Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Org...

Data Mining Techniques and Applications: A Ten-Year Update

Highlights •The frameworks evolved for support to medical experiments and algorithms for accurate prediction.•An unsupervised learning approach used for accurate prediction of the dataset of pima indian diabetes.•Early identification of diabetes is vital to lead to sound health and life.•The model achieved 98.16% accuracy and it was better than other state-of-art methods.Diabetes is a severe disease, most of the people are not aware of the risk associated with the disease because of that people die due to diabetic nephropathy, cardiac stroke and some other disorders. Therefore, early identification of diabetes helps to maintain sound health and life. Deep Learning approaches are used to predict diabetes accurately as humans do. In this paper, Deep Neural Network (DNN) classifier, an unsupervised learning approach is used for accurate prediction on Pima Indian Diabetes dataset and Feature Importance model that is bagged with Extra Trees and Random Forest is used for feature selection. The Pima Indian Diabetes dataset (PID) was acquired from the repository of UCI. The existing dataset has experimented with different formats of train test splits. The performance of the model was evaluated through accuracy, specificity, sensitivity, recall and precision. The model acheived 98.16% accuracy with random train-test split and it is observed that, the model obtained better performance than other state-of-art methods.

Type 2: Diabetes mellitus prediction using Deep Neural Networks classifier

Using Web APIs registered in service sharing communities for mobile APP development can not only reduce development period and cost, but also fully reuse state-of-the-art research outcomes in broad domain so as to ensure up-to-date APP development and applications. However, the big volume of available APIs in Web communities as well as their differences make it difficult for APIs selection considering compatibility, preferred partial APIs and expected APIs functions which are often of high variety. Accordingly, how to recommend a set of functional-satisfactory and compatibility-optimal APIs based on the APP developer's multiple function expectation and pre-chosen partial APIs is on demand as a significant challenge for successful APP development. To address this challenge, we first construct a Web APIs correlation graph that incorporates functional descriptions and compatibility information of Web APIs, and then propose a correlation graph-based approach for personalized and compatible Web APIs recommendation in mobile APP development. Finally, through extensive experiments on a real dataset crawled from Web APIs websites, we prove the feasibility of our proposed recommendation approach.

A Correlation Graph Based Approach for Personalized and Compatible Web APIs Recommendation in Mobile APP Development

Data redundancy is a significant issue that wastes plenty of storage space in the cloud-fog storage integrated environments. Most of the current techniques, which mainly center around the static scenes, for example, the backup and archive systems, are not appropriate because of the dynamic nature of data in the cloud or integrated cloud environments. This problem can be effectively reduced and successfully managed by data deduplication techniques, eliminating duplicate data in cloud storage systems. Implementation of data deduplication (DD) over encrypted data is always a significant challenge in an integrated cloud-fog storage and computing environment to optimize the storage efficiently in a highly secured manner. This paper develops a new method using Convergent and Modified Elliptic Curve Cryptography (MECC) algorithms over the cloud and fog environment to construct secure deduplication systems. The proposed method focuses on the two most important goals of such systems. On one side, the redundancy of data needs to be reduced to its minimum, and on the other hand, a robust encryption approach must be developed to ensure the security of the data. The proposed technique is well suited for operations such as uploading new files by a user to the fog or cloud storage. The file is first encrypted using the Convergent Encryption (CE) technique and then re-encrypted using the Modified Elliptic Curve Cryptography (MECC) algorithm. The proposed method can recognize data redundancy at the block level, reducing the redundancy of data more effectively. Testing results show that the proposed approach can outperform a few state-of-the-art methods of computational efficiency and security levels.

/pdf/a-secure-data-deduplication-system-for-integrated-cloud-edge-4a82fzbzlo.pdf

A secure data deduplication system for integrated cloud-edge networks

To curb the accident rate and traffic levels, strict implementation of the rules and continuous monitoring of the traffic is mandatory. Traffic Rule Violation Monitoring System ensures that the rules are followed strictly and it reduces the human effort. The main objective of this work is to identify the Triple Riding. To detect the triple riders, the deep learning framework darknet is used, which in turn uses a type of convolutional neural networks i.e. Deconvolutional neural network-based YOLO (You Only Look Once) algorithm for detection of the number of persons riding a bike, the system classifies the vehicle as to the rule-breach vehicle or not. The junctions acting as the data collections center, collects the data. The image of the vehicle classified as the rule-breach is stored along with the data such as vehicle manufacturing ID and vehicle speed transferred at the particular frame. The transfer of the data is facilitated using the GSM module and the NodeMCU deployed on the vehicle. The vehicle number will be verified with the transport office. To survive the lack of internet connectivity or low internet connectivity, the system is being equipped with the GSM module; else, the data related to the vehicle can be pulled by the development boards deployed at the junctions, acting them as the central part of the public internetwork deployed. This public internetwork acting the medium to pull the data from the vehicle to the central system. This is carried out using the concept of dynamic network configuration in NodeMCU. The use of Node MCU and the public network system makes the system much more viable, available and reliable. Thereby making the riders follow the rules properly and reducing irresponsible driving.

Detection of the triple riding and speed violation on two-wheelers using deep learning algorithms

----------------------------------------------------------------------ABSTRACT---------------------------------------------------------------A mobile ad-hoc network is a network (MANET) of wireless mobile nodes (MNs) that communicate with each other without centralized control or established infrastructure. Routing protocols are divided into Proactive and Reactive.Pro-active is a table–driven protocols. The proactive routing protocols use linkstate routing algorithm which frequently flood the link information about its neighbors. Reactive or on-demand routing protocols create routes when they are needed by the source host and these routes are maintained while they are needed. This paper proposes a solution for performance enhancement of VoIP and HTTP in Ad-hoc WLANs. This paper compares the performance of VoIP and HTTP over different IEEE standards and draws a conclusion based on performance of the network over different QoS parameters. Later the suitability of different routing protocols like AODV, DSR, OLSR and GRP for VoIP and HTTP traffic is compared and OLSR is found to have highest throughput with least delay as compared to other protocols. Thus we observed that, the throughput increase of around 80% over the existing routing standard and enormous decrease in end to end delay.

/pdf/performance-analysis-of-manet-wlan-using-different-routing-2d971qhqyh.pdf

Performance Analysis of MANET (WLAN) Using Different Routing Protocols in Multi service Environments-An Quantitative Study

Face recognition is an emerging research area in recognition of the people. A novel feature extraction technique was introduced for robust face recognition. Enhanced Local binary pattern (EnLBP) divided the image into sub regions. For each sub region, the salient features are extracted by obtaining the mean value of each sub region. In LBP, each pixel was replaced by applying LBP into each sub region. In this paper, the mean value of sub region was replaced for the sub region. It reduced the dimension of the image and extracts the salient information on each sub region. The extracted features are compared with similarity measures to recognize the person. EnLBP reduces the operation time and computational complexity of the system. The experimental results were carried out in the standard benchmark database LFW-a. The proposed system achieved a higher recognition rate than other local descriptors.

https://www.beei.org/index.php/EEI/article/download/761/526

Robust Face Recognition Using Enhanced Local Binary Pattern

Numerous destructive things influence the working arrangement of human body as hypertension, smoking, obesity, inappropriate medication taking which causes many contrasting diseases as diabetes, thyroid, strokes and coronary diseases. The impermanence and horribleness of the environment situation is also the reason for the coronary disease. The structure of Apache start relies on the evolution which requires gathering of the data. To break down the significance of use programming focused on data structure the Apache stop ought to be utilized and it gives various central focuses as it is fast in light as it uses memory worked in preparing. Apache Spark continues running on dispersed environment and chops down the data in bunches giving a high profitability rate. Utilizing mining procedure as a part of the determination of coronary disease has been exhaustively examined indicating worthy levels of precision. Decision trees, Neural Network, Gradient Boosting Algorithm are the various apache spark proficiencies which help in collecting the information.

https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042078/pdf

R K Nadesh

Papers

A secure data deduplication system for integrated cloud-edge networks

Detection of the triple riding and speed violation on two-wheelers using deep learning algorithms

Performance Analysis of MANET (WLAN) Using Different Routing Protocols in Multi service Environments-An Quantitative Study

Robust Face Recognition Using Enhanced Local Binary Pattern

Prediction of heart disease using apache spark analysing decision trees and gradient boosting algorithm