19 research outputs found
A Trust Based Fuzzy Algorithm for Congestion Control in Wireless Multimedia Sensor Networks (TFCC)
Network congestion has become a critical issue for resource constrained
Wireless Sensor Networks (WSNs), especially for Wireless Multimedia Sensor
Networks (WMSNs)where large volume of multimedia data is transmitted through
the network. If the traffic load is greater than the available capacity of the
sensor network, congestion occurs and it causes buffer overflow, packet drop,
deterioration of network throughput and quality of service (QoS). Again, the
faulty nodes of the network also aggravate congestion by diffusing useless
packets or retransmitting the same packet several times. This results in the
wastage of energy and decrease in network lifetime. To address this challenge,
a new congestion control algorithm is proposed in which the faulty nodes are
identified and blocked from data communication by using the concept of trust.
The trust metric of all the nodes in the WMSN is derived by using a two-stage
Fuzzy inferencing scheme. The traffic flow from source to sink is optimized by
implementing the Link State Routing Protocol. The congestion of the sensor
nodes is controlled by regulating the rate of traffic flow on the basis of the
priority of the traffic. Finally we compare our protocol with other existing
congestion control protocols to show the merit of the work.Comment: 6 pages, 5 figures, conference pape
A Trust Based Congestion Aware Hybrid Ant Colony Optimization Algorithm for Energy Efficient Routing in Wireless Sensor Networks (TC-ACO)
Congestion is a problem of paramount importance in resource constrained
Wireless Sensor Networks, especially for large networks, where the traffic
loads exceed the available capacity of the resources. Sensor nodes are prone to
failure and the misbehavior of these faulty nodes creates further congestion.
The resulting effect is a degradation in network performance, additional
computation and increased energy consumption, which in turn decreases network
lifetime. Hence, the data packet routing algorithm should consider congestion
as one of the parameters, in addition to the role of the faulty nodes and not
merely energy efficient protocols. Unfortunately most of the researchers have
tried to make the routing schemes energy efficient without considering
congestion factor and the effect of the faulty nodes. In this paper we have
proposed a congestion aware, energy efficient, routing approach that utilizes
Ant Colony Optimization algorithm, in which faulty nodes are isolated by means
of the concept of trust. The merits of the proposed scheme are verified through
simulations where they are compared with other protocols.Comment: 6 pages, 5 figures and 2 tables (Conference Paper
An Energy Efficient Evo-Fuzzy Sleep Scheduling Protocol for Stationary Target Coverage in Wireless Sensor Networks
Target coverage is a fundamental problem that needs to be addressed in sensor networks for a variety of applications such as environment monitoring and surveillance purposes. A typical approach to prolong network lifetime would entail the partitioning of the sensors capable of sensing the targets, in a network for target monitoring into several disjoint subsets such that each subset can cover all the targets. Thus, each time only the sensors in one of such subsets are activated. In this paper, we have proposed a novel sleep scheduling protocol, abbreviated as EEFSSP, based on this concept which incorporates three novel features. Firstly, it paves way for an equitable distribution of nodes while forming cover sets through the proposed CSGH heuristic. Secondly, it schedules the cover sets using an evolutionary approach with the objective being to optimize the maximum breach interval. Thirdly, the EEFSSP introduces a novel routing protocol abbreviated as DFPRP to establish routes to transfer data packets to the Base Station, with the objective being to ensure energy-efficiency and minimize the number of packet drops. We finally conduct experiments by simulation to evaluate the performance of the proposed scheme under various conditions, and compare its performance with other relevant protocols. The experimental results show that the proposed scheme clearly outperforms its peers by delivering a much longer network lifetime and minimizing the number of packet drops
Clustering using Vector Membership: An Extension of the Fuzzy C-Means Algorithm
Clustering is an important facet of explorative data mining and finds
extensive use in several fields. In this paper, we propose an extension of the
classical Fuzzy C-Means clustering algorithm. The proposed algorithm,
abbreviated as VFC, adopts a multi-dimensional membership vector for each data
point instead of the traditional, scalar membership value defined in the
original algorithm. The membership vector for each point is obtained by
considering each feature of that point separately and obtaining individual
membership values for the same. We also propose an algorithm to efficiently
allocate the initial cluster centers close to the actual centers, so as to
facilitate rapid convergence. Further, we propose a scheme to achieve crisp
clustering using the VFC algorithm. The proposed, novel clustering scheme has
been tested on two standard data sets in order to analyze its performance. We
also examine the efficacy of the proposed scheme by analyzing its performance
on image segmentation examples and comparing it with the classical Fuzzy
C-means clustering algorithm.Comment: 6 pages, 8 figures and 1 table (Conference Paper
Trust Integrated Congestion Aware Energy Efficient Routing forWireless Multimedia Sensor Networks (TCEER)
Congestion control and energy consumption in Wireless Multimedia Sensor Network is a new research subject which has been ushered in through the introduction of multimedia sensor nodes that are capable of transmitting large volume of high bit rate heterogeneous multimedia data. Most of the existing congestion control algorithms for Wireless Sensor Networks do not discuss the impact of security attacks by the malicious nodes in network congestion. Sensor nodes are prone to failure and malicious nodes aggravate congestion by sending fake messages. Hence, isolation of malicious nodes from data routing path reduces congestion significantly. Considering that, we have proposed a new Trust Integrated Congestion Aware Energy Efficient Routing algorithm, in which malicious nodes are identified using the concept of trust. The parameter Node Potential is computed, on the basis of the trust value, congestion status, residual energy and the distance of the node from the base station, using Fuzzy Logic Controller. The source node selects the node with the highest potential in its one hop radio range for data transmission which is light weight as well as energy efficient. Finally, merits of the proposed scheme are discussed by comparing them with existing protocols and the study exhibits 25% improvements in network performance
Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples
Over the past few years, there has been significant interest in Quantum
Machine Learning (QML) among researchers, as it has the potential to transform
the field of machine learning. Several models that exploit the properties of
quantum mechanics have been developed for practical applications. In this
study, we investigated the application of our previously proposed quantum
pre-processing filter (QPF) to binary image classification. We evaluated the
QPF on four datasets: MNIST (handwritten digits), EMNIST (handwritten digits
and alphabets), CIFAR-10 (photographic images) and GTSRB (real-life traffic
sign images). Similar to our previous multi-class classification results, the
application of QPF improved the binary image classification accuracy using
neural network against MNIST, EMNIST, and CIFAR-10 from 98.9% to 99.2%, 97.8%
to 98.3%, and 71.2% to 76.1%, respectively, but degraded it against GTSRB from
93.5% to 92.0%. We then applied QPF in cases using a smaller number of training
and testing samples, i.e. 80 and 20 samples per class, respectively. In order
to derive statistically stable results, we conducted the experiment with 100
trials choosing randomly different training and testing samples and averaging
the results. The result showed that the application of QPF did not improve the
image classification accuracy against MNIST and EMNIST but improved it against
CIFAR-10 and GTSRB from 65.8% to 67.2% and 90.5% to 91.8%, respectively.
Further research will be conducted as part of future work to investigate the
potential of QPF to assess the scalability of the proposed approach to larger
and complex datasets.Comment: 13 pages, 8 figure
Efficient VQE Approach for Accurate Simulations on the Kagome Lattice
The Kagome lattice, a captivating lattice structure composed of
interconnected triangles with frustrated magnetic properties, has garnered
considerable interest in condensed matter physics, quantum magnetism, and
quantum computing.The Ansatz optimization provided in this study along with
extensive research on optimisation technique results us with high accuracy.
This study focuses on using multiple ansatz models to create an effective
Variational Quantum Eigensolver (VQE) on the Kagome lattice. By comparing
various optimisation methods and optimising the VQE ansatz models, the main
goal is to estimate ground state attributes with high accuracy. This study
advances quantum computing and advances our knowledge of quantum materials with
complex lattice structures by taking advantage of the distinctive geometric
configuration and features of the Kagome lattice. Aiming to improve the
effectiveness and accuracy of VQE implementations, the study examines how
Ansatz Modelling, quantum effects, and optimization techniques interact in VQE
algorithm. The findings and understandings from this study provide useful
direction for upcoming improvements in quantum algorithms,quantum machine
learning and the investigation of quantum materials on the Kagome Lattice.Comment: 7 pages,7 figure
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models
This paper proposes a novel quantum pre-processing filter (QPF) to improve
the image classification accuracy of neural network (NN) models. A simple four
qubit quantum circuit that uses Y rotation gates for encoding and two
controlled NOT gates for creating correlation among the qubits is applied as a
feature extraction filter prior to passing data into the fully connected NN
architecture. By applying the QPF approach, the results show that the image
classification accuracy based on the MNIST (handwritten 10 digits) and the
EMNIST (handwritten 47 class digits and letters) datasets can be improved, from
92.5% to 95.4% and from 68.9% to 75.9%, respectively. These improvements were
obtained without introducing extra model parameters or optimizations in the
machine learning process. However, tests performed on the developed QPF
approach against a relatively complex GTSRB dataset with 43 distinct class
real-life traffic sign images showed a degradation in the classification
accuracy. Considering this result, further research into the understanding and
the design of a more suitable quantum circuit approach for image classification
neural networks could be explored utilizing the baseline method proposed in
this paper.Comment: 13 pages, 10 figure
Quantum Circuit Optimization of Arithmetic circuits using ZX Calculus
Quantum computing is an emerging technology in which quantum mechanical
properties are suitably utilized to perform certain compute-intensive
operations faster than classical computers. Quantum algorithms are designed as
a combination of quantum circuits that each require a large number of quantum
gates, which is a challenge considering the limited number of qubit resources
available in quantum computing systems. Our work proposes a technique to
optimize quantum arithmetic algorithms by reducing the hardware resources and
the number of qubits based on ZX calculus. We have utilised ZX calculus rewrite
rules for the optimization of fault-tolerant quantum multiplier circuits where
we are able to achieve a significant reduction in the number of ancilla bits
and T-gates as compared to the originally required numbers to achieve
fault-tolerance. Our work is the first step in the series of arithmetic circuit
optimization using graphical rewrite tools and it paves the way for advancing
the optimization of various complex quantum circuits and establishing the
potential for new applications of the same