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Mithun Mukherjee
Department of Electronic and Computer Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

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Preprint content
Published: 25 June 2021
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Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.

ACS Style

Mithun Mukherjee; Vikas Kumar; Mian Guo; Daniel B. da Costa; Ertugrul Basar; Zhiguo Ding. The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G. 2021, 1 .

AMA Style

Mithun Mukherjee, Vikas Kumar, Mian Guo, Daniel B. da Costa, Ertugrul Basar, Zhiguo Ding. The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G. . 2021; ():1.

Chicago/Turabian Style

Mithun Mukherjee; Vikas Kumar; Mian Guo; Daniel B. da Costa; Ertugrul Basar; Zhiguo Ding. 2021. "The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G." , no. : 1.

Preprint content
Published: 25 June 2021
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Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.

ACS Style

Mithun Mukherjee; Vikas Kumar; Mian Guo; Daniel B. da Costa; Ertugrul Basar; Zhiguo Ding. The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G. 2021, 1 .

AMA Style

Mithun Mukherjee, Vikas Kumar, Mian Guo, Daniel B. da Costa, Ertugrul Basar, Zhiguo Ding. The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G. . 2021; ():1.

Chicago/Turabian Style

Mithun Mukherjee; Vikas Kumar; Mian Guo; Daniel B. da Costa; Ertugrul Basar; Zhiguo Ding. 2021. "The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G." , no. : 1.

Journal article
Published: 01 March 2021 in IEEE Transactions on Industrial Informatics
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ACS Style

Geetanjali Rathee; Farhan Ahmad; Razi Iqbal; Mithun Mukherjee. Cognitive Automation for Smart Decision-Making in Industrial Internet of Things. IEEE Transactions on Industrial Informatics 2021, 17, 2152 -2159.

AMA Style

Geetanjali Rathee, Farhan Ahmad, Razi Iqbal, Mithun Mukherjee. Cognitive Automation for Smart Decision-Making in Industrial Internet of Things. IEEE Transactions on Industrial Informatics. 2021; 17 (3):2152-2159.

Chicago/Turabian Style

Geetanjali Rathee; Farhan Ahmad; Razi Iqbal; Mithun Mukherjee. 2021. "Cognitive Automation for Smart Decision-Making in Industrial Internet of Things." IEEE Transactions on Industrial Informatics 17, no. 3: 2152-2159.

Journal article
Published: 07 December 2020 in IEEE Journal on Selected Areas in Communications
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Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data. In some degree, single kernel methods face the challenging problem of kernel choice. To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years. However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data; (2) they disregard the highorder correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels. In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for nonlinear data clustering, namely TMKGC. Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs. Afterwards, the highorder structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently. Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function. Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.

ACS Style

Zhenwen Ren; Mithun Mukherjee; Mehdi Bennis; Jaime Lloret. Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks. IEEE Journal on Selected Areas in Communications 2020, 39, 1946 -1956.

AMA Style

Zhenwen Ren, Mithun Mukherjee, Mehdi Bennis, Jaime Lloret. Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks. IEEE Journal on Selected Areas in Communications. 2020; 39 (7):1946-1956.

Chicago/Turabian Style

Zhenwen Ren; Mithun Mukherjee; Mehdi Bennis; Jaime Lloret. 2020. "Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks." IEEE Journal on Selected Areas in Communications 39, no. 7: 1946-1956.

Journal article
Published: 07 October 2020 in IEEE Transactions on Intelligent Transportation Systems
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Radio-frequency-energy-powered cognitive radio network (RF-CRN) is being taken seriously in Connected Vehicles, especially in 5G network, which can better address the challenges of energy limitation and spectrum scarcity. However, the energy efficiency (EE) of the RF-CRN wherein multiple secondary users (SUs) share the same channel is rarely presented. In this article, we consider a RF-CRN in which SUs first harvest energy from RF signals originating from a primary network (PN) and then utilize the available energy in the battery to transmit data. Since all SUs can access the authorized spectrum for transmission simultaneously, co-frequency interference (Co-FI) occurs among SUs. Given the quality of service (QoS) requirement, our goal is to achieve the maximum EE of the RF-CRN by jointly optimizing transmission time and power control. To this end, a resource allocation scheme referred to as approximate convex policy for co-frequency interference (CO-ACP) is proposed. Specifically, the EE problem is firstly converted into a convex one by CO-ACP. Then, we utilize Frank-Wolfe (FW) and one-dimensional linear programming to obtain the optimal solution. Simulation results demonstrate that a tight lower-bound optimum solution for the non-convex EE maximization can be achieved by CO-ACP. Moreover, the CO-ACP provides meaningful system features, such as the number of SUs, energy harvesting efficiency, and the battery energy state of the SUs under different RF-CRN scenarios, providing a clear reference for future deployment of RF-CRN.

ACS Style

He Xiao; Hong Jiang; Fanrong Shi; Ying Luo; Liping Deng; Mithun Mukherjee; Jalil Piran. Energy-Efficient Resource Allocation in Radio-Frequency-Powered Cognitive Radio Network for Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 5426 -5436.

AMA Style

He Xiao, Hong Jiang, Fanrong Shi, Ying Luo, Liping Deng, Mithun Mukherjee, Jalil Piran. Energy-Efficient Resource Allocation in Radio-Frequency-Powered Cognitive Radio Network for Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (8):5426-5436.

Chicago/Turabian Style

He Xiao; Hong Jiang; Fanrong Shi; Ying Luo; Liping Deng; Mithun Mukherjee; Jalil Piran. 2020. "Energy-Efficient Resource Allocation in Radio-Frequency-Powered Cognitive Radio Network for Connected Vehicles." IEEE Transactions on Intelligent Transportation Systems 22, no. 8: 5426-5436.

Article
Published: 12 September 2020 in Multimedia Tools and Applications
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Traditional multimedia forensics techniques inspect images to identify, localize forged regions and estimate forgery methods that have been applied. Provenance filtering is the research area that has been evolved recently to retrieve all the images that are involved in constructing a morphed image in order to analyze an image, completely forensically. This task can be performed in two stages: one is to detect and localize forgery in the query image, and the second integral part is to search potentially similar images from a large pool of images. We propose a multimodal system which covers both steps, forgery detection through deep neural networks(CNN) followed by part based image retrieval. Classification and localization of manipulated region are performed using a deep neural network. InceptionV3 is employed to extract key features of the entire image as well as for the manipulated region. Potential donors and nearly duplicates are retrieved by using the Nearest Neighbour Algorithm. We take the CASIA-v2, CoMoFoD and NIST 2018 datasets to evaluate the proposed system. Experimental results show that deep features outperform low-level features previously used to perform provenance filtering with achieved [email protected] of 92.8%.

ACS Style

Saira Jabeen; Usman Ghani Khan; Razi Iqbal; Mithun Mukherjee; Jaime Lloret. A deep multimodal system for provenance filtering with universal forgery detection and localization. Multimedia Tools and Applications 2020, 80, 17025 -17044.

AMA Style

Saira Jabeen, Usman Ghani Khan, Razi Iqbal, Mithun Mukherjee, Jaime Lloret. A deep multimodal system for provenance filtering with universal forgery detection and localization. Multimedia Tools and Applications. 2020; 80 (11):17025-17044.

Chicago/Turabian Style

Saira Jabeen; Usman Ghani Khan; Razi Iqbal; Mithun Mukherjee; Jaime Lloret. 2020. "A deep multimodal system for provenance filtering with universal forgery detection and localization." Multimedia Tools and Applications 80, no. 11: 17025-17044.

Journal article
Published: 20 July 2020 in IEEE Transactions on Industrial Informatics
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In the cognitive computing of intelligent Industrial Internet of Things (IIoT), clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for non-linear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this paper, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterwards, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any post-processing clustering step. Comprehensive experimental results demonstrate the superiority of our method.

ACS Style

Zhenwen Ren; Mithun Mukherjee; Jaime Lloret; P. Venu. Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT. IEEE Transactions on Industrial Informatics 2020, 17, 2956 -2963.

AMA Style

Zhenwen Ren, Mithun Mukherjee, Jaime Lloret, P. Venu. Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT. IEEE Transactions on Industrial Informatics. 2020; 17 (4):2956-2963.

Chicago/Turabian Style

Zhenwen Ren; Mithun Mukherjee; Jaime Lloret; P. Venu. 2020. "Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT." IEEE Transactions on Industrial Informatics 17, no. 4: 2956-2963.

Journal article
Published: 16 July 2020 in IEEE Transactions on Circuits and Systems for Video Technology
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Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed.

ACS Style

Zhihong Sun; Jun Chen; Liang Chao; Weijian Ruan; Mithun Mukherjee. A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework. IEEE Transactions on Circuits and Systems for Video Technology 2020, 31, 1819 -1833.

AMA Style

Zhihong Sun, Jun Chen, Liang Chao, Weijian Ruan, Mithun Mukherjee. A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework. IEEE Transactions on Circuits and Systems for Video Technology. 2020; 31 (5):1819-1833.

Chicago/Turabian Style

Zhihong Sun; Jun Chen; Liang Chao; Weijian Ruan; Mithun Mukherjee. 2020. "A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework." IEEE Transactions on Circuits and Systems for Video Technology 31, no. 5: 1819-1833.

Journal article
Published: 18 June 2020 in IEEE Internet of Things Journal
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The IEEE 802.15.4 standard specifies two network topologies: star and cluster-tree. A cluster-tree network comprises of multiple clusters that allow the network to scale by connecting devices over multiple wireless hops. The role of a cluster-head (CH) is to aggregate data from all the devices in the cluster and then transmit it to the overall personal area network (PAN) coordinator. This specific role of CH needs to be rotated among multiple coordinators in the cluster to prevent it from energy drain out. Prior works on CH rotation are either based on threshold energy levels or rely on periodic rotation. Both approaches have their respective limitations and, at times, result in unnecessary CH rotations or non-optimal selection of CH. To address this, we propose a non-threshold cluster-head rotation scheme (NCHR), which incurs minimal rotation overhead. It supports topological changes, node heterogeneity, and can also handle CH failures. Through simulations and hardware implementation, the performance of the proposed NCHR scheme is analyzed in terms of network lifetime, CH rotation overhead, and the number of CH rotations. It is shown that the proposed scheme boosts network lifetime, incurs less rotation overhead, and needs fewer CH rotations compared to other related schemes.

ACS Style

Nikumani Choudhury; Rakesh Matam; Mithun Mukherjee; Jaime Lloret; Ezhil Kalaimannan. NCHR: A Nonthreshold-Based Cluster-Head Rotation Scheme for IEEE 802.15.4 Cluster-Tree Networks. IEEE Internet of Things Journal 2020, 8, 168 -178.

AMA Style

Nikumani Choudhury, Rakesh Matam, Mithun Mukherjee, Jaime Lloret, Ezhil Kalaimannan. NCHR: A Nonthreshold-Based Cluster-Head Rotation Scheme for IEEE 802.15.4 Cluster-Tree Networks. IEEE Internet of Things Journal. 2020; 8 (1):168-178.

Chicago/Turabian Style

Nikumani Choudhury; Rakesh Matam; Mithun Mukherjee; Jaime Lloret; Ezhil Kalaimannan. 2020. "NCHR: A Nonthreshold-Based Cluster-Head Rotation Scheme for IEEE 802.15.4 Cluster-Tree Networks." IEEE Internet of Things Journal 8, no. 1: 168-178.

Journal article
Published: 06 May 2020 in IEEE Communications Letters
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In this letter, we study the computational offloading scheme for the delay-aware tasks of the end-users in the fog computing network. We consider a fog federation of different service providers where an individual fog node allocates its computing resources to the end-user in its proximity, while a fog manager coordinates the load balancing among the fog nodes over the entire network. At first, an individual fog node aims to maximize its revenue by selling the computational resources to the end-user in a distributed manner without any global knowledge of the network. To further maximize the overall revenue considering all fog nodes in the fog federation, the fog manager utilizes the remaining computing resources of the underloaded fog nodes. The extensive simulation results show the revenue improvement leveraging fog federation over entire network while maintaining the same and even better delay-performance for the end-users.

ACS Style

Mithun Mukherjee; Vikas Kumar; Jaime Lloret; Qi Zhang. Revenue Maximization in Delay-Aware Computation Offloading Among Service Providers With Fog Federation. IEEE Communications Letters 2020, 24, 1799 -1803.

AMA Style

Mithun Mukherjee, Vikas Kumar, Jaime Lloret, Qi Zhang. Revenue Maximization in Delay-Aware Computation Offloading Among Service Providers With Fog Federation. IEEE Communications Letters. 2020; 24 (8):1799-1803.

Chicago/Turabian Style

Mithun Mukherjee; Vikas Kumar; Jaime Lloret; Qi Zhang. 2020. "Revenue Maximization in Delay-Aware Computation Offloading Among Service Providers With Fog Federation." IEEE Communications Letters 24, no. 8: 1799-1803.

Journal article
Published: 04 April 2020 in Remote Sensing
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As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks.

ACS Style

Jie Kong; Quansen Sun; Mithun Mukherjee; Jaime Lloret. Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval. Remote Sensing 2020, 12, 1164 .

AMA Style

Jie Kong, Quansen Sun, Mithun Mukherjee, Jaime Lloret. Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval. Remote Sensing. 2020; 12 (7):1164.

Chicago/Turabian Style

Jie Kong; Quansen Sun; Mithun Mukherjee; Jaime Lloret. 2020. "Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval." Remote Sensing 12, no. 7: 1164.

Journal article
Published: 27 February 2020 in IEEE Access
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The IEEE 802.15.4 standard is one of the widely adopted networking specification for Internet of Things (IoT). It defines several physical layer (PHY) options and medium access control (MAC) sub-layer protocols for interconnection of constrained wireless devices. These devices are usually battery-powered and need to support requirements like low-power consumption and low-data rates. The standard has been revised twice to incorporate new PHY layers and improvements learned from implementations. Research in this direction has been primarily centered around improving the energy consumption of devices. Recently, to meet specific Quality-of-Service (QoS) requirements of different industrial applications, the IEEE 802.15.4e amendment was released that focuses on improving reliability, robustness and latency. In this paper, we carry out a performance-to-cost analysis of Deterministic and Synchronous Multi-channel Extension (DSME) and Time-slotted Channel Hopping (TSCH) MAC modes of IEEE 802.15.4e with 802.15.4 MAC protocol to analyze the trade-off of choosing a particular MAC mode over others. The parameters considered for performance are throughput and latency, and the cost is quantified in terms of energy. A Markov model has been developed for TSCH MAC mode to compare its energy costs with 802.15.4 MAC. Finally, we present the applicability of different MAC modes to different application scenarios.

ACS Style

Nikumani Choudhury; Rakesh Matam; Mithun Mukherjee; Jaime Lloret. A Performance-to-Cost Analysis of IEEE 802.15.4 MAC With 802.15.4e MAC Modes. IEEE Access 2020, 8, 41936 -41950.

AMA Style

Nikumani Choudhury, Rakesh Matam, Mithun Mukherjee, Jaime Lloret. A Performance-to-Cost Analysis of IEEE 802.15.4 MAC With 802.15.4e MAC Modes. IEEE Access. 2020; 8 (99):41936-41950.

Chicago/Turabian Style

Nikumani Choudhury; Rakesh Matam; Mithun Mukherjee; Jaime Lloret. 2020. "A Performance-to-Cost Analysis of IEEE 802.15.4 MAC With 802.15.4e MAC Modes." IEEE Access 8, no. 99: 41936-41950.

Journal article
Published: 24 December 2019 in IEEE Embedded Systems Letters
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Universal-filtered multi-carrier (UFMC) is one of the potential candidates for 5G multicarrier waveforms due to its several attractive features such as suppressed out-of-band radiation to the nearby subband. However, the hardware realization of UFMC systems is limited by a large number of arithmetic units for inverse fast Fourier transform (IFFT) and pulse shaping filters. In this letter, we propose an architecture that presents a refreshing approach towards designing a low-complexity architecture for the baseband UFMC transmitter with Dolph-Chebyshev filter. Compared to the ROM-based state-of-the-art, the proposed architecture requires less number of ROM locations and has the flexibility to externally select the inverse discrete Fourier transform (IDFT)-size, number of subbands, and number of subcarriers in a subband. Moreover, we implement the proposed architecture on commercially available Virtex-5 field-programmable gate array (FPGA) device for testing and analyzing the baseband UFMC signal. Finally, the XILINX post-route results are found comparable with MATLAB simulations.

ACS Style

Vikas Kumar; Mithun Mukherjee; Jaime Lloret. A Hardware-Efficient and Reconfigurable UFMC Transmitter Architecture With its FPGA Prototype. IEEE Embedded Systems Letters 2019, 12, 109 -112.

AMA Style

Vikas Kumar, Mithun Mukherjee, Jaime Lloret. A Hardware-Efficient and Reconfigurable UFMC Transmitter Architecture With its FPGA Prototype. IEEE Embedded Systems Letters. 2019; 12 (4):109-112.

Chicago/Turabian Style

Vikas Kumar; Mithun Mukherjee; Jaime Lloret. 2019. "A Hardware-Efficient and Reconfigurable UFMC Transmitter Architecture With its FPGA Prototype." IEEE Embedded Systems Letters 12, no. 4: 109-112.

Journal article
Published: 02 December 2019 in IEEE Transactions on Industrial Informatics
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ACS Style

Mithun Mukherjee; Suman Kumar; Constandinos X. Mavromoustakis; George Mastorakis; Rakesh Matam; Vikas Kumar; Qi Zhang. Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications. IEEE Transactions on Industrial Informatics 2019, 16, 6050 -6058.

AMA Style

Mithun Mukherjee, Suman Kumar, Constandinos X. Mavromoustakis, George Mastorakis, Rakesh Matam, Vikas Kumar, Qi Zhang. Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications. IEEE Transactions on Industrial Informatics. 2019; 16 (9):6050-6058.

Chicago/Turabian Style

Mithun Mukherjee; Suman Kumar; Constandinos X. Mavromoustakis; George Mastorakis; Rakesh Matam; Vikas Kumar; Qi Zhang. 2019. "Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications." IEEE Transactions on Industrial Informatics 16, no. 9: 6050-6058.

Journal article
Published: 10 October 2019 in IEEE Internet of Things Journal
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ACS Style

Mainak Adhikari; Mithun Mukherjee; Satish Narayana Srirama. DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing. IEEE Internet of Things Journal 2019, 7, 5773 -5782.

AMA Style

Mainak Adhikari, Mithun Mukherjee, Satish Narayana Srirama. DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing. IEEE Internet of Things Journal. 2019; 7 (7):5773-5782.

Chicago/Turabian Style

Mainak Adhikari; Mithun Mukherjee; Satish Narayana Srirama. 2019. "DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing." IEEE Internet of Things Journal 7, no. 7: 5773-5782.

Journal article
Published: 16 September 2019 in IEEE Access
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With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-user’s limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.

ACS Style

Mithun Mukherjee; Suman Kumar; Qi Zhang; Rakesh Matam; Constandinos X. Mavromoustakis; Yunrong Lv; George Mastorakis. Task Data Offloading and Resource Allocation in Fog Computing With Multi-Task Delay Guarantee. IEEE Access 2019, 7, 152911 -152918.

AMA Style

Mithun Mukherjee, Suman Kumar, Qi Zhang, Rakesh Matam, Constandinos X. Mavromoustakis, Yunrong Lv, George Mastorakis. Task Data Offloading and Resource Allocation in Fog Computing With Multi-Task Delay Guarantee. IEEE Access. 2019; 7 (99):152911-152918.

Chicago/Turabian Style

Mithun Mukherjee; Suman Kumar; Qi Zhang; Rakesh Matam; Constandinos X. Mavromoustakis; Yunrong Lv; George Mastorakis. 2019. "Task Data Offloading and Resource Allocation in Fog Computing With Multi-Task Delay Guarantee." IEEE Access 7, no. 99: 152911-152918.

Journal article
Published: 24 July 2019 in IEEE Systems Journal
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ACS Style

Vikas Kumar; Mithun Mukherjee; Jaime Lloret. Reconfigurable Architecture of UFMC Transmitter for 5G and Its FPGA Prototype. IEEE Systems Journal 2019, 14, 28 -38.

AMA Style

Vikas Kumar, Mithun Mukherjee, Jaime Lloret. Reconfigurable Architecture of UFMC Transmitter for 5G and Its FPGA Prototype. IEEE Systems Journal. 2019; 14 (1):28-38.

Chicago/Turabian Style

Vikas Kumar; Mithun Mukherjee; Jaime Lloret. 2019. "Reconfigurable Architecture of UFMC Transmitter for 5G and Its FPGA Prototype." IEEE Systems Journal 14, no. 1: 28-38.

Journal article
Published: 15 July 2019 in Sensors
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The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

ACS Style

Abdelouahid Derhab; Mohamed Guerroumi; Abdu Gumaei; Leandros Maglaras; Mohamed Amine Ferrag; Mithun Mukherjee; Farrukh Aslam Khan. Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security. Sensors 2019, 19, 3119 .

AMA Style

Abdelouahid Derhab, Mohamed Guerroumi, Abdu Gumaei, Leandros Maglaras, Mohamed Amine Ferrag, Mithun Mukherjee, Farrukh Aslam Khan. Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security. Sensors. 2019; 19 (14):3119.

Chicago/Turabian Style

Abdelouahid Derhab; Mohamed Guerroumi; Abdu Gumaei; Leandros Maglaras; Mohamed Amine Ferrag; Mithun Mukherjee; Farrukh Aslam Khan. 2019. "Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security." Sensors 19, no. 14: 3119.

Conference paper
Published: 18 October 2018 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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With the advancement in Internet of Things (Iot), the speech recognition technology in mobile terminals’ applications has become a new trend. Consequently, how to accelerate the training and improve the accuracy in speech recognition has attracted the attention of academia and industry. Generally, Deep Belief Network (DBN) with Graphic Processing Unit (GPU) is applied in acoustic model of speech recognition, critical research challenges are yet to be solved. It’s hard for GPU to store the parameters of DBN at one time as well as GPU’s shared memory is not fully used. And parameters transmission have become a bottleneck in multi-GPUs. This paper presents a new method in which the weight matrix is divided into sub-weight matrices and established a reasonable memory model. To eliminate the inefficient idle-state during data transfers, a stream process model is proposed in which the data transfer and kernel execution are performed simultaneously. Further, apply the optimized single GPU implementation to multi-GPUs and is intend to solve the parameters transmission. Experimental results show the optimized GPU implementation without violating the size limitation of GPU’s memory.

ACS Style

Weipeng Jing; Tao Jiang; Mithun Mukherjee; Lei Shu; Jian Kang. An Optimized Implementation of Speech Recognition Combining GPU with Deep Belief Network for IoT. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, 251 -260.

AMA Style

Weipeng Jing, Tao Jiang, Mithun Mukherjee, Lei Shu, Jian Kang. An Optimized Implementation of Speech Recognition Combining GPU with Deep Belief Network for IoT. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2018; ():251-260.

Chicago/Turabian Style

Weipeng Jing; Tao Jiang; Mithun Mukherjee; Lei Shu; Jian Kang. 2018. "An Optimized Implementation of Speech Recognition Combining GPU with Deep Belief Network for IoT." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 251-260.

Journal article
Published: 04 October 2018 in IEEE Transactions on Industrial Informatics
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The papers in this special section examine the use of fog computing applications in industrial electronics. Due to the increased number of connected things in industrial applications, the growing volume and velocity of Internet of Things (IoTs) data exchange urge for more and more communication resources, leading to the bottleneck in terms of data processing, data latency, and traffic overhead. Fog computing emerges as an alternative for traditional cloud computing to support geographically distributed, latency-sensitive, and QoS-aware IoT applications while reducing the burden of data centers in traditional cloud computing. In particular, fog computing with the features (e.g., low latency, location awareness, and capacity of processing large number of nodes with wireless access) to support heterogeneity and real-time applications is an attractive solution to delay- and resource-constraint large-scale industrial applications. However, with the benefits of fog computing, the research challenges arise regarding fog computing for industrial applications.

ACS Style

Lei Shu; Gerhard P Hancke; Der-Jiunn Deng; Chunsheng Zhu; Mithun Mukherjee. Guest Editorial Fog Computing for Industrial Applications. IEEE Transactions on Industrial Informatics 2018, 14, 4481 -4486.

AMA Style

Lei Shu, Gerhard P Hancke, Der-Jiunn Deng, Chunsheng Zhu, Mithun Mukherjee. Guest Editorial Fog Computing for Industrial Applications. IEEE Transactions on Industrial Informatics. 2018; 14 (10):4481-4486.

Chicago/Turabian Style

Lei Shu; Gerhard P Hancke; Der-Jiunn Deng; Chunsheng Zhu; Mithun Mukherjee. 2018. "Guest Editorial Fog Computing for Industrial Applications." IEEE Transactions on Industrial Informatics 14, no. 10: 4481-4486.