Selection of Machine Learning Techniques for Network Lifetime Parameters and Synchronization Issues in Wireless Networks

Srilakshmi, Nimmagadda,Sangaiah, Arun Kumar Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.4

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

Selection of Machine Learning Techniques for Network Lifetime Parameters and Synchronization Issues in Wireless Networks

Nimmagadda Srilakshmi,Arun Kumar Sangaiah 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.4

In real time applications, due to their effective cost and small size, wireless networks play an important role inreceiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisationof nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, faultdetection, and quality of service, among others. Conventional methods were programmed, for static networkswhich made it difficult for networks to respond dynamically. Here, machine learning strategies can be appliedfor dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with lesshuman intervention and reprogramming. In this paper, we present a wireless networks survey based ondifferent machine learning algorithms and network lifetime parameters, and include the advantages anddrawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion,synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluationof the survey, the motive for choosing specific techniques to deal with wireless network problems, and a briefdiscussion on the challenges inherent in this area of research.

Enhanced Security Framework for E-Health Systems using Blockchain

Kubendiran, Mohan,Singh, Satyapal,Sangaiah, Arun Kumar Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.2

An individual's health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.

Privacy-preserving image retrieval for mobile devices with deep features on the cloud

Rahim, Nasir,Ahmad, Jamil,Muhammad, Khan,Sangaiah, Arun Kumar,Baik, Sung Wook Elsevier 2018 Journal of Computer Communications Vol.127 No.-

<P><B>Abstract</B></P> <P>With the prevalent use of mobile cameras to capture images, the demands for efficient and effective methods for indexing and retrieval of personal image collections on mobile devices have also risen. In this paper, we propose to represent images with hash codes, which is a compressed representation of deep convolutional features using deep auto-encoder on the cloud. To ensure user's privacy, the image is first encrypted using a light-weight encryption algorithm on mobile device prior to offloading it to the cloud for features extraction. This approach eliminates the computationally expensive process of features extraction on resource constrained devices. A pre-trained convolutional neural network (CNN) is used to extract features which are then transformed to compact binary codes using a deep auto-encoder. The hash codes are then sent back to the mobile device where they are stored in a hash table along with image location. Approximate nearest neighbor (ANN) search approach is utilized to efficiently retrieve the desired images without exhaustive searching of the entire image collection. The proposed method is evaluated against three different publicly available image datasets namely Corel-10K, GHIM-10K, and Product image dataset. Experimental results demonstrate that features representation using CNN and auto-encoder shows much better results than several state-of-the-art hashing schemes for image retrieval on mobile devices.</P>

Small Sample Face Recognition Algorithm Based on Novel Siamese Network

Zhang, Jianming,Jin, Xiaokang,Liu, Yukai,Sangaiah, Arun Kumar,Wang, Jin Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.6

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

Enhanced Security Framework for E-Health Systems using Blockchain

Mohan Kubendiran,Satyapal Singh,Arun Kumar Sangaiah 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.2

An individual’s health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.

Small Sample Face Recognition Algorithm based on Novel Siamese Network

( Jianming Zhang ),( Xiaokang Jin ),( Yukai Liu ),( Arun Kumar Sangaiah ),( Jin Wang ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.6

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn’t need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFace1, which uses pairs of face images as inputs and maps them to target space so that the L2 norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

UAV-enabled Friendly Jamming Scheme to Secure Industrial Internet of Things

Qubeijian Wang,Hong-Ning Dai,Hao Wang,Guangquan Xu,Arun Kumar Sangaiah 한국통신학회 2019 Journal of communications and networks Vol.21 No.5

Eavesdropping is a critical threat to the security of industrialInternet of things (IIoT) since many malicious attacks oftenfollow eavesdropping activities. In this paper, we present ananti-eavesdropping scheme based on multiple unmanned aerial vehicles(UAVs) who emit jamming signals to disturb eavesdroppingactivities. We name such friendly UAV-enabled jamming schemeas Fri-UJ scheme. In particular, UAV-enabled jammers (UJs) emitartificial noise to mitigate the signal to interference plus noise ratio(SINR) at eavesdroppers consequently reducing the eavesdroppingprobability. In order to evaluate the performance of the proposedFri-UJ scheme, we establish a theoretical framework to analyzeboth the local eavesdropping probability and the overall eavesdroppingprobability. Our analytical results show that the Fri-UJscheme can significantly reduce the eavesdropping risk while havingnearly no impact on legitimate communications. Meanwhile,the simulation results also agree with the analytical results, verifyingthe accuracy of the proposed model. The merits of Fri-UJscheme include the deployment flexibility and no impact on legitimatecommunications.

Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services

Sajjad, Muhammad,Nasir, Mansoor,Ullah, Fath U Min,Muhammad, Khan,Sangaiah, Arun Kumar,Baik, Sung Wook Elsevier science 2019 Information sciences Vol.479 No.-

<P><B>Abstract</B></P> <P>Facial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. In this paper, we propose a novel, cost-effective, and energy-efficient framework designed for suspicious activity recognition based on facial expression analysis for smart security in law-enforcement services. The Raspberry Pi camera captures the video stream and detects faces using the Viola Jones algorithm. The face region is pre-processed using Gabor filter and median filter prior to feature extraction. Oriented FAST and Rotated BRIEF (ORB) features are then extracted and the support vector machine (SVM) classifier is trained, which predicts the known emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise). Based on the collective emotions of the faces, we predict the sentiment behind the scene. Using this approach, we predict if a certain situation is hostile and can prevent it prior to its occurrence. The system is tested on three publically available datasets: Cohen Kande (CK+), MMI, and JAFEE. A detailed comparative analysis based on SURF, SIFT, and ORB is also presented. Experimental results verify the efficiency and effectiveness of the proposed system in accurate recognition of suspicious activity compared to state-of-the-art methods and validate its superiority for enhancing security in law enforcement services.</P>