This paper proposes a novel methodology of analyzing mobile Internet of Things (IoT) data by performing spatial and anthropogenic factor-based thematic interactions with it to retrieve interesting patterns that account for the data variation. In order to test out this methodology, a study is conducted by collecting Particulate Matter (PM) data across India using a mobile IoT node, and look into the neighbouring spatial and anthropogenic factors such as human activities, settlement patterns and vegetation profile corresponding to each geo-location of the PM data. By performing the spatial factor analysis on the mobile IoT data, we evaluated the influence of human activities on PM10 levels, most significantly observed for 0
CV and IoT-based Remote Triggered Labs: Use Case of Conservation of Mechanical Energy
Kandala Savitha Viswanadh,Om Rajendra Kathalkar,Om. K,P. Vinzey,Nitin Nilesh,Sachin Chaudhari,Venkatesh Choppella
Future Internet of Things and Cloud, FiCloud, 2022
@inproceedings{bib_CV_a_2022, AUTHOR = {Kandala Savitha Viswanadh, Om Rajendra Kathalkar, Om. K, P. Vinzey, Nitin Nilesh, Sachin Chaudhari, Venkatesh Choppella}, TITLE = {CV and IoT-based Remote Triggered Labs: Use Case of Conservation of Mechanical Energy}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2022}}
Remote Triggered Labs (RTL) are helpful for students to work on laboratory experiments virtually anytime, anywhere. Such setups can facilitate distance learning and are helpful during pandemics. In this paper, the use of Computer Vision (CV) is demonstrated for RTL experiments. For this, a use-case of the Conservation of Mechanical Energy experiment is considered. A CV-based approach is used to estimate an object’s velocity whose setup primarily consists of a microprocessor, a camera and infrared (IR) sensors. The experiment is recorded, and various CV techniques are employed to estimate the object’s velocity. This paper also compares a CV-based and an IR sensor-based approach to estimate the object’s velocity. Linear regression applied to the CV-based implementation resulted in an optimal mean-squared error (MSE), nearly 10 times better than IR-based implementation.
Improving IoT-based Smart Retrofit Model for Analog Water Meters using DL based Algorithm
Ayush Kumar Lall,Ansh Khandelwal,Nitin Nilesh,Sachin Chaudhari
International Conference on Future Internet of Things and Cloud, Fi Cloud, 2022
Abs | | bib Tex
@inproceedings{bib_Impr_2022, AUTHOR = {Ayush Kumar Lall, Ansh Khandelwal, Nitin Nilesh, Sachin Chaudhari}, TITLE = {Improving IoT-based Smart Retrofit Model for Analog Water Meters using DL based Algorithm}, BOOKTITLE = {International Conference on Future Internet of Things and Cloud}. YEAR = {2022}}
This paper proposes a deep learning (DL)-based algorithm which is used for improving the performance of digit detection from internet-of-things (IoT)-based analog water meters. The DL algorithm is trained on a rich dataset of over 160,000 images collected from six water nodes deployed at locations with different environmental conditions. A detailed comparison between the proposed DL and machine learning (ML) algorithm is made based on detection accuracy, feature analysis, error analysis, and computational complexity analysis. It is observed that compared to the ML model, the proposed DL model maintained a higher detection accuracy and is more generalized in terms of feature extraction, which makes the algorithm robust.
Performance Analysis of Selective Decode-and-Forward Relaying for Satellite-IoT
Nikhil Lamba,Ayush Kumar Dwivedi,Sachin Chaudhari
IEEE Globecom Communications Conference Workshops, Globecom -W, 2022
@inproceedings{bib_Perf_2022, AUTHOR = {Nikhil Lamba, Ayush Kumar Dwivedi, Sachin Chaudhari}, TITLE = {Performance Analysis of Selective Decode-and-Forward Relaying for Satellite-IoT}, BOOKTITLE = {IEEE Globecom Communications Conference Workshops}. YEAR = {2022}}
This paper considers a low-earth-orbit (LEO) satellite-based topology for an internet-of-things (IoT) network, where multiple IoT devices broadcast the information to all the visible satellites over a shared channel using slotted ALOHA. The satellites selectively decode-and-forward (DF) the information from the IoT devices over orthogonal channels to the ground station (GS), which does maximal ratio combining (MRC). For decoding at the satellites, capture and successive interference cancellation (SIC) schemes are considered. For the considered topology, the closed-form expressions are derived for the end-to-end outage probability (OP) for an arbitrary number of IoT devices and satellites in the capture model and for the two-device, two satellite case in the case of the SIC model. The expressions are derived for both independent and non-identically distributed (inid) and independent and identically distributed (iid) uplink channels. The OP is analyzed as a function of the parameters like the number of satellites, the number of devices, and the desired data rate. The results demonstrate that the proposed approach leverages the benefits of mega-LEO satellites to make the topology feasible and attractive for low-powered and low-complexity IoT networks.
IoT and ML-based AQI Estimation using Real-time Traffic Data
Nitin Nilesh,Jayati Narang,Ayu Parmar,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_IoT__2022, AUTHOR = {Nitin Nilesh, Jayati Narang, Ayu Parmar, Sachin Chaudhari}, TITLE = {IoT and ML-based AQI Estimation using Real-time Traffic Data}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
This paper proposes an IoT and machine learning (ML)-based novel method to estimate the air quality index (AQI) using traffic data in real-time. With the help of particulate matter (PM) monitoring nodes deployed in fifteen locations with diverse traffic scenarios of Indian roads, and using digital map service providers, a rich traffic dataset with approximately 210,000 samples has been collected. Three different ML models, namely random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), are trained on this dataset to predict the AQI category into five levels. The experimental results show an accuracy of 82.60% with the F1-score of 83.67% on the complete dataset. Apart from this, ML models were also trained on individual node datasets, and the behavior of AQI levels was observed. Index Terms—AQI Estimation, Traffic Data, Machine Learning, IoT
Comparative Evaluation of Low-Cost CO2 Sensors for Indoor Air Pollution Monitoring
Rishikesh Bose,Ayu Parmar,Narla Harsha Vardhan,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_Comp_2022, AUTHOR = {Rishikesh Bose, Ayu Parmar, Narla Harsha Vardhan, Sachin Chaudhari}, TITLE = {Comparative Evaluation of Low-Cost CO2 Sensors for Indoor Air Pollution Monitoring}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
In this paper, four low-cost CO2 sensors are evaluated for IoT-based indoor air pollution monitoring. Specifically, CO2 sensors SCD30, Prana Air, MHZ14, and T6713 are evaluated against a standard reference Aeroqual S-500 device. The experiment was carried out in an indoor environment inside one of the labs in IIIT Hyderabad, India. It is shown that calibration is needed for some of these low-cost devices locally even though the sensors may be factory calibrated. For calibration, simple and widely-used machine learning algorithms are employed such as linear regression, least absolute deviation, random forest, support vector regression, and Gaussian regression. The parameters considered to assess the performance of these sensors are coefficient of determination (R 2 ), coefficient of variability (Cv), and root mean square error (RMSE). After calibration with a reference sensor, it is observed that these low-cost sensors operate well. Index Terms—IoT, Determination coefficient, Low-cost CO2 sensor, Coefficient of variability, Root mean square error.
IoT-based AQI Estimation using Image Processing and Learning Methods
Nitin Nilesh,Ishan Patwardhan,Jayati Narang,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2022
@inproceedings{bib_IoT-_2022, AUTHOR = {Nitin Nilesh, Ishan Patwardhan, Jayati Narang, Sachin Chaudhari}, TITLE = {IoT-based AQI Estimation using Image Processing and Learning Methods}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2022}}
Air pollution is a concern to the health of all living beings. It is essential to check on the quality of air in the surroundings. This article presents an IoT-based real-time air quality index (AQI) estimation technique using images and weather sensors on Indian rods. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localization-based Deep Learning (DL) method along with image processing techniques were used to extract image features while an Machine Learning (ML) model was trained on those features to estimate the AQI. In order to conduct this experiment, a dataset containing 5048 images along with co-located AQI values across different seasons was collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82% for AQI prediction. Index Terms—Air Quality, CNN, Edge computing, Machine Learning.
Techno-economic Study of 5G Network Slicing to Improve Rural Connectivity in India
SHRUTHI KORATAGERE ANANTHA KUMAR,Robert Stewart,David Crawford,Sachin Chaudhari
IEEE Open Journal of the Communications Society, OJ-COMS, 2021
@inproceedings{bib_Tech_2021, AUTHOR = {SHRUTHI KORATAGERE ANANTHA KUMAR, Robert Stewart, David Crawford, Sachin Chaudhari}, TITLE = {Techno-economic Study of 5G Network Slicing to Improve Rural Connectivity in India}, BOOKTITLE = {IEEE Open Journal of the Communications Society}. YEAR = {2021}}
Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.
Security Analysis of Large Scale IoT Network for Pollution Monitoring in Urban India
G V Ihita,Kandala Savitha Viswanadh,Sudhansh Yelishetty,Sachin Chaudhari,SAGAR GAUR
World Forum on Internet of Things, WF-IoT, 2021
@inproceedings{bib_Secu_2021, AUTHOR = {G V Ihita, Kandala Savitha Viswanadh, Sudhansh Yelishetty, Sachin Chaudhari, SAGAR GAUR}, TITLE = {Security Analysis of Large Scale IoT Network for Pollution Monitoring in Urban India}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2021}}
Maximum Frequency Based Adaptive Sensing for Particulate Matter Nodes in IoT Network
Chinthalapani Rajashekar Reddy,S. De,Sachin Chaudhari
World Forum on Internet of Things, WF-IoT, 2021
@inproceedings{bib_Maxi_2021, AUTHOR = {Chinthalapani Rajashekar Reddy, S. De, Sachin Chaudhari}, TITLE = {Maximum Frequency Based Adaptive Sensing for Particulate Matter Nodes in IoT Network }, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2021}}
Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network
Chinthalapani Rajashekar Reddy,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2021
@inproceedings{bib_Hier_2021, AUTHOR = {Chinthalapani Rajashekar Reddy, Sachin Chaudhari}, TITLE = {Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2021}}
For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.
Making Analog Water Meter Smart using ML and IoT-based Low-Cost Retrofitting
Ayush Kumar Lall,Ansh Khandelwal,Rishi Bose,Bawankar Nilesh Kundanrao,Nitin Nilesh,Ayush Kumar Dwivedi,Sachin Chaudhari
Future Internet of Things and Cloud, FiCloud, 2021
@inproceedings{bib_Maki_2021, AUTHOR = {Ayush Kumar Lall, Ansh Khandelwal, Rishi Bose, Bawankar Nilesh Kundanrao, Nitin Nilesh, Ayush Kumar Dwivedi, Sachin Chaudhari}, TITLE = {Making Analog Water Meter Smart using ML and IoT-based Low-Cost Retrofitting}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2021}}
—This paper introduces an internet-of-things (IoT) based economic retrofitting setup for digitising the analog water meters to make them smart. The setup contains a Raspberry-Pi microcontroller and a Pi-camera mounted on top of the analog water meter to take its images. The captured images are then preprocessed to estimate readings using a machine learning (ML) model. The employed ML algorithm is trained on a rich dataset that includes digits from the images of water meters captured by the hardware setup for ten days. The readings are posted on a cloud server in real-time using Raspberry-Pi. High temporal resolution plots of flow rate and volume are generated to derive inferences. The collected data can be used for deriving water consumption patterns and fault detection for efficient water management.
IoT Network-Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown
Souradeep Deb,Chinthalapani Rajashekar Reddy,Sachin Chaudhari,KAVITA VEMURI,K.S. Rajan
International Conference on Electronics, Computing and Communication Technologies, CONECCT, 2021
@inproceedings{bib_IoT__2021, AUTHOR = {Souradeep Deb, Chinthalapani Rajashekar Reddy, Sachin Chaudhari, KAVITA VEMURI, K.S. Rajan}, TITLE = {IoT Network-Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown}, BOOKTITLE = {International Conference on Electronics, Computing and Communication Technologies}. YEAR = {2021}}
During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown
Comparative evaluation of new low-cost particulate matter sensors
Ishan Patwardhan,Sara Spanddhana,Sachin Chaudhari
International Conference on Future Internet of Things and Cloud, Fi Cloud, 2021
@inproceedings{bib_Comp_2021, AUTHOR = {Ishan Patwardhan, Sara Spanddhana, Sachin Chaudhari}, TITLE = {Comparative evaluation of new low-cost particulate matter sensors}, BOOKTITLE = {International Conference on Future Internet of Things and Cloud}. YEAR = {2021}}
In recent times, a few new low-cost sensors have been introduced to the global market for monitoring particulate matter (PM). In this paper, the performance of three such low-cost PM sensors, namely SDS011, Prana Air, and SPS30, for measuring PM2.5 and PM10 levels is evaluated against a standard reference Aeroqual Series-500. The test setup was exposed to PM concentrations ranging from 30 µg/cm3 to 600 µg/cm3 . The results were based on 1 min, 15 min, 30 min, and 1 hr average readings. The experiments were carried out in indoor as well as outdoor environments. The comparative evaluation was performed before and after calibration. The performance of these sensors is evaluated in terms of coefficient of determination (R 2 ), coefficient of variation (Cv) and root mean square error (RMSE). Evaluation results show that these low-cost sensors have good performance after calibration with a reference sensor.
IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown
Souradeep Deb,Chinthalapani Rajashekar Reddy,Sachin Chaudhari,Kavita Vemuri,Rajan Krishnan Sundara
International Conference on Electronics, Computing and Communication Technologies, CONECCT, 2021
@inproceedings{bib_IoT__2021, AUTHOR = {Souradeep Deb, Chinthalapani Rajashekar Reddy, Sachin Chaudhari, Kavita Vemuri, Rajan Krishnan Sundara}, TITLE = {IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown}, BOOKTITLE = {International Conference on Electronics, Computing and Communication Technologies}. YEAR = {2021}}
During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. Index Terms
Beamformed Energy Detection in the Presence of an Interferer for Cognitive mmWave Network
M. Madhuri Latha,Dara Sai Krishna Charan,Sachin Chaudhari,Neeraj Varshney
Vehicular Technology Conference, VTC, 2021
@inproceedings{bib_Beam_2021, AUTHOR = {M. Madhuri Latha, Dara Sai Krishna Charan, Sachin Chaudhari, Neeraj Varshney}, TITLE = {Beamformed Energy Detection in the Presence of an Interferer for Cognitive mmWave Network}, BOOKTITLE = {Vehicular Technology Conference}. YEAR = {2021}}
In this paper, we propose beamformed energy detection (BFED) spectrum sensing schemes for a single secondary user (SU) or a cognitive radio to detect a primary user (PU) transmission in the presence of an interferer. In the millimeter wave (mmWave) band, due to high attenuation, there are fewer multipaths, and the channel is sparse, giving rise to fewer directions of arrivals (DoAs). Sensing in only these paths instead of blind energy detection can reap significant benefits. An analog beamforming weight vector is designed such that the beamforming gain in the true DoAs of the PU signal is maximized while minimizing interference from the interferer. To demonstrate the bound on the system performance, the proposed sensing scheme is designed under the knowledge of full channel state information (CSI) at the SU for the PU-SU and Interferer-SU channels. However, as the CSI may not be available at the SU, another BFED sensing scheme is proposed, which only utilizes the estimate the DoAs. To model the estimates of DoAs, perturbations are added to the true DoAs. The distribution of the test statistic for BFED with full CSI schemes is derived under the null hypothesis so that the threshold of the NeymanPearson detector can be found analytically. The performance of both schemes is also compared with the traditional energy detector for multi-antenna systems. Index Terms—Beamforming, direction of arrival (DoA), energy detection, mmWave, spectrum sensing.
Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors
Chinthalapani Rajashekar Reddy,Mukku Tanmai,Ayush Kumar Dwivedi,AUROPRAVA ROUT,Sachin Chaudhari,Kavita Vemuri,Rajan Krishnan Sundara,Aftab M. Hussain
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2020
@inproceedings{bib_Impr_2020, AUTHOR = {Chinthalapani Rajashekar Reddy, Mukku Tanmai, Ayush Kumar Dwivedi, AUROPRAVA ROUT, Sachin Chaudhari, Kavita Vemuri, Rajan Krishnan Sundara, Aftab M. Hussain}, TITLE = {Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2020}}
Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.
Performance Analysis of Novel Direct Access Schemes for LEO Satellites Based IoT Network
Ayush Kumar Dwivedi,Chokkarapu Sai Praneeth,Sachin Chaudhari,Neeraj Varshney
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2020
@inproceedings{bib_Perf_2020, AUTHOR = {Ayush Kumar Dwivedi, Chokkarapu Sai Praneeth, Sachin Chaudhari, Neeraj Varshney}, TITLE = {Performance Analysis of Novel Direct Access Schemes for LEO Satellites Based IoT Network}, BOOKTITLE = {International Symposium on Personal, Indoor and Mobile Radio Communications}. YEAR = {2020}}
This paper analyzes the performance of low earth orbit (LEO) satellites based internet-of-things (IoT) network where each IoT node makes use of multiple satellites to communicate with the ground station (GS). In this work, we consider fixed and variable gain amplify-and-forward (AF) relaying protocol at each satellite where the received signal from each IoT node is amplified before transmitting to the terrestrial GS for data processing. To analyze the performance of this novel LEO satellites based direct access architecture, the closed-form expressions for outage probability are derived considering two combining schemes at the GS:(i) selection combining;(ii) maximal ratio combining. Further, to gain more insights for diversity order and coding gain, asymptotic outage probability analysis at high SNR for both schemes is also performed. Finally, simulation results are presented to validate the analytical results derived and also to develop several interesting insights into the system performance.
Embedded Machine Learning-Based Data Reduction in Application-Specific Constrained IoT Networks, Czech Republic
Adarsh Pal Singh,Sachin Chaudhari
ACM Symposium on Applied Computing, SAC, 2020
@inproceedings{bib_Embe_2020, AUTHOR = {Adarsh Pal Singh, Sachin Chaudhari}, TITLE = {Embedded Machine Learning-Based Data Reduction in Application-Specific Constrained IoT Networks, Czech Republic}, BOOKTITLE = {ACM Symposium on Applied Computing}. YEAR = {2020}}
Reducing the amount of wireless data transmissions in constrainedbattery-powered sensor nodes is an effective way of prolongingtheir lifetime. In this paper, we present a machine learning-baseddata transmission reduction scheme for application-specific IoTnetworks. Though many error thresholding-based data predictionschemes have been explored in the past, this is the first work toincorporate machine learning in constrained sensor nodes to reducedata transmissions. We also provide a generic overview and com-parison of five traditional supervised machine learning algorithmsin the context of offloading trained models to memory and computa-tionally constrained microcontrollers. The proposed data reductionscheme is validated on an occupancy estimation testbed deployed inour lab. Experimental results demonstrate 99.91% overall reductionin data transmissions while imparting similar performance and 18to 82 times lesser transmissions than Shewhart change detectionalgorithm.
Opportunistic Use of Successive Interference Cancellation in Reverse TDD HetNets
GORREPATI RAKESH,Sachin Chaudhari,Taneli Riihonen
Wireless Communications Letters, WCL, 2020
@inproceedings{bib_Oppo_2020, AUTHOR = {GORREPATI RAKESH, Sachin Chaudhari, Taneli Riihonen}, TITLE = {Opportunistic Use of Successive Interference Cancellation in Reverse TDD HetNets}, BOOKTITLE = {Wireless Communications Letters}. YEAR = {2020}}
Cross-tier interference management is one of the major challenges in heterogeneous cellular networks (HetNets). Though the network throughput increases due to a better area spectral efficiency of a HetNet, there is possibility that high interference will make few link capacities close to zero when users regard interference as noise (IAN). In this letter, successive interference cancellation (SIC) is used to cancel the cross-tier interference in a reverse time division duplexing (RTDD) scheme. We demonstrate that by opportunistic use of SIC, a minimum guarantee on the sum link capacity can be ensured for an RTDD HetNet. This minimum sum link capacity is later on proved to be the maximum that can be achieved by orthogonal resource allocation schemes. Through system-level simulations for random allocation, it is shown that the proposed scheme is better than using SIC and IAN alone. To further improve the overall system capacity, an optimization problem for selecting co-channel users is formulated, and the Hungarian algorithm is employed to solve it.
Copula-Based Cooperative Sensing of OFDM Signals in Cognitive Radios
Akhil Singh,Chokkarapu Sai Praneeth,Sachin Chaudhari,Pramod K. Varshney
International Conference on Communication Systems & Networks, COMSNETS, 2020
@inproceedings{bib_Copu_2020, AUTHOR = {Akhil Singh, Chokkarapu Sai Praneeth, Sachin Chaudhari, Pramod K. Varshney}, TITLE = {Copula-Based Cooperative Sensing of OFDM Signals in Cognitive Radios}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2020}}
This paper proposes the use of copula theory for cooperative spectrum sensing (CSS) of orthogonal frequency-division multiplexing (OFDM) based primary user (PU). A distributed detection model is assumed where secondary users (SUs) employ autocorrelation detectors (ADs) for the detection of a PU. In the presence of a PU, it is assumed that the observations across different SUs and subsequently the decision statistics are dependent. For the fusion of these dependent statistics, different copulas such as -copula, Gaussian, Clayton and Gumbel are employed. In the presence of dependence among decision statistics, significant improvement in detection performance is observed while using copula theory instead of the traditional assumption of independence. Simulation results are presented to show the superiority of copula-based spectrum sensing.
Beamformed Sensing using Dominant DoA in Cognitive mmWave Network
M. Madhuri Latha,Dara Sai Krishna Charan,Sachin Chaudhari
International Conference on Advanced Networks and Telecommunications Systems, ANTS, 2020
@inproceedings{bib_Beam_2020, AUTHOR = {M. Madhuri Latha, Dara Sai Krishna Charan, Sachin Chaudhari}, TITLE = {Beamformed Sensing using Dominant DoA in Cognitive mmWave Network}, BOOKTITLE = {International Conference on Advanced Networks and Telecommunications Systems}. YEAR = {2020}}
In this paper, we propose spectrum sensing schemes for a secondary user (SU) with multiple antennas to detect a primary user (PU) transmission in a cognitive mmWave network. The channel model considered at mmWave carrier frequencies is the clustered Rician fading channel, which has few multipaths. For the considered scenario, we propose three beamformed energy detection (BFED) schemes where beamforming is done in the dominant direction of arrival (DoA) at the SU and then energy detection (ED) is applied. The three schemes differ in the amount of information assumed about the DoAs at the SU. The performance of these schemes has been compared with the traditional ED and maximal ratio combining (MRC) schemes for multiantenna systems. It is shown through simulations that the proposed BFED approaches provide significant performance gains over the ED and negligible loss as compared to the MRC, which makes an impractical assumption of the channel between the PU and the SU to be exactly known.
Business model for rural connectivity using multi-tenancy 5G network slicing
Shruthi Koratagere Anantha Kumar,Anantha Kumar,Robert Stewart,David Crawford,Sachin Chaudhari
h International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HO, HONET, 2020
@inproceedings{bib_Busi_2020, AUTHOR = {Shruthi Koratagere Anantha Kumar, Anantha Kumar, Robert Stewart, David Crawford, Sachin Chaudhari}, TITLE = {Business model for rural connectivity using multi-tenancy 5G network slicing }, BOOKTITLE = {h International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HO}. YEAR = {2020}}
Rural areas are often neglected while deploying newer mobile technologies. Hence, these place are digitally disconnected from the world. To overcome this challenge, 5G network slicing supporting multi-tenancy, also known as neutral host network, is studied to improve rural connectivity. The infrastructure provider (InP) deploys the rural 5G network and mobile network operators (MNOs) lease the slices from InP to serve their end-users. This aims to study the value network configuration (VNC) for the 5G network slicing architecture to understand the possible business model. As a result, three configurations are defined driven by micro-operator, MNO and community end-users respectively. The business models are constructed using SWOT analysis and business canvas models. The revenue streams for the proposed rural network are analyzed.
Rate and Power Throttling for Traffic Asymmetry in Reverse TDD HetNets
GORREPATI RAKESH,Nachiket Ayir,Sachin Chaudhari,Taneli Riihonen
URSI Convention on Radio Science, URSI, 2019
@inproceedings{bib_Rate_2019, AUTHOR = {GORREPATI RAKESH, Nachiket Ayir, Sachin Chaudhari, Taneli Riihonen}, TITLE = {Rate and Power Throttling for Traffic Asymmetry in Reverse TDD HetNets}, BOOKTITLE = {URSI Convention on Radio Science}. YEAR = {2019}}
In this paper, sum link capacity expressions for successive interference cancellation (SIC) and regarding interference as noise (IAN) in reverse time-division duplexing (RTDD) heterogeneous cellular network are derived. The considered RTDD network always operates in a synchronized fashion such that if the macro tier is in the uplink (UL), then the small tier will be in the downlink (DL) and vice-versa. Rate and power throttling are used in the uplink (UL) for both IAN and SIC to consider an asymmetric traffic network (DL≫ UL). Systemlevel simulations are performed to compare the overall system throughput of IAN and SIC for different DL/UL ratios. It is observed that rate or power-throttled SIC performs better than rate-throttled IAN and worse than power-throttled IAN.
Improving the Accuracy of the Shewhart Test-based Data-Reduction Technique using Piggybacking
ANISH SHASTRI,Vivek Jain,Sachin Chaudhari,Shailesh Singh Chouhan,Stefan Werner
World Forum on Internet of Things, WF-IoT, 2019
@inproceedings{bib_Impr_2019, AUTHOR = {ANISH SHASTRI, Vivek Jain, Sachin Chaudhari, Shailesh Singh Chouhan, Stefan Werner}, TITLE = {Improving the Accuracy of the Shewhart Test-based Data-Reduction Technique using Piggybacking}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2019}}
This paper proposes the use of Shewharttest to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggy-backing and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime
Improving Accuracy of the Shewhart-based Data-Reduction in IoT Nodes using Piggybacking
ANISH SHASTRI,Vivek Jain,Sachin Chaudhari,Shailesh Singh Chouhan,Stefan Werner
World Forum on Internet of Things, WF-IoT, 2019
@inproceedings{bib_Impr_2019, AUTHOR = {ANISH SHASTRI, Vivek Jain, Sachin Chaudhari, Shailesh Singh Chouhan, Stefan Werner}, TITLE = {Improving Accuracy of the Shewhart-based Data-Reduction in IoT Nodes using Piggybacking}, BOOKTITLE = {World Forum on Internet of Things}. YEAR = {2019}}
This paper proposes the use of Shewhart test to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggybacking and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime. Published in: 2019 IEE
On the Implementation of LMS-based Algorithm for Increasing the Lifetime of IoT Networks
ANISH SHASTRI,Vivek Jain,RHISHI PRATAP SINGH,Sachin Chaudhari,Shailesh Singh Chouhan
International Conference on Advanced Networks and Telecommunications Systems, ANTS, 2018
@inproceedings{bib_On_t_2018, AUTHOR = {ANISH SHASTRI, Vivek Jain, RHISHI PRATAP SINGH, Sachin Chaudhari, Shailesh Singh Chouhan}, TITLE = {On the Implementation of LMS-based Algorithm for Increasing the Lifetime of IoT Networks}, BOOKTITLE = {International Conference on Advanced Networks and Telecommunications Systems}. YEAR = {2018}}
This paper focuses on the customized-wireless sensor node implementation of the classical least mean square (LMS) algorithm for the reduction in data-transmissions from the sensor nodes to the sink in internet of things (IoT) networks. This reduction, in turn, increases the battery life of the sensor node. The system was deployed in outdoor and indoor environments to read the ambient temperature and then perform the prediction of the sensed data in order to minimize the number of data-transmissions to the sink node. The utility of the proposed concept has been demonstrated using the measured data and the battery life is increased 2.64 and 2.53 times in indoor and outdoor environments, respectively.
Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes
Adarsh Pal Singh,Vivek Jain,Sachin Chaudhari,Frank Alexander Kraemer,Stefan Werner,Vishal Garg
IEEE Global Communications Conference, GLOBECOM, 2018
@inproceedings{bib_Mach_2018, AUTHOR = {Adarsh Pal Singh, Vivek Jain, Sachin Chaudhari, Frank Alexander Kraemer, Stefan Werner, Vishal Garg}, TITLE = {Machine Learning-based Occupancy Estimation Using Multivariate Sensor Nodes}, BOOKTITLE = {IEEE Global Communications Conference}. YEAR = {2018}}
In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately estimating the number of occupants in a room by leveraging multiple heterogeneous sensor nodes and machine learning models. For this purpose, low-cost and non-intrusive sensors such as CO 2 , temperature, illumination, sound and motion were used. The sensor nodes were deployed in a room in a star configuration and measurements were recorded for a period of four days. A regression based method is proposed for calculating the slope of CO 2 , a new feature derived from real-time CO 2 values. Supervised learning algorithms such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM) and random forest (RF) were used on several different combinations of feature sets. Moreover, multiple performance metrics such as accuracy, F1 score and confusion matrix were used to evaluate the performance of our models. Experimental results demonstrate a maximum accuracy of 98.4% and a high F1 score of 0.953 for estimating the number of occupants in the room. Principal component analysis (PCA) was also applied to evaluate the performance of a dataset with reduced dimensionality.
Cooperative Energy Detection with Heterogeneous Sensors under Noise Uncertainty: SNR Wall and use of Evidence Theory
PRAKASH BORPATRA GOHAIN,Sachin Chaudhari,V. Koivunen
IEEE Transactions on Cognitive Communications and Networking, TCCN, 2018
@inproceedings{bib_Coop_2018, AUTHOR = {PRAKASH BORPATRA GOHAIN, Sachin Chaudhari, V. Koivunen }, TITLE = {Cooperative Energy Detection with Heterogeneous Sensors under Noise Uncertainty: SNR Wall and use of Evidence Theory}, BOOKTITLE = {IEEE Transactions on Cognitive Communications and Networking}. YEAR = {2018}}
The analyzed system model in this paper is a distributed parallel detection network in which each secondary user (SU) evaluates the energy-based test statistic from the received observations and sends it to a fusion center (FC), which makes the final decision. Uncertainty in the noise variance at each SU is modeled as an unknown constant in a certain interval around the nominal noise variance. It is assumed that the SUs are heterogeneous in that the nominal noise variances and the uncertainty intervals can be different for different SUs. Moreover, the received signal power at each SU may be different. For the considered system model, the paper presents important results for two inter-related themes on cooperative energy detection (CED) in the presence of noise uncertainty (NU). First, the expressions for generalized signal-to-noise ratio (SNR) walls are derived for the classical CED fusion rule, i.e., sum of energies from all SUs. Second, a Dempster-Shafer theory based CED is proposed in the presence of NU with heterogeneous sensors. In the proposed scheme, the test statistic from each SU is the energy-based basic mass assignment values, which are first discounted depending on the uncertainty level associated with the SU and then fused at the FC using the Dempster rule of combination to arrive at the global decision. It is shown that the proposed scheme outperforms the traditional sum fusion rule in terms of detection performance as well as the location of SNR wall.
Low Complexity Two-Stage Sensing Using Energy Detection and Beamforming
M. Madhuri Latha,PRAKASH BORPATRA GOHAIN,Sachin Chaudhari
National Conference on Communications, NCC, 2018
@inproceedings{bib_Low__2018, AUTHOR = {M. Madhuri Latha, PRAKASH BORPATRA GOHAIN, Sachin Chaudhari}, TITLE = {Low Complexity Two-Stage Sensing Using Energy Detection and Beamforming}, BOOKTITLE = {National Conference on Communications}. YEAR = {2018}}
In this paper, we propose two two-stage spectrum sensing schemes for a single secondary user (SU) or cognitive radio (CR) with multiple antennas to detect a primary user (PU) transmission. For both the proposed schemes, the first stage involves low-complexity coarse-sensing using simple energy detection (ED). The second stage for both methods involve high-performance fine-sensing using beamformed energy detection (BFED) in the estimated direction of arrival (DoA) of the PU signal. In the two-stage method, the second stage is conditional and sensing process goes to the second stage only if certain performance criteria is not met in the first stage. The two proposed methods differ in the performance criteria, which decides if the second stage of BFED is needed or not. The first two-stage method is designed to reduce complexity when there is no PU transmission while the second method is designed to reduce complexity when the PU signal is present. It is shown through simulations that the proposed two-stage schemes have significantly lower complexity as compared to only employing single-stage BFED with little or no performance loss.
Cooperative Sensing of OFDM Signals Using Heterogeneous Sensors
Akhil Singh,PRAKASH BORPATRA GOHAIN,Sachin Chaudhari
National Conference on Communications, NCC, 2018
@inproceedings{bib_Coop_2018, AUTHOR = {Akhil Singh, PRAKASH BORPATRA GOHAIN, Sachin Chaudhari}, TITLE = {Cooperative Sensing of OFDM Signals Using Heterogeneous Sensors}, BOOKTITLE = {National Conference on Communications}. YEAR = {2018}}
In this paper, we investigate a distributed and heterogeneous cognitive radio network (CRN), comprising of secondary users (SUs) employing either energy detector (ED) or autocorrelation detector (AD) to detect the presence or absence of an orthogonal frequency-division multiplexing (OFDM) based primary user (PU). For the considered heterogeneous cooperative spectrum sensing (CSS), the optimal soft combining rule is derived. The performance of this optimal fusion rule and different hard combining schemes such as OR, AND, and MAJOR- ITY is presented for the case when the noise variance is exactly known. Later, the effect of noise uncertainty is also presented. The proposed heterogeneous CSS is shown to combine the excellent performance of the EDs (when the noise variance is exactly known) and robustness of the ADs to the noise uncertainty.
Autocorrelation-Based Spectrum Sensing of FBMC Signal
Sachin Chaudhari,KEESARA UPENDER REDDY
International Conference on Communication Systems & Networks, COMSNETS, 2018
@inproceedings{bib_Auto_2018, AUTHOR = {Sachin Chaudhari, KEESARA UPENDER REDDY}, TITLE = {Autocorrelation-Based Spectrum Sensing of FBMC Signal}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2018}}
The focus of this paper is on a feature detector for filter bank multicarrier (FBMC) signal in cognitive radio. In this paper, we first prove that the FBMC signal samples are uncorrelated with each other. However, if the FBMC signal is processed by our proposed method, then the autocorrelation function (ACF) of FBMC signal becomes non-zero at the lag equal to number of subcarriers. On the other hand, additive white Gaussian noise (AWGN) samples after the same proposed processing remain uncorrelated. Using this feature, an autocorrelation based feature detector is proposed to detect FBMC signal in noise. The main advantage of the proposed detector is that, unlike blind detectors, this detector can distinguish between FBMC signal and noise (or interference). Next, the distribution of the test statistic of the proposed detector is derived under noise-only scenario so that the threshold of the Neyman-Pearson detector can be designed to maintain constant false alarm rate while maximizing the probability of detection. Simulation results demonstrate the efficacy of the proposed detector
Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements
Sachin Chaudhari,Marko Kosunen,Semu Makinen,Chandrasekaran Ramanathan,Jan Oksanen,Markus Laatta,Jussi Ryynanen,Visa Koivunen,Mikko Valkama
IEEE Transactions on Vehicular Technology, TVT, 2018
@inproceedings{bib_Spat_2018, AUTHOR = {Sachin Chaudhari, Marko Kosunen, Semu Makinen, Chandrasekaran Ramanathan, Jan Oksanen, Markus Laatta, Jussi Ryynanen, Visa Koivunen, Mikko Valkama}, TITLE = {Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements}, BOOKTITLE = {IEEE Transactions on Vehicular Technology}. YEAR = {2018}}
The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map using field measurements obtained by cyclostationary-based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to the widely used energy detectors, such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting, ordinary kriging, and universal kriging. The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight digital video broadcasting-terrestrial (DVB-T) channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross-validation approach with the widely used root mean square error as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator. Comparison results clearly show the performance improvement and robustness obtained using cyclostationary based detectors instead of energy detectors.
Evidence Theory based Cooperative Energy Detection under Noise Uncertainty
PRAKASH BORPATRA GOHAIN,Sachin Chaudhari,Visa Koivunen
IEEE Global Communications Conference, GLOBECOM, 2017
@inproceedings{bib_Evid_2017, AUTHOR = {PRAKASH BORPATRA GOHAIN, Sachin Chaudhari, Visa Koivunen}, TITLE = {Evidence Theory based Cooperative Energy Detection under Noise Uncertainty}, BOOKTITLE = {IEEE Global Communications Conference}. YEAR = {2017}}
Noise power uncertainty is a major issue in detectors for spectrum sensing. Any uncertainty in the noise power leads to significant reduction in the detection performance of the energy detector and also results in a performance limitation in the form of SNR walls. In this paper, we propose an evidence theory (also called Dempster-Shafer theory (DST)) based cooperative energy detection (CED) for spectrum sensing. The noise variance is modeled as a random variable with a known distribution. The analyzed system model is similar to a distributed parallel detection network where each secondary user (SU) evaluates the energy from its received signal samples and sends it to a fusion center (FC), which makes the final decision. However, in the proposed DST-based CED scheme, the SUs sends computed belief-values instead of actual energy value to the FC. Any uncertainty in the noise variance is accounted for by discounting the belief values based on the amount of uncertainty associated with each SU. Finally, the discounted belief values are combined using Dempster rule to reach at a global decision. Simulation results indicate that the proposed DST scheme significantly improves the detection probability at low average signal-to-noise ratio (ASNR) in comparison to the traditional sum fusion rule in the presence of noise uncertainty.
Improved Estimation of TV White Spaces in India using Terrain Data
GORREPATI RAKESH,Abhignya Eturu,Sachin Chaudhari,Jan Oksanen
National Conference on Communications, NCC, 2017
@inproceedings{bib_Impr_2017, AUTHOR = {GORREPATI RAKESH, Abhignya Eturu, Sachin Chaudhari, Jan Oksanen}, TITLE = {Improved Estimation of TV White Spaces in India using Terrain Data}, BOOKTITLE = {National Conference on Communications}. YEAR = {2017}}
Cognitive radio offers a novel solution to over-come the problem of spectrum underutilization by providing spectral access to secondary users. The television (TV) bands are of particular interest for secondary usage due to their high penetration power and greater coverage. These licensed bands are occupied only in few regions while in most of the other regions they are unoccupied and are termed asTV white space(TVWS). The estimation of TVWS has mostly been done by usingthe statistical and empirical propagation models. In this paper,terrain data is incorporated into the estimation of TVWS and shown to improve the quantitative estimation of TVWS. Using the relevant transmitter information and terrain data in the Indian state of Telangana, the efficacy of the proposed approach is demonstrated. The performance of the proposed approach is compared to that of widely used H at a propagation model. It is shown that the accuracy in TV coverage estimation increases onan average by 45% while incorporating terrain data as compared to using only empirical propagation model. As area outside theTV coverage is TVWS, the increased accuracy in the estimation of TV coverage directly translates to improved accuracy in the estimation of TVWS, which in turn translates into more efficient use of spectrum and better interference management
Distributed spatial modulation with dynamic frequency allocation
S KUNAL SHAM,Sachin Chaudhari,Ramamurthy Garimella
Physical Communications, PC, 2017
@inproceedings{bib_Dist_2017, AUTHOR = {S KUNAL SHAM, Sachin Chaudhari, Ramamurthy Garimella}, TITLE = {Distributed spatial modulation with dynamic frequency allocation}, BOOKTITLE = {Physical Communications}. YEAR = {2017}}
This paper proposes a distributed implementation of spatial modulation (SM) using cognitive radios. In distributed spatial modulation (DSM), multiple relays form a virtual antenna array and assist a source to transmit its information to a destination. The source broadcasts its signal, which is independently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Further, an instantaneous symbol error rate (SER) based selection combining is proposed to incorporate the direct link between the source and destination with existing DSM. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end SER as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.
Performance Evaluation of Cyclostationary - Based Cooperative Sensing Using Field Measurements
Sachin Chaudhari,Marko Kosunen,Marko Kosunen,Semu Mäkinen,Jan Oksanen,Markus Laatta,Jaakko Ojaniem,Visa Koivunen,Mikko Valkama
IEEE Transactions on Vehicular Technology, TVT, 2016
@inproceedings{bib_Perf_2016, AUTHOR = {Sachin Chaudhari, Marko Kosunen, Marko Kosunen, Semu Mäkinen, Jan Oksanen, Markus Laatta, Jaakko Ojaniem, Visa Koivunen, Mikko Valkama}, TITLE = {Performance Evaluation of Cyclostationary - Based Cooperative Sensing Using Field Measurements }, BOOKTITLE = {IEEE Transactions on Vehicular Technology}. YEAR = {2016}}
This paper focuses on evaluating the gains obtained through cooperative spectrum sensing in the real world while using cyclostationary-based mobile sensors. In cooperative sensing(CS), different secondary users (SUs) in a geographical neighbor-hood cooperate to detect the presence of a primary user (PU).Compared with single-user sensing, cooperation provides diversity gains in the face of multi path fading and shadowing. The effectiveness of CS is demonstrated by analyzing data acquired in two extensive field measurement campaigns. The first measurement campaign (MC-I) focuses on measurements at fixed locations,whereas the second measurement campaign (MC-II) focuses on a scenario where measurements are taken inside a moving car.These measurements are carried out for DVB-T channels in the Capital Region of Finland, which consists of urban and suburban environments. Hard decision rules such as OR,AND,and MAJOR-ITYand a soft decision rule such as sum of cyclostationary test statistics (SUM) are employed, and their detection performances are compared with a cyclostationary-based single-user detector. A performance parameter of relative increase in probability of detection (RIPD) is used to efficiently demonstrate the cooperation gain obtained relative to local sensing. It is shown that cooperation can significantly improve the performance of a sensor severely affected by fading and shadowing effects. Furthermore, it is shown that increasing the number of collaborating users beyond few users(five to eight) does not, in practice, bring significant improvement in terms of the expected RIPD. The performances of CS schemes evaluated from MC-I are also compared with the corresponding simulated CS results using empirical channel models and terrain data for the same experimental parameters. It is shown that the use of empirical or theoretical models may result in detection errors in practical conditions, and measurements should be used to improve the accuracy in such scenarios.
Detection and Classification of OFDM Waveforms Using Cepstral Analysis
J. Jäntti,Sachin Chaudhari,Visa Koivune
IEEE Transactions on Signal Processing, TSP, 2015
@inproceedings{bib_Dete_2015, AUTHOR = {J. Jäntti, Sachin Chaudhari, Visa Koivune}, TITLE = {Detection and Classification of OFDM Waveforms Using Cepstral Analysis}, BOOKTITLE = {IEEE Transactions on Signal Processing}. YEAR = {2015}}
Cepstral analysis has been widely used in audio and speech processing applications because of its ability to reveal periodicities in a signal. The presence of cyclic prefix (CP) in orthogonal frequency division multiplexing (OFDM) signals induces periodicities. Motivated by this, the paper focuses on cepstral analysis of OFDM signal. The distributions of cepstral coefficients are de-rived for two scenarios of noise only and OFDM signal in noise. Itis shown that the OFDM cepstrum is significantly different from the additive white Gaussian noise (AWGN) cepstrum and can be used to detect OFDM waveforms. It is also shown that the cepstrum of OFDM is rich in features and can be used to estimate OFDM parameters such as number of sub carriers and length of the CPin an OFDM symbol. These OFDM waveform parameters can be used to automatically recognize or classify different OFDM waveforms, which are important for cognitive radios, coexistence of heterogeneous networks and signal intelligence. Two schemes are proposed to detect OFDM based primary user (PU) signalsin cognitive radio systems. The distributions of the test statistics under the two hypotheses are established.Neyman–Pearson detec-tions trategy is employed.Algorithms for estimating the number of subcarrier and the length of the CP are proposed and their performances studied through simulations. Later the proposed schemes are extended to cooperative sensing scenario with multiple secondary users (SUs) and it is shown that the collaboration between them significantly improve the performance of the proposed cepstrum based detection and estimation schemes.
Cooperative Energy Detection using Dempster-Shafer Theory under Noise Uncertainties
PRAKASH BORPATRA GOHAIN,Sachin Chaudhari
International Conference on Communication Systems & Networks, COMSNETS, 2014
@inproceedings{bib_Coop_2014, AUTHOR = {PRAKASH BORPATRA GOHAIN, Sachin Chaudhari}, TITLE = {Cooperative Energy Detection using Dempster-Shafer Theory under Noise Uncertainties}, BOOKTITLE = {International Conference on Communication Systems & Networks}. YEAR = {2014}}
Cooperative spectrum sensing (CSS) is one of the efficient scheme that helps in improving the spectrum sensing performance of a cognitive radio network. In this paper we propose a Dempster-Shafer theory (DST) based CSS for cognitive radio network. The fusion rule is the D-S combination rule which is well known for its ability to handle uncertainty and has been used in quite a different number of fields. Noise uncertainty, which is unavoidable in practical field, can gravely limit the detection performance of CSS. In this paper we specially investigate sensor nodes undergoing uncertainty in noise power when the detection scheme is based on energy of the received signal and use DS theory to minimize such effects. Simulation results reveal significant improvement in CSS gain as compared to previous traditional methods.
