Papers by Arka Prokash Mazumdar
Opportunistic Routing Protocols for Infrastructure-Less Wireless Networks
Development of a service robot for indoor operation using distributed computing
Zeszyty Naukowe Instytutu Pojazdów / Politechnika Warszawska, 2017
Mitigating Content Poisoning in Content Centric Network: A Lightweight Approach
The internet paradigm was designed to forward packets from host-to-host. But nowadays the focal p... more The internet paradigm was designed to forward packets from host-to-host. But nowadays the focal point has moved to data. The Internet Centric Network (ICN) provides architectures to meet this requirement. The Content Centric Network (CCN) is the most widely used ICN architecture. Information Centric Network's ability to perform in-network caching lead to faster retrieval of data on subsequent request. Although latency is solved, caching in a router makes it vulnerable to attacks that focus on the cache. One such attack is content poisoning, that will fill the router with poisoned content making the end user difficult to retrieve original valid data. In this paper, we propose a solution to mitigate content poisoning attack that will consume minimum time and require minimal storage overhead during the verification process.
Chapman and Hall/CRC eBooks, May 9, 2023
Capsule endoscopy is ideally suited to artificial intelligence based interpretation given its rel... more Capsule endoscopy is ideally suited to artificial intelligence based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software, necessitates close human supervision given poor sensitivity relative to an expert reader. However, Accepted Article This article is protected by copyright. All rights reserved with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence based clinical applications are likely to proliferate rapidly. Artificial Intelligence and Deep Learning for Small Bowel Capsule Endoscopy Capsule endoscopy-challenges and potential benefits of artificial intelligence Wireless capsule endoscopy (WCE) examines the 2-8 meters of small bowel not seen during endoscopy. WCE in humans was first described in 2000. 1 Since then, it has expanded to the esophagus, stomach, and colon, though not first-line in these areas. 2-4 Indications for small bowel WCE include obscure gastrointestinal bleeding, small bowel Crohn's, and to a lesser extent, screening in polyposis syndromes, celiac disease or other small bowel pathology. 5 The detection rate for WCE depends on indication but was 56%-61% in a pooled analysis. 6 Capsule endoscopy is superior in many respects to alternative imaging but has a significant miss rate of 5.9% for vascular lesions, 0.5% for ulcers, and 18.9% for neoplasms. 7 Importantly, many missed lesions are due to inherent limitations in human reader ability. 50,000 images are obtained in a typical small bowel WCE study, and it is possible for the pathology of interest to be present in as few as one single image. Most studies report viewing times in the range
International Journal of Ad Hoc and Ubiquitous Computing, 2014
Although opportunistic routing (OR) for ad hoc networks have been shown to improve network throug... more Although opportunistic routing (OR) for ad hoc networks have been shown to improve network throughput, energy audit of these protocols have not been done. In this paper, an analytical model to characterise the energy consumption of OR protocols is presented. Total energy consumption is computed taking into account the energy consumed in exchanging control packets, data packet transmission including retransmission and reception. The model considers packet retransmissions that can occur due to network conditions and protocol inaccuracies. The proposed analytical model is used to compute the energy consumption of some well known OR protocols available in literature. The result of the mathematical model is compared with simulation results. The theoretical and experimental results are found to be in conformance.
Overlapping FSS-Based Energy-Efficient Routing in Wireless Sensor Networks
SBI-DHGR: Skeleton-based intelligent dynamic hand gestures recognition
Expert Systems With Applications, Dec 1, 2023
Selecting stable route in multipath routing protocols
Link failure due to node mobility is routine in mobile ad hoc networks (MANETs). A standard appro... more Link failure due to node mobility is routine in mobile ad hoc networks (MANETs). A standard approach to deal with such failures is to select stable routes instead of the shortest route. However, protocols that select stable route do not attempt to optimize the number of hops and hence do not guarantee improvement in performance. Another approach to handle mobility induced route failure is multipath reactive protocols. These protocols start forwarding the packet on the first available route and switches path when a route failure is detected. Although these multipath protocols save on route rediscovery overhead, we establish that they still incur a substantial overhead called route handover cost, while switching among the alternate paths. In this work, we propose a light weight metric link lifetime, which helps to choose a stable path in multipath protocols. We incorporate link lifetime in standard multipath protocol and show that it reduces routing overhead and consequently improves throughput.
CoAP Congestion Control: A Dynamic Send Rate based Approach
2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Sep 24, 2021
A common practice in the Internet of Things (IoT) is to integrate the Constrained Application Pro... more A common practice in the Internet of Things (IoT) is to integrate the Constrained Application Protocol (CoAP), a lightweight application layer protocol based on UDP (User Datagram Protocol), in its architecture. While UDP does not have any provision to control congestion at the transport layer, CoAP and its other variants, such as CoCoA and CoCoA+, get around this issue by employing their own congestion control mechanism, a send-rate-based congestion control technique, at the application layer. These techniques estimate the send-rate through Round Trip Time (RTT), however, fail to map acknowledgments and re-transmitted messages, leading to inaccurate RTT in case of bursty traffic and thus performs poorly. To tackle this problem, the article proposes novel a technique to regulate the send-rate considering the current and previous congestion ratio, congestion factor, and throughput. The proposed method aims to regulate the send-rate according to congestion level in the network. The performance measurements of the proposed approach show significant increments in goodput and fairness as compared to the techniques used in CoCoA+ and CoAP in burst traffic scenarios.
Hand Gesture Recognition with Gaussian Scaling and Kirsch Edge Rotation
Hand gesture recognition is a vital aspect of robotic vision models. This paper presents a fusion... more Hand gesture recognition is a vital aspect of robotic vision models. This paper presents a fusion based approach for hand gesture recognition. In this approach, we first extract the Gaussian scale space of an image and compute features on different scales. Kirsch’s convolution mask is then applied on the feature map. The aim of the proposed approach is to remove unwanted information extract scale, rotation, and illumination invariant patterns from hand gestures. The final feature vector is aggregated through the concatenation of multiscale histograms. The Support Vector Machine classifier is demonstrated using extracted features. Moreover, we calculate the progress efficiency of proposed methods on three distinct databases by conducting experiments viz, Thomson, Bochum, and HGRI. The proposed method achieves classification accuracies of 94.25%, 92.77%, and 95.78% respectively on the investigated databases that outperform the existing approaches for hand gesture recognition
Energy Efficient Multi-Objective Task Allocation in Software-Defined Wireless Sensor Network
Congestion control in Internet of Things: Classification, challenges, and future directions
Sustainable Computing: Informatics and Systems, Sep 1, 2022
On Performance Modeling of Ad Hoc Opportunistic Routing Protocols
Springer eBooks, 2013
ABSTRACT Since the inception of opportunistic routing in ad hoc networks, a number of OR protocol... more ABSTRACT Since the inception of opportunistic routing in ad hoc networks, a number of OR protocols have been proposed. The performance measure of these protocols is predominantly based on simulation. Although simulation provides a simple and economical mechanism to test complex routing algorithms, they have been reported to produce inconsistent results. Simulation-based evaluation of ad hoc routing protocols should, therefore, be complemented with mathematical modeling and verification. OR protocols try to restrict the number of transmissions, therefore, the most important performance evaluation metric of OR protocols is packet transmission. In this chapter we examine the various mathematical evaluation models available in literature. During our study we found that these models do not consider retransmitted packets. We, therefore, propose an analytical model which account for packet retransmissions. The mathematical models are validated using prominent OR protocols. The performance of OR protocols depend not only on design issues like candidate selection and forwarder prioritization but also on network parameters like node topology, node density, etc. While the user has no control on the network parameters the performance of the protocol can be improved by carefully tuning the design issues. As a proof of concept we propose an OR protocol that minimizes packet retransmissions.
PBFS: A technique to select forwarders in Opportunistic Routing
Abstract Opportunistic Routing (OR) is a class of routing protocol that exploits the broadcast na... more Abstract Opportunistic Routing (OR) is a class of routing protocol that exploits the broadcast nature of wireless network to improve routing efficiency. The primary step in OR is to select a group of nodes as forwarders and prioritize them. The current approach of selecting the forwarders is through simulation which does not always give the best results, select forwarders on diverse paths and can be compute intensive for large networks. In this paper we formulate the problem using OR algebra. We propose a forwarder selection scheme ...
Eurasip Journal on Wireless Communications and Networking, Sep 25, 2013
Opportunistic routing (OR) protocols for ad hoc networks basically consist of selecting a few for... more Opportunistic routing (OR) protocols for ad hoc networks basically consist of selecting a few forwarders between the source and destination and prioritizing their transmission. The performance of OR protocols depends on how these two steps are performed. The aim was to reduce the number of transmissions to deliver packets to the destination. In this paper, we first present a mathematical model to compute the total number of packets including duplicate packets generated by OR protocols. We use the model to analyse well-known OR protocols and understand the reason behind their increase in number of transmissions. Next, we propose an OR scheme transmission-aware opportunistic ad hoc routing (TOAR) protocol, which attempts to minimize retransmissions. Our proposed OR protocol uses tree structures to select forwarders and prioritize them. The use of tree structures helps in identifying primary forwarders which carry packets farthest to the destination during each transmission round. TOAR also helps in choosing secondary forwarders which will transmit packets missed out by the forwarder. The optimized selection of forwarders results in significant reduction in retransmissions, a smaller forwarder list set, and improvement in goodput.
BLAST-IoT: BLockchain Assisted Scalable Trust in Internet of Things
Computers & Electrical Engineering, Jul 1, 2023
Defence Science Journal, Dec 6, 2022
The unprecedented ballooning of network traffic flow, specifically the Internet of Things (IoT) n... more The unprecedented ballooning of network traffic flow, specifically the Internet of Things (IoT) network traffic, has big stress of congestion on today's Internet. Non-recurring network traffic flow may be caused by temporary disruptions, such as packet drop, poor quality of services, delay, etc. Hence, network traffic flow estimation is important in IoT networks to predict congestion. As the data in IoT networks is collected from a large number of diversified devices with unlike formats of data and manifest complex correlations, the generated data is heterogeneous and nonlinear. Conventional machine learning approaches are unable to deal with nonlinear datasets and suffer from the misclassification of real network traffic due to overfitting. Therefore, it also becomes hard for conventional machine learning tools like shallow neural networks to predict congestion accurately. The accuracy of congestion prediction algorithms plays an important role in controlling congestion by regulating the send rate of the source. Various deep learning methods, such as LSTM, CNN, and GRU, are considered in designing network traffic flow predictors, which have shown promising results. This work proposes a novel congestion predictor for IoT that uses TCN. Furthermore, we use the Taguchi method to optimize the TCN model which reduces the number of experiment runs. We compare TCN with the other four deep learning-based models concerning Mean Absolute Error (MAE) and Mean Relative Error (MRE). The experimental results show that the TCN-based deep learning framework achieves improved performance with 95.52% accuracy in predicting network congestion. Further, we design the Home IoT network testbed to capture the real network traffic flows as no standard dataset is available.
A multilane traffic and collision generator for IoV
Simulation Modelling Practice and Theory, Nov 1, 2022
Distance-aware Hierarchical Data-collecting Path Selection for Mobile Sink in Sparse WSNs
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Papers by Arka Prokash Mazumdar