Ashish Rauniyar

• Rauniyar, Ashish; Østerbø, Olav Norvald; Håkegård, Jan Erik & Engelstad, Paal E. (2022). Secrecy Performance Analysis of Cooperative Nonorthogonal Multiple Access in IoT Networks. IEEE Sensors Journal. ISSN 1530-437X. 22(19), p. 19030–19045. doi: 10.1109/JSEN.2022.3199540. Full text in Research Archive Show summary

  Different system models utilizing Non-orthogonal multiple access (NOMA) have been successfully studied to meet the growing capacity demands of the Internet of Things (IoT) devices for the next-generation networks. However, analyzing the anti-eavesdropping for NOMA systems under different scenarios and settings still needs further exploration before it can be practically deployed. Therefore, in this paper, we study the secrecy performance of a cooperative NOMA system in IoT networks where two source nodes communicate with their respective destination nodes via a common relay in the presence of an eavesdropper. Specifically, two source node sends their data in parallel over the same frequency band to the common relay node using uplink NOMA. Then, the relay node forwards the decoded symbols to the respective destination nodes using downlink NOMA in the presence of an eavesdropper. To enhance the security performance of the considered system, we study and propose an artificial noise (AN)-aided scheme in which the two destination nodes emit a jamming signal to confuse the eavesdropper while receiving the signal from the common relay node. We also study the effect of NOMA power allocation, perfect successive interference cancellation (pSIC), and imperfect SIC (ipSIC) on the considered system. Analytical expressions for the Ergodic capacity, Ergodic secrecy sum rate (ESSR), and secrecy outage probability (SOP) are mathematically derived and verified with the simulation results. Our results demonstrate that a significantly higher ESSR and lower SOP of the system can be attained compared to a conventional NOMA system without a destination-assisted jamming signal scheme.

• Rauniyar, Ashish; Hagos, Desta Haileselassie; Jha, Debesh & Håkegård, Jan Erik (2022). COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics. Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. ISSN 1551-2282. p. 216–220. doi: 10.1109/SAM53842.2022.9827805. Full text in Research Archive Show summary

  Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even the world’s best healthcare-providing countries could not handle the pandemic because of the strain of treating thousands of patients at a time. The count of infections and deaths is increasing at an alarming rate because of the spread of the virus. We believe that innovative technologies could help reduce pandemics to a certain extent until we find a definite solution from the medical field to handle and treat such pandemic situations. Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, this paper proposes the idea of using drones as a companion to tackle current and future pandemics. Our COROID drone is based on the principle of crowdsourcing sensors data of the public's smart devices, which can correlate the reading of the infrared cameras equipped on COROID drones. To the best of our knowledge, this concept has yet to be investigated either as a concept or as a product. Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.

• Rauniyar, Ashish; Berge, Truls Svenn & Håkegård, Jan Erik (2022). NEMO: Internet of Things based Real-time Noise and Emissions MOnitoring System for Smart Cities. Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. ISSN 1551-2282. p. 206–210. doi: 10.1109/SAM53842.2022.9827835. Full text in Research Archive Show summary

  With the advent of ubiquitous sensors and Internet of Things (IoT) applications, research and development initiatives on smart cities are ramping up worldwide. It enables remote monitoring, management, and control of devices and the generation of fresh and actionable insight from huge quantities of real-time data. Real-time noise and emissions monitoring of vehicles remain indispensable in a smart city context. Effective management and control of noise and emissions of vehicles on the road are necessary and possible through analyzing lots of sensor data in real-time to take an actionable insight. To contribute to this, as part of an ongoing effort of the European Union project called ''NEMO: Noise and Emissions Monitoring and Radical Mitigation'', in this paper, we present the design and development of an IoT-based real-time noise and emissions monitoring system for vehicles in a smart city context. Real-world sensor data of the vehicles in some European cities are collected during the pilot tests. We have developed a complete application for infrastructure managers and analysts to monitor the sensor data related to noise and emissions of vehicles in real-time. The data of the individual road vehicles and trains in selected EU cities and from trains on a track in the Netherlands are collected in the cloud and analyzed with artificial intelligence (AI) algorithms for classification such as high emitter, medium emitter, and normal emitters. We present the development of a complete software solution that can be integrated with existing intelligent transportation systems in smart cities. Finally, we report the initial vehicle classification results from the Rotterdam (Netherlands) pilot test as a representative example for the NEMO monitoring system.

• Jha, Debesh; Rauniyar, Ashish; Johansen, Håvard D.; Johansen, Dag; Riegler, Michael Alexander & Halvorsen, Pål [Show all 7 contributors for this article] (2022). Video Analytics in Elite Soccer: A Distributed Computing Perspective. Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. ISSN 1551-2282. p. 221–225. doi: 10.1109/SAM53842.2022.9827827. Full text in Research Archive Show summary

  Ubiquitous sensors and Internet of Things (IoT)technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post-game. New methods, including machine learning, image, and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA’s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing, and its importance in video analytics and propose a future research perspective.

• Rauniyar, Saurab; Jha, Vabesh Kumar; Jha, Ritika Kumari; Jha, Debesh & Rauniyar, Ashish (2021). Improving Polyp Segmentation in Colonoscopy using Deep Learning . Nordic Machine Intelligence (NMI). ISSN 2703-9196. 1(1), p. 1–3. doi: 10.5617/nmi.9136.
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Exploiting SWIPT for IoT NOMA-based Diamond Relay Networks. In Mühlhäuser, Max; Polyzos, George C.; Michahelles, Florian; Guinea, Alejandro Sánchez & Wang, Lin (Ed.), MobiQuitous '20: Computing, Networking and Services, Virtual Event / Darmstadt, Germany, December 7-9, 2020. ACM 2020. ACM Digital Library. ISSN 978-1-4503-8840-5. p. 137–146. doi: 10.1145/3448891.3448931.
• Rauniyar, Ashish; Yazidi, Anis; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). A Reinforcement Learning based Game Theoretic Approach for Distributed Power Control in Downlink NOMA . In Avresky, Dimiter R. (Eds.), 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-8327-5. doi: 10.1109/NCA51143.2020.9306737.
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). An Adaptive User Pairing Strategy for Uplink Non-Orthogonal Multiple Access. In Darwazeh, Izzat & Aghvami, Hamid (Ed.), 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-4491-7. doi: 10.1109/PIMRC48278.2020.9217383. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Ergodic Capacity Performance of D2D IoT Relay NOMA-SWIPT Systems with Direct Links. In Herencsar, Norbert & Benedetto, Francesco (Ed.), 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-6377-2. p. 117–123. doi: 10.1109/TSP49548.2020.9163552.
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Ergodic Capacity Performance of NOMA-SWIPT Aided IoT Relay Systems with Direct Link. In Huang, Longbo; Koutsopoulos, Iordanis & Subramanian, Vijay (Ed.), 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-3-903176-29-4. p. 222–229.
• Upreti, Ramesh; Rauniyar, Ashish; Kunwar, Jeevan; Haugerud, Hårek; Engelstad, Paal E. & Yazidi, Anis (2020). Adaptive Pursuit Learning for Energy‐efficient Target Coverage in Wireless Sensor Networks. Concurrency and Computation. ISSN 1532-0626. doi: 10.1002/cpe.5975. Full text in Research Archive Show summary

  With the proliferation of technologies such as wireless sensor networks (WSNs) and the Internet of things (IoT), we are moving towards the era of automation without any human intervention. Sensors are the principal components of the WSNs that bring the idea of IoT into reality. Over the last decade, WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care monitoring, and so on. However, the energy efficiency of the sensor nodes in WSN remains a challenging issue due to the use of a small battery. Moreover, replacing the batteries of the sensor nodes deployed in a hostile environment frequently is not a feasible option. Therefore, intelligent scheduling of the sensor nodes for optimizing its energy-efficient operation and thereby extending the lifetime of WSN has received a lot of research attention lately. In particular, this article investigates extending the lifetime of the WSN in the context of target coverage problems. To tackle this problem, we propose a scheduling technique for WSN based on a novel concept within the theory of learn-ing automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with anLA so that it can autonomously select its proper state, that is, either sleep or active, with an aim to cover all targets with the lowest energy cost possible. Our comprehensive experimental testing of the proposed algorithm not only verifies the efficiency of our algorithm, but it also demonstrates its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). On the Performance of Bidirectional NOMA-SWIPT Enabled IoT Relay Networks. IEEE Sensors Journal. ISSN 1530-437X. 21(2), p. 2299–2315. doi: 10.1109/JSEN.2020.3018905. Full text in Research Archive Show summary

  In this paper, a bidirectional relaying (BR) non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) termed as BR NOMA-SWIPT is proposed and investigated for the Internet of Things (IoT) relay networks. Here, multiple NOMA users in one group can communicate or exchange information with multiple NOMA users in another group through a common energy harvesting (EH) based relay. The EH based relay exploits the radio frequency (RF) energy supplied by the two NOMA user groups to recharge itself, and then it exchanges the information between them. Specifically, the two groups of NOMA users transmit the information intended for the exchange to the relay node using the uplink NOMA protocol. The relay node first harvests the RF energy through the signals of the two group of NOMA users, and then it carries out the exchange of information between two NOMA user groups by using the downlink NOMA protocol. To the best of our knowledge, there is no existing study or research on BR NOMA with SWIPT. Therefore, in this paper, we examine in detail, the performance of BR NOMA-SWIPT enabled IoT relay networks. Specifically, we study a popular EH time switching (TS) relaying architecture with BR and NOMA. We also study the effect of both perfect successive interference cancellation (pSIC) and imperfect SIC (ipSIC) on the proposed BR NOMA-SWIPT system. Analytical expressions for the outage probability and ergodic capacity are mathematically derived. The analytical results of our proposed system model are validated by the simulation results, and representative performance comparisons are presented thoroughly, which not only provides practical insights into the effect of different system parameters on the overall network performance, but it also demonstrates that our proposed BR NOMA-SWIPT can attain significant throughput and capacity gains as compared to conventional BR multiple access schemes.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Ergodic Sum Capacity Analysis of NOMA‐SWIPT Enabled IoT Relay Systems. Internet Technology Letters (ITL). doi: 10.1002/itl2.218. Show summary

  Instead of using the participating relay node only for relaying the data of the source node, in this letter, we have also considered transmitting the data of the relay node along with the source node data based on time switch-ing (TS), power splitting (PS) relaying and NOMA. Therefore, in this context, we investigate the ergodic sum capacity (ESC) analysis of simultaneous wireless information and power transfer (SWIPT) based non-orthogonal multiple access (NOMA) for the Internet of Things (IoT) relay systems. Furthermore, we also study the effect of both perfect successive interference cancellation (pSIC) and imperfect SIC (ipSIC) on the proposed system. Analytical expressions for the ESC under both pSIC and ipSIC scenarios are mathematically derived and corroborated through Monte-Carlo simulations. Our result demonstrates that the PS relaying with NOMA is more energy-efficient than TS relaying with NOMAfor the proposed system.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Capacity Enhancement of NOMA‐SWIPT IoT Relay System with Direct Links over Rayleigh Fading Channels. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. doi: 10.1002/ett.3913. Show summary

  It is known that when the direct links between the base station (BS) and the users exist and are nonnegligible, consolidating direct links could significantly enhance the performance of the cooperative relaying systems. Therefore, taking the impact of direct link into account, in this article, we investigate the capacity enhancement of the nonorthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) for the Internet of Things (IoT) relay systems over the Rayleigh fading channels. Specifically, for the considered NOMA‐SWIPT system with direct links, we study a time‐switching energy harvesting (EH) architecture in which a BS transmits two symbols to the two users through the direct links and via an EH‐based relay node. On the receiving user node, we employ maximum ratio combining (MRC) to show the capacity enhancement of the considered system. Moreover, analytical expressions for the Ergodic capacity and Ergodic Sum Capacity are mathematically derived for the MRC and single signal decoding scheme, and the analytical expressions are corroborated with the Monte‐Carlo simulation results. This not only reveals the effect of different EH parameters on the system performance but it also demonstrates the capacity enhancement of the considered NOMA‐SWIPT system compared to a similar system without direct links and to conventional OMA schemes.

• Pokhrel, Ramesh; Rauniyar, Ashish & Yazidi, Anis (2019). In the Quest of Trade-off between Job Parallelism and Throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly. In Tian, Hui; Shen, Hong & Tan, Wee Lum Tan (Ed.), 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies. IEEE conference proceedings. ISSN 978-1-7281-2616-6. p. 448–453. doi: 10.1109/PDCAT46702.2019.00086.
• Rauniyar, Ashish; Kunwar, Jeevan; Haugerud, Hårek; Yazidi, Anis & Engelstad, Paal E. (2019). Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning. In Jemili, Imem & Mosbah, Mohamed (Ed.), Distributed Computing for Emerging Smart Networks. Springer Nature. ISSN 978-3-030-40131-3. p. 133–147. doi: 10.1007/978-3-030-40131-3_9. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2019). Performance Analysis of RF Energy Harvesting and Information Transmission based on NOMA with Interfering Signal for IoT Relay Systems. IEEE Sensors Journal. ISSN 1530-437X. 19(17), p. 7668–7682. doi: 10.1109/JSEN.2019.2914796. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2018). RF Energy Harvesting and Information Transmission in IoT Relay Systems based on Time Switching and NOMA. In Gregory, Mark A (Eds.), 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). IEEE conference proceedings. ISSN 978-1-5386-7177-1. p. 168–174. doi: 10.1109/ATNAC.2018.8615403.
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2018). RF Energy Harvesting and Information Transmission based on Power Splitting and NOMA for IoT Relay Systems, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA). IEEE conference proceedings. ISSN 978-1-5386-7659-2. doi: 10.1109/NCA.2018.8548068.
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2018). RF energy harvesting and information transmission based on NOMA for wireless powered IoT relay systems. Sensors. ISSN 1424-8220. 18:3254(10), p. 1–22. doi: 10.3390/s18103254. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Moen, Hans Jonas Fossum (2018). A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things. IEEE Vehicular Technology Conference (VTC) Proceedings. ISSN 1550-2252. 2018-June, p. 1–7. doi: 10.1109/VTCSpring.2018.8417665.
• Rauniyar, Ashish & Shin, Soo Young (2018). Cooperative spectrum sensing based on adaptive activation of energy and preamble detector for cognitive radio networks. APSIPA Transactions on Signal and Information Processing (SIP). ISSN 2048-7703. 7(2), p. 1–7. doi: 10.1017/ATSIP.2018.5.
• Rauniyar, Ashish; Hagos, Desta Haileselassie & Shrestha, Manish (2018). A Crowd-Based Intelligence Approach for Measurable Security, Privacy, and Dependability in Internet of Automated Vehicles with Vehicular Fog. Mobile Information Systems. ISSN 1574-017X. 2018, p. 1–14. doi: 10.1155/2018/7905960. Full text in Research Archive Show summary

  With the advent of Internet of things (IoT) and cloud computing technologies, we are in the era of automation, device-to-device (D2D) and machine-to-machine (M2M) communications. Automated vehicles have recently gained a huge attention worldwide, and it has created a new wave of revolution in automobile industries. However, in order to fully establish automated vehicles and their connectivity to the surroundings, security, privacy, and dependability always remain a crucial issue. One cannot deny the fact that such automatic vehicles are highly vulnerable to different kinds of security attacks. Also, today’s such systems are built from generic components. Prior analysis of different attack trends and vulnerabilities enables us to deploy security solutions effectively. Moreover, scientific research has shown that a “group” can perform better than individuals in making decisions and predictions. Therefore, this paper deals with the measurable security, privacy, and dependability of automated vehicles through the crowd-based intelligence approach that is inspired from swarm intelligence. We have studied three use case scenarios of automated vehicles and systems with vehicular fog and have analyzed the security, privacy, and dependability metrics of such systems. Our systematic approaches to measuring efficient system configuration, security, privacy, and dependability of automated vehicles are essential for getting the overall picture of the system such as design patterns, best practices for configuration of system, metrics, and measurements.

• Rauniyar, Ashish; Irfan, Mohammad; Saputra, Oka Danil; Kim, Jin Woo; Lee, Ah Ra & Jang, Jae Min [Show all 7 contributors for this article] (2017). Design and Development of a Real-Time Monitoring System for Multiple Lead–Acid Batteries Based on Internet of Things. Future Internet. ISSN 1999-5903. 9(3), p. 1–16. doi: 10.3390/fi9030028. Show summary

  In this paper, real-time monitoring of multiple lead-acid batteries based on Internet of things is proposed and evaluated. Our proposed system monitors and stores parameters that provide an indication of the lead acid battery’s acid level, state of charge, voltage, current, and the remaining charge capacity in a real-time scenario. To monitor these lead–acid battery parameters, we have developed a data acquisition system by building an embedded system, i.e., dedicated hardware and software. The wireless local area network is used as the backbone network. The information collected from all the connected battery clients in the system is analyzed in an asynchronous transmission control protocol/user datagram protocol-based C♯ server program running on a personal computer (server) to determine important parameters like the state of charge of the individual battery, and if required, appropriate action can be taken in advance to prevent excessive impairment to the battery. Further, data are also displayed on an Android mobile device and are stored in an SQL server database. We have developed a real prototype to devise an end product for our proposed system.

• Rauniyar, Ashish; Engelstad, Paal E.; Feng, Boning & Do, van Thanh (2016). Crowdsourcing-based Disaster Management using Fog Computing in Internet of Things Paradigm. In Bilof, Randall (Eds.), 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-4607-2. p. 490–494. doi: 10.1109/CIC.2016.074.
• Rauniyar, Ashish & Shin, Soo Young (2015). A Novel Energy-Efficient Clustering Based Cooperative Spectrum Sensing for Cognitive Radio Sensor Networks. International Journal of Distributed Sensor Networks (IJDSN). ISSN 1550-1329. 11, p. 1–8. doi: 10.1155/2015/198456. Show summary

  Cognitive radio has been proposed as a promising way to effectively utilize the scarce spectrum resources. A cognitive radio sensor network (CRSN) is a wireless sensor network that is equipped with cognitive radio capability. Clustering is a popular technique that can be applied to wireless sensor networks, although it has been proven to be a challenge to implement it in CRSNs. Moreover, few proposals have successfully applied energy-efficient clustering techniques in CRSNs. Therefore, with the aim of increasing energy efficiency, network lifetime, network stability, and optimal cluster-head selection process, this paper proposes a novel energy-efficient clustering based on cooperative spectrum sensing (ECS) for CRSNs. The proposed ECS scheme utilizes the concept of pairing among sensor nodes and switches between Awake and Sleep modes for energy efficiency. A comprehensive simulation in MATLAB was carried out to validate the proposed method. The simulation results show that, compared with conventional methods, the proposed method is more energy efficient and that the overall CRSN's lifetime is prolonged.

• Triwicaksono, Dimas; Rauniyar, Ashish & Shin, Soo Young (2015). Cascaded Energy Detector and Matched Filter-Clear Channel Assessment for Wireless Network. Wireless personal communications. ISSN 0929-6212. 84(4), p. 2427–2443. doi: 10.1007/s11277-015-2713-7. Show summary

  In this paper, a novel cascaded-clear channel assessment (CCA) method: cascaded energy detector matched filter (CEM) is proposed for wireless network. CEM cartel the advantages of two CCA mechanisms, energy efficient energy detector (ED) and reliable matched filter (MF) to devise a new approach of CCA in wireless network. The probability of detection and false alarm of CEM are depicted in receiver operation curves and are compared with other CCA methods such as ED, MF, preamble detection (PD), and cascaded CCA (Shin et al. in Trans Internet Inf Syst 5(8):1404–1422, 2011) through simulations and analytic calculations. The performance of CEM is evaluated via simulations that implement CCA modules and medium access control protocol for IEEE 802.15.4 as a representative example. Throughput, energy consumption and energy efficiency are used for the performance measurement of our proposed CEM detector. Simulation results show that CEM has higher energy efficiency among other CCA methods and higher throughput compared to Shin et al. (Trans Internet Inf Syst 5(8):1404–1422, 2011).

• Rauniyar, Ashish & Shin, Soo Young (2015). Multiple Antenna-Aided Cascaded Energy and Matched Filter Detector for Cognitive Radio Networks. International Journal of Distributed Sensor Networks (IJDSN). ISSN 1550-1329. 11(9). doi: 10.1155/2015/175943. Show summary

  A new multiple antenna-aided cascaded energy and matched filter detector (MCEM) for cognitive radio networks is proposed. Our proposed scheme is equipped with multiple receive antennas employing energy efficient energy detector (ED) and reliable matched filter (MF) to mitigate the channel fading and increase the detection performance of the secondary users (SUs). Partial decisions are first made by using cascaded energy and matched filter detector (CEM) at each antenna and then the final collaborative decision is made based on those partial decisions. The probability of detection/false alarm of the proposed scheme is presented in terms of the complementary receiver operating characteristics (ROCs). The performance of MCEM scheme is explored via MATLAB simulations that implement the clear channel assessment (CCA) modules for IEEE 802.15.4. Simulation results showed that our proposed MCEM scheme improves the detection performance and is more energy efficient as compared to CEM scheme employing single antenna and other conventional schemes.

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• Rauniyar, Saurab; Jha, Vabesh Kumar; Jha, Ritika Kumari; Jha, Debesh; Rauniyar, Ashish & Srivastava, Abhishek (2021). Automated Polyp Segmentation in Colonoscopy using MSRFNet.
• Rauniyar, Ashish; Jeong, Dae Ro; Chand, G.S.L. Krsihna & Shin, Soo Young (2015). Performance Analysis of Cascaded Energy and Matched Filter Detector With Malicious Users in Cognitive Radio Networks. Show summary

  The inefficient use of valuable and scarce spectrum resources can be exploited by cognitive radio (CR) technology. For CR technology, an energy-efficient detection techniques with reliable detection performance can guarantee the protection of primary users (PU) from secondary users (SU) and prolong the lifetime of the networks. Also, such CR networks are prone to different kinds of attacks which put down the effective operation of the network. In this paper, we consider the performance analysis of cascaded energy and matched filter (CEM) detector in the presence of malicious users (MU). The observable sensing operation of CEM detector i.e., probability of detection is analyzed with the incorrect sensing information of the MUs. To bring down the consequence of MUs on the spectrum sensing efficiency, we suggest the use of authentication code which will be validated by the fusion center (FC) for overall decision-making process. We also showed that there lies an optimum authentication code required for the detection efficiency as the number of MU increases in the network. The equations for cooperative CEM detector’s false alarm and miss detection probabilities are formulated in the presence of MUs. Simulation results show the validity of suggested authentication code scheme for cooperative CEM detectors with MUs.

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