Satyabrata Maity

Bio: Satyabrata Maity is an academic researcher from Information Technology University. The author has contributed to research in topics: Histogram & Sensor node. The author has an hindex of 4, co-authored 11 publications receiving 44 citations. Previous affiliations of Satyabrata Maity include Siksha O Anusandhan University.

Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

Abstract: Fault detection in sensor nodes is a pertinent issue that has been an important area of research for a very long time. But it is not explored much as yet in the context of Internet of Things. Internet of Things work with a massive amount of data so the responsibility for guaranteeing the accuracy of the data also lies with it. Moreover, a lot of important and critical decisions are made based on these data, so ensuring its correctness and accuracy is also very important. Also, the detection needs to be as precise as possible to avoid negative alerts. For this purpose, this work has adopted Dempster–Shafer theory of evidence to collate the information from sensors to come up with a decision regarding the faulty status of a sensor node from a data-centric perspective. To verify the validity of the proposed method, simulations have been performed on a benchmark data set and data collected through a test bed in a laboratory set-up. For the different types of faults, the proposed method shows very high accuracy for both the benchmark (99.8%) and laboratory data sets (99.9%) when compared to the other state-of-the-art machine learning techniques.

IoT Based Secure Smart City Architecture Using Blockchain

Abstract: Standard security protocols are heavy weight interms of memory foot prints which make all the security protocol unfit for budgeted platforms such as Internet of Things (IoT). The blockchain (BC) is a very efficient architecture to preserve five basic cryptographic primitives, such as authenticity, integrity, confidentiality, availability and non-repudiation. Conventional adoption of blockchain in IoT causes significant energy consumption, delay, and computational overhead which are not suitable for various resource-constrained IoT devices. In our submission we change the basic architecture of blockchain and make it more efficient for IoT application. The article proposes an IoT based smart city architecture which adopted the blockchain technology preserving all the cryptographic security issues. The adoption of blockchain causes very minimal overhead on IoT platform. Comparison of all security parameters with existing literature shows that the architecture is reasonably efficient in terms of security

Robust Human Action Recognition Using AREI Features and Trajectory Analysis from Silhouette Image Sequence

Abstract: In the present work, we have proposed an efficient approach for human action recognition (HAR) from silhouette image sequence in videos. The efficiency of the approach lies in feature extraction an...

Detection of Moving Objects in Video Using Block-Based Approach

Abstract: In this paper, an efficient technique has been proposed to detect moving objects in the video under dynamic as well as static background condition. The proposed method consists block-based background modelling, current frame updating, block processing of updated current frame and elimination of background using bin histogram approach. Next, enhanced foreground objects are obtained in the post-processing stage using morphological operations. The proposed approach effectively minimizes the effect of dynamic background to extract the foreground information. We have applied our proposed technique on Change Detection CDW-2012 dataset and compared the results with the other state-of-the-art methods. The experimental results prove the efficiency of the proposed approach compared to the other state-of-the-art methods in terms of different evaluation metrics.

Block-Based Quantized Histogram (BBQH) for efficient background modeling and foreground extraction in video

Abstract: This paper proposes an efficient way of background modeling and elimination for extracting foreground information from the video, applying a new block-based statistical feature extraction technique coined as Block Based Quantized Histogram (BBQH) for background modeling. The inclusion of contrast normalization and anisotropic smoothing in the preprocessing step, makes the feature extraction procedure more robust towards several unorthodox situations like illumination change, dynamic background, bootstrapping, noisy video and camouflaged conditions. The experimental results on the benchmark video frames clearly demonstrate that BBQH has successfully extracted the foreground information despite the various irregularities. BBQH also gives the best F-measure values for most of the benchmark videos in comparison with the other state of the art methods, and hence its novelty is well justified.

Blockchain for 5G and beyond networks: A state of the art survey

Abstract: The fifth generation (5G) wireless networks are on the way to be deployed around the world. The 5G technologies target to support diverse vertical applications by connecting heterogeneous devices and machines with drastic improvements in terms of high quality of service, increased network capacity and enhanced system throughput. However, 5G systems still remain a number of security challenges that have been mentioned by researchers and organizations, including decentralization, transparency, risks of data interoperability, and network privacy vulnerabilities. Furthermore, the conventional techniques may not be sufficient to deal with the security requirements of 5G. As 5G is generally deployed in heterogeneous networks with massive ubiquitous devices, it is quite necessary to provide secure and decentralized solutions. Motivated from these facts, in this paper we provide a state-of-the-art survey on the integration of blockchain with 5G networks and beyond. In this detailed survey, our primary focus is on the extensive discussions on the potential of blockchain for enabling key 5G technologies, including cloud computing, edge computing, Network Function Virtualization, Network Slicing, and D2D communications. We then explore and analyse the opportunities that blockchain potentially empowers important 5G services, ranging from spectrum management, data sharing, network virtualization, resource management to interference management, federated learning, privacy and security provision. The recent advances in the applications of blockchain in 5G Internet of Things are also surveyed in a wide range of popular use-case domains, such as smart healthcare, smart city, smart transportation, smart grid and UAVs. The main findings derived from the comprehensive survey on the cooperated blockchain-5G networks and services are then summarized, and possible research challenges with open issues are also identified. Lastly, we complete this survey by shedding new light on future directions of research on this newly emerging area.

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Abstract: Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera (i.e CCD cameras, omnidirectional cameras, …), foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Abstract: Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

A survey on the adoption of blockchain in IoT: challenges and solutions

Abstract: Conventional Internet of Things (IoT) ecosystems involve data streaming from sensors, through Fog devices to a centralized Cloud server. Issues that arise include privacy concerns due to third party management of Cloud servers, single points of failure, a bottleneck in data flows and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance perspective. Blockchain technologies avoid trusted third parties and safeguard against a single point of failure and other issues. This has inspired researchers to investigate blockchain’s adoption into IoT ecosystem. In this paper, recent state-of-the-arts advances in blockchain for IoT, blockchain for Cloud IoT and blockchain for Fog IoT in the context of eHealth, smart cities, intelligent transport and other applications are analyzed. Obstacles, research gaps and potential solutions are also presented.

Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges

Abstract: The blockchain technology is taking the world by storm. Blockchain with its decentralized, transparent and secure nature has emerged as a disruptive technology for the next generation of numerous industrial applications. One of them is Cloud of Things enabled by the combination of cloud computing and Internet of Things. In this context, blockchain provides innovative solutions to address challenges in Cloud of Things in terms of decentralization, data privacy and network security, while Cloud of Things offer elasticity and scalability functionalities to improve the efficiency of blockchain operations. Therefore, a novel paradigm of blockchain and Cloud of Things integration, called BCoT, has been widely regarded as a promising enabler for a wide range of application scenarios. In this article, we present a state-of-the-art review on the BCoT integration to provide general readers with an overview of the BCoT in various aspects, including background knowledge, motivation, and integrated architecture. Particularly, we also provide an in-depth survey of BCoT applications in different use-case domains such as smart healthcare, smart city, smart transportation and smart industry. Then, we review the recent BCoT developments with the emerging blockchain and cloud platforms, services, and research projects. Finally, some important research challenges and future directions are highlighted to spur further research in this promising area.