable probability of error in a communication setup where a single transmitter broadcasts an common message sequence to a set of receivers. The communication is aided by the availability of perfect causal feedback of all the received symbols to the encoder. Two types of schemes are considered: in the first model, the encoder and the decoders have to agree in advance on the sequence number of the intended message, whereas the decoders should also Figure out the sequence number from the received symbols in the second model. The former is called coordinated message transmission, and latter is named streaming block transmission. The challenge faced in both models is to appropriately synchronize the independent receivers, while leveraging the boost in error probability due to feedback. We propose error exponents, which are optimal for a class of broadcast channels under coordinated message transmission. We propose an achievable exponent under streaming block transmission. These results extend the best known single receiver results.
{"title":"On the Error Exponents for Common Message Broadcasting over DMCs with Feedback","authors":"A. Sarkar, B. Dey, S. R. Pillai","doi":"10.1109/SPCOM55316.2022.9840799","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840799","url":null,"abstract":"We investigate the achievable probability of error in a communication setup where a single transmitter broadcasts an common message sequence to a set of receivers. The communication is aided by the availability of perfect causal feedback of all the received symbols to the encoder. Two types of schemes are considered: in the first model, the encoder and the decoders have to agree in advance on the sequence number of the intended message, whereas the decoders should also Figure out the sequence number from the received symbols in the second model. The former is called coordinated message transmission, and latter is named streaming block transmission. The challenge faced in both models is to appropriately synchronize the independent receivers, while leveraging the boost in error probability due to feedback. We propose error exponents, which are optimal for a class of broadcast channels under coordinated message transmission. We propose an achievable exponent under streaming block transmission. These results extend the best known single receiver results.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127046004","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840765
M. Kumar, S. Sharma, K. Deka, V. Bhatia
Terahertz (THz) band communication is a promising technology for the 6G wireless networks. However, due to very high spread attenuation and molecular absorption, THz frequencies provide a limited coverage area and need novel solutions to overcome these constraints. Further, intelligent reflecting surfaces (IRS) is introduced to enhance the coverage area by reconfiguring the wireless propagation environment. Therefore, in this paper, an IRS-assisted THz system is designed and analyzed and referred to as IRS-TH system. The proposed IRS-TH significantly improves bit error rate (BER) performance by passive beamforming at the IRS panel. Impact of IRS-TH parameters such as different phase shifting methods, the position of IRS panel, and line-of-sight path is studied. Exhaustive simulation results show that the proposed IRS-TH can significantly enhance BER and sum-rate performance as compared to conventional THz system without IRS.
{"title":"Intelligent Reflecting Surface Assisted Terahertz Communications","authors":"M. Kumar, S. Sharma, K. Deka, V. Bhatia","doi":"10.1109/SPCOM55316.2022.9840765","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840765","url":null,"abstract":"Terahertz (THz) band communication is a promising technology for the 6G wireless networks. However, due to very high spread attenuation and molecular absorption, THz frequencies provide a limited coverage area and need novel solutions to overcome these constraints. Further, intelligent reflecting surfaces (IRS) is introduced to enhance the coverage area by reconfiguring the wireless propagation environment. Therefore, in this paper, an IRS-assisted THz system is designed and analyzed and referred to as IRS-TH system. The proposed IRS-TH significantly improves bit error rate (BER) performance by passive beamforming at the IRS panel. Impact of IRS-TH parameters such as different phase shifting methods, the position of IRS panel, and line-of-sight path is studied. Exhaustive simulation results show that the proposed IRS-TH can significantly enhance BER and sum-rate performance as compared to conventional THz system without IRS.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126355967","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840844
Sagnik Bhattacharya, Abhishek K. Gupta
An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an efficient convolutional neural network (CNN) based THz channel estimator that estimates the THz channel factors using uplink sub-6GHz channel. Further, we use the estimated THz channel factors to predict the optimal beamformer from a pre-given codebook, using a dense neural network. We not only get rid of the overhead associated with the conventional methods, but also achieve near-optimal spectral efficiency rates using the proposed beamformer predictor. The proposed method also outperforms deep learning based beamformer predictors accepting THz channel matrices as input, thus proving the validity and efficiency of our sub-6GHz based approach.
{"title":"Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel","authors":"Sagnik Bhattacharya, Abhishek K. Gupta","doi":"10.1109/SPCOM55316.2022.9840844","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840844","url":null,"abstract":"An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an efficient convolutional neural network (CNN) based THz channel estimator that estimates the THz channel factors using uplink sub-6GHz channel. Further, we use the estimated THz channel factors to predict the optimal beamformer from a pre-given codebook, using a dense neural network. We not only get rid of the overhead associated with the conventional methods, but also achieve near-optimal spectral efficiency rates using the proposed beamformer predictor. The proposed method also outperforms deep learning based beamformer predictors accepting THz channel matrices as input, thus proving the validity and efficiency of our sub-6GHz based approach.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624178","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840810
Ketan Atul Bapat, M. Chakraborty
In this paper, we propose a new algorithm named Randomized Simultaneous Hard Thresholding Pursuit(RSHTP) for the multiple measurements vector (MMV) problem in compressed sensing. In the proposed algorithm, the gradient is calculated only with respect to few of the signals at each iteration that are chosen randomly. This reduces the computational cost which is significant when the problem size is large. A deterministic convergence analysis is carried out where we present theoretical guarantees using the restricted isometric property (RIP). Simulation studies show that the proposed algorithm enjoys at par performance even at a moderate rate of column selection in each iteration.
{"title":"Randomized Simultaneous Hard Thresholding Pursuit Algorithm for Multiple Measurement Vectors","authors":"Ketan Atul Bapat, M. Chakraborty","doi":"10.1109/SPCOM55316.2022.9840810","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840810","url":null,"abstract":"In this paper, we propose a new algorithm named Randomized Simultaneous Hard Thresholding Pursuit(RSHTP) for the multiple measurements vector (MMV) problem in compressed sensing. In the proposed algorithm, the gradient is calculated only with respect to few of the signals at each iteration that are chosen randomly. This reduces the computational cost which is significant when the problem size is large. A deterministic convergence analysis is carried out where we present theoretical guarantees using the restricted isometric property (RIP). Simulation studies show that the proposed algorithm enjoys at par performance even at a moderate rate of column selection in each iteration.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129783363","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840776
Darshil Shah, K. G. Gopan, N. Sinha
Parkinson’s Disease (PD) is a disorder of the central nervous system which affects movement, often including tremors. Nerve cell damage in the brain causes dopamine levels to drop which gradually degrades the functionality of the brain. Since PD is a neurodegenerative ailment, Electroencephalography (EEG) signal are used for early detection of Parkinson’s Disease. EEG being non-linear and non-stationary manual analysis is not only time consuming but prone to error. To detect PD, two methods are discussed in this paper: (1) CNN for EEG images and (2) k-nearest neighbors for manually extracted features from EEG signals. The proposed methodology is applied to publicly available datasets (1) University of New Mexico (UNM) (27 PD patients and 27 controls) and (2) Iowa (14 PD patients and 14 controls). Data from New Mexico is used to evaluate the performance of the model using k-fold cross-validation method and data from Iowa is used for out-of-sample evaluation. Mean test accuracy on the mentioned datasets reaches to 88.51% and 87.6% respectively making an improvement of 3.11% and 1.9% for UNM and Iowa dataset, as compared to the current state-of-the-art accuracy.
{"title":"Analysis of EEG for Parkinson’s Disease Detection","authors":"Darshil Shah, K. G. Gopan, N. Sinha","doi":"10.1109/SPCOM55316.2022.9840776","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840776","url":null,"abstract":"Parkinson’s Disease (PD) is a disorder of the central nervous system which affects movement, often including tremors. Nerve cell damage in the brain causes dopamine levels to drop which gradually degrades the functionality of the brain. Since PD is a neurodegenerative ailment, Electroencephalography (EEG) signal are used for early detection of Parkinson’s Disease. EEG being non-linear and non-stationary manual analysis is not only time consuming but prone to error. To detect PD, two methods are discussed in this paper: (1) CNN for EEG images and (2) k-nearest neighbors for manually extracted features from EEG signals. The proposed methodology is applied to publicly available datasets (1) University of New Mexico (UNM) (27 PD patients and 27 controls) and (2) Iowa (14 PD patients and 14 controls). Data from New Mexico is used to evaluate the performance of the model using k-fold cross-validation method and data from Iowa is used for out-of-sample evaluation. Mean test accuracy on the mentioned datasets reaches to 88.51% and 87.6% respectively making an improvement of 3.11% and 1.9% for UNM and Iowa dataset, as compared to the current state-of-the-art accuracy.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127846670","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840809
Pratap Kumar Koppolu, K. Chemmangat
The myoelectric Pattern Recognition (PR) collects surface Electromyographic (sEMG) signals using the electrodes placed on the upper limb of the amputee. Then it recognizes patterns in those signals based on the intended limb movement using signal processing and machine learning techniques. The performance of the PR system should be robust against multiple factors, like wrist orientation, muscle force level changes, limb position changes, and electrode shifts. This paper demonstrates how performance is affected by wrist orientation and proposes a method to overcome those effects. A two-stage classification technique with Dynamic Time Warping (DTW) as the classifier, along with features extracted from a three-axis accelerometer and six-channel sEMG sensors, is proposed here. Accelerometer features are used to identify the wrist orientation, and sEMG features are used to classify the various limb movements performed by ten subjects. The performance of the proposed method was measured by classification error and classification accuracy of limb movements. The corresponding results were compared with the state-of-the-art techniques.
{"title":"A two-stage classification strategy to reduce the effect of wrist orientation in surface myoelectric pattern recognition","authors":"Pratap Kumar Koppolu, K. Chemmangat","doi":"10.1109/SPCOM55316.2022.9840809","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840809","url":null,"abstract":"The myoelectric Pattern Recognition (PR) collects surface Electromyographic (sEMG) signals using the electrodes placed on the upper limb of the amputee. Then it recognizes patterns in those signals based on the intended limb movement using signal processing and machine learning techniques. The performance of the PR system should be robust against multiple factors, like wrist orientation, muscle force level changes, limb position changes, and electrode shifts. This paper demonstrates how performance is affected by wrist orientation and proposes a method to overcome those effects. A two-stage classification technique with Dynamic Time Warping (DTW) as the classifier, along with features extracted from a three-axis accelerometer and six-channel sEMG sensors, is proposed here. Accelerometer features are used to identify the wrist orientation, and sEMG features are used to classify the various limb movements performed by ten subjects. The performance of the proposed method was measured by classification error and classification accuracy of limb movements. The corresponding results were compared with the state-of-the-art techniques.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123878671","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840770
Aditya Singh, Surender Redhu, R. Hegde
Maintaining adequate energy in low-powered Internet of Things (IoT) nodes is crucial for developing several applications like smart homes, autonomous industries, etc. In this context, energy harvesting plays an essential role in improving the operational lifetime of the IoT nodes. Unmanned Aerial Vehicles (UAVs) have become a feasible option for reaching out to the low-powered IoT nodes in remote areas and recharging them by acting as efficient energy transmitter units. However, ensuring a sustainable and regular supply of power to these IoT nodes mainly depends on the trajectories of UAVs. In this context, the UAV trajectory optimization problem is first formulated. Subsequently, an energy-efficient UAV route planning algorithm (UAV-RPA) is proposed to generate the UAV trajectory to recharge the IoT nodes. The proposed algorithm minimizes the UAV-travel time by selecting an optimal sequence of IoT nodes such that the UAV trajectory length is minimized. Moreover, extensive simulations are also conducted under various network scenarios to evaluate the performance of the route planning algorithm. It is observed that the proposed UAV-RPA generates a minimal length UAV trajectory over an IoT network when compared to other UAV trajectory generation algorithms. Also, the average residual energy per IoT node in the network is also improved. This, in turn, improves the operational lifetime of self-sustaining UAV-powered IoT networks.
{"title":"Energy-Efficient UAV Trajectory Planning in Rechargeable IoT Networks","authors":"Aditya Singh, Surender Redhu, R. Hegde","doi":"10.1109/SPCOM55316.2022.9840770","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840770","url":null,"abstract":"Maintaining adequate energy in low-powered Internet of Things (IoT) nodes is crucial for developing several applications like smart homes, autonomous industries, etc. In this context, energy harvesting plays an essential role in improving the operational lifetime of the IoT nodes. Unmanned Aerial Vehicles (UAVs) have become a feasible option for reaching out to the low-powered IoT nodes in remote areas and recharging them by acting as efficient energy transmitter units. However, ensuring a sustainable and regular supply of power to these IoT nodes mainly depends on the trajectories of UAVs. In this context, the UAV trajectory optimization problem is first formulated. Subsequently, an energy-efficient UAV route planning algorithm (UAV-RPA) is proposed to generate the UAV trajectory to recharge the IoT nodes. The proposed algorithm minimizes the UAV-travel time by selecting an optimal sequence of IoT nodes such that the UAV trajectory length is minimized. Moreover, extensive simulations are also conducted under various network scenarios to evaluate the performance of the route planning algorithm. It is observed that the proposed UAV-RPA generates a minimal length UAV trajectory over an IoT network when compared to other UAV trajectory generation algorithms. Also, the average residual energy per IoT node in the network is also improved. This, in turn, improves the operational lifetime of self-sustaining UAV-powered IoT networks.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124026984","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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