International Journal of Hybrid Intelligence (12 papers in press)
Special Issue on: Hybrid Computational Intelligence in Big Data Analytics and Cloud Computing
Smart Healthcare Model with Fog-Cloud Network Architecture
by Rohan Basu Roy, Arani Roy, Amitava Mukherjee, Mrinal Kanti Naskar
Abstract: In this paper, the performance of an Internet of Things (IoT) based, real time, smart ECG signal compression and transmission protocol is investigated through both simulation and hardware implementation. The protocol consists of a combination of Fog and Cloud computing architecture. The input to the model is noisy ECG signal acquired through sensors. The model is based upon a four layered structurernwith first layer consisting of wearable ECG sensor and noise filter embedded in the device. Second layer is an encoder which consists of an algorithm subdivided into two schemes namely, Geometry Based Method (GBM) and Wavelet Based Iterative Thresholding (WTIT). The algorithm is based on the fact that ECG signals can be approximated by the linear combination of a few coefficients obtained from a wavelet basis. GBM reduces the minimal signal values to zero geometrically in time domain and WTIT encodes the signal in time-frequency domain. Further, Compressed Row Hoffman Coding algorithm (CRHFC) is applied to convert the sparse coefficient matrices to compressed, transmittable matrices. The third layer consists of wireless transmission medium from the wearable to a private Cloud, from where data is accessed by the hospital servers. The data obtained in the receiver is the final layer where signal reconstruction is performed using inverse transforms. The performance metrics-compression ratio (CR), percentage RMS differencern(PRD), Quality Score (QS), time complexity and sparsity are used to evaluate the performance of the model.
Keywords: IoT; hardware implementation; Fog; Cloud; compression.
Improved real coded genetic algorithm based short-term hydrothermal generation planning
by DIPANWITA GANGULY, Saborni Das, Abhik Hazra, Ashish Laddha, Mousumi Basu
Abstract: Real coded genetic algorithm (RCGA) and improved real coded genetic algorithm (IRCGA) have been applied for the solution of short-term hydrothermal scheduling problem. The improved technique has been developed and tested on a multi reservoir cascaded hydroelectric system having generation-load power balance, upper and lower limits on reservoir capacity, water discharge rate, water spillage rate, hydraulic continuity restriction and operating capacity limits of different hydro and thermal units. The water transport delay between connected reservoirs has also been taken into consideration. The performance of the proposed approach is validated with four test systems. The results of the proposed algorithm are compared with those of modified differential evolution (MDE), teaching learning based optimization (TLBO), clonal selection algorithm (CSA), improved fast evolutionary programming (IFEP), improved particle swarm optimization (IPSO), and genetic algorithm (GA). From numerical results, it has been found that the IRCGA based approach is able to provide better solutions in lesser computational time.
Keywords: Hydrothermal operation planning; improved real coded genetic algorithm; limiting values of ramping rate; loading effect of valve point; restricted operating sections.
A Survey on Big Data: An Emerging Imparity and Revolution in Digital World
by Anupam Mukherjee, Sourav De, Siddhartha Bhattacharyya
Abstract: Size of the heterogeneous data is increasing rapidly at an electrifying speed. But we cannot handle this massive amount of unstructured data in traditional database, most of the data in the digital universe is unstructured. Big data analytics provides better computational power and efficient mechanism to handle this situation. This paper attempts to offer a survey report of big data, which changes rapidly of high volume, velocity, verity. This survey paper considers some of the major challenges of big data and its characteristics followed by a conceptual framework of big data. This survey paper also focuses on the complexity of medical images, data mining and soft computing problems.
Keywords: Big data analysis; Social media data; unstructured data; heterogeneous dimensionalities; architectural framework; medical image data; data mining; soft computing.
A comparative study of text mining in big data analytics using Deep Learning and other machine learning algorithms
by Souvik Chowdhury, Shibakali Gupta
Abstract: Text mining has become an important aspect in modern day. Text mining has various applications e.g. spam email classification, similar news item clustering etc. There have been many scenarios where regression is accompanied with text mining e.g. for predicting sales of a product of any store the product description also plays an important role. Big data analytics applications modify information scientists, prognostic modelers, statisticians and different analytics professionals to investigate growing volumes of structured group action information, and different sorts of information that square measure typically left untapped by standard business intelligence (BI) and analytics programs. That encompasses a mixture of semi-structured associate degreed unstructured info -- as an example, web clickstream info, net server logs, social media content, text from shopper emails and survey responses, mobile-phone call-detail records and machine info captured by sensors connected to the net of things. In this paper we have tried to solve text mining problems in big data analytics using Deep Learning methods. Deep learning on the other hand known to be strongest supervised learning method. Here we can make use of back propagation concept to harness the power. We can also use gradient descent learning method to reduce the cost function and settle to global minima. We will also do a comparative analysis of other machine learning algorithms with deep learning methods. We will also construct a rXs contingency table popularly known as Crosstab.
Keywords: Deep Learning; Text mining; Machine Learning.
Cloud Database Failure Prediction using Multi Agent System
by Souvik Chowdhury, Shibakali Gupta
Abstract: We have proposed a new method of fault tolerance mechanism in modern cloud database. Cloud database is important for big enterprises and have strict SLA (Service Level Agreement) so any downtime is costly. All databases have their own fault tolerance mechanism by means of clustering technique, standby, backup recovery strategies with almost no data loss. The above methods are either costly or time consuming. If any measurement can be taken which can predict cloud database failure well ahead of it actually happens, suggest reason for the failure and also provide solution for the problem from Knowledge database will not only reduce cost and time problem mentioned above but also will add a new dimension to fault tolerance technology. In this paper we have tried to develop an idea of failure prediction for an Cloud database with the help of Multi Agent System and probable solution based on Knowledge base search.
Keywords: Cloud database; Oracle; Multi Agent System (MAS); RDBMS.
IoT based algorithms for distributed location detection for flights
by Amlan Chatterjee, Hugo Flores, Soumya Sen, Khondker Hasan, Ashish Mani
Abstract: Detecting the location of aircraft at all times during flight is of utmost importance in commercial aviation. In recent times, there have been instances of aircraft that became untraceable during flight, and has not been located. This potentially has a huge negative impact on the entire aviation industry, and recovery of specific airlines from such an issue is extremely difficult. With the increasing number of aircraft that operate over various routes across the globe, tracking each flight with accurate location detection is a challenge. Specifically, over oceanic routes, where aircraft are not within the range of radar or other traditional tracking devices, the issue of location tracking is difficult with existing infrastructure. In this paper, we propose an internet of things based framework for aircraft that can assist with tracking the same. Our introduced model includes aircraft, air traffic control towers, project loon balloons and infrastructure enabled aircraft as the different components. Our proposed algorithms work for aircraft that operate on routes over land as well as large water bodies, specifically oceans. The algorithms are distributed in nature, thereby creating a model with no single point of failure. All the introduced algorithms are implemented and tested using simulation for the tracking of aircraft in different scenarios. The experimental results show that using the proposed techniques, an additional 70% of aircraft under consideration can share location data and can be detected as compared to using conventional radar based techniques.
Keywords: Internet of Things; IoT; Distributed Location Detection; Commercial Aviation; Communication; Flight Tracking.
Combined Economic Emission and Load Dispatch Using Hybrid Metaheuristics
by Dipankar Santra, Krishna Sarker, Anirban Mukherjee, Subrata Mondal
Abstract: This paper attempts to report optimal result for economic emission dispatch (EED) problem together with economic load dispatch (ELD) problem with valve point effect. The result is obtained using a hybrid meta-heuristic technique not used earlier for the said purpose. The technique involves hybridized particle swarm optimization (PSO) and ant colony optimization (ACO) methods. The purpose is to minimize fuel cost and operating cost of generators and at the same time minimize harmful emission of NOX. In this study ELD problem has been considered for a power system with 13 generating units whereas ELD and EED both combined i.e. CEED problem is solved for 40-generator test system. The proposed PSO-ACO hybrid shows excellent convergence property and encouraging results in comparison with other hybrid methods.
Keywords: combined economic emission dispatch (CEED); economic load dispatch (ELD); fossil fuel; environment; hybrid metaheuristics; PSO; ACO.
Special Issue on: Intelligent Techniques for Ad-hoc and Wireless Sensor Networks
A Differential Evolution-Based Routing Algorithm for Multi-Path Environment in Mobile Ad-hoc Network
by Anju Sharma, Madhavi Sinha
Abstract: Optimization is a procedure through which the best possible ways of decision variables are obtained under the given set of constraints and in accordance to a selected optimization objective function. Over the last decade, Differential Evolution (DE) algorithms have been extensively used in various problem domains and succeeded effectively in finding the optimal solutions. The DE algorithm is a bio-inspired, heuristic approach with three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using limited control parameters. The present paper emphasis on a DE algorithm based Ad-hoc On-demand Multi-path Distance Vector (DE_AOMDV) protocol for MANET. The proposed DE_AOMDV routing protocol has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless ad-hoc networks. The main objective of this research paper is to find the optimal path from available multiple paths between source and destination to be used in route recovery process. DE_AOMDV provides backup paths to avoid reroute discovery in the case of link failure between nodes. The descriptive analysis of the comparative study of DE_AOMDV protocol in terms of End to End Delay, Packet Delivery Ratio and Throughput with AOMDV protocol is the primarily focus of this paper. Simulation based results and data analysis shows DE_AOMDV protocol is better than AOMDV.
Keywords: Differential Evolution (DE); Mobile ad-hoc networks; Routing Strategy; AOMDV; Strategy adaptation.
Wireless Sensor Networks for Extreme Environments: Remote Sensing and Space Industry
by Mohammed El-Telbany
Abstract: The adoption of wireless sensor networks (WSNs) for environmental
monitoring is currently considered one of the most challenging applications
for this emerging technology. These set of sensors that collaboratively perform
embedded sensing and communication tasks owned features such as low cost,
flexibility, fault tolerance, high sensing fidelity, creating many new and exciting
applications for remote sensing and space industry where many essential
phenomena have hardly been investigated. In this paper, a state-of-art survey
clearly shows theWSNshave been successfully implemented and tested in remote
sensing applications. Morevever, the developments and challenges in design
WSNs for space-based application are discussed in order boost the use of WSNs
in remote sensing and space industry in Egypt.
Keywords: WSNs; Remote Sensing; Satellite; Space Industry.
Performance Improvement of Vehicular Ad-Hoc Network with Nature Inspired Biological Computing Algorithm
by Komal Mehta(Bhagat), Preeti Bajaj, L.G. Malik
Abstract: Vehicular Ad-Hoc Network (VANET) is a hybrid Ad-Hoc network between vehicles and Road Side Units (RSUs). It uses various routing techniques to establish thecommunication link between vehicles and RSUs. Although there are many routing techniques available, still finding stable routedue to the high mobility of nodes is an open challenge.However intelligent techniques are used to solve this problem up to some extent, this paper introduces a new routing algorithm to find a stable route. The proposed algorithm is a hybrid algorithm which uses ZoneBased Routing, fuzzy logic, andNature Inspired Biological Computing (NIBC) algorithm. In proposed algorithm ZoneBased Routing is used to divide the network into small and stable zones,fuzzy logic is used to find the quality of links between nodes, and NIBC to find the stable route in short time.Six techniques for the VANET has been implemented and compared in this paper. Among them, five are NIBC techniques including proposed one, and sixth is state of art algorithm AODV. NS2.34 network simulator is used for simulations. Simulations were carried out for variable transmission rate and variable speed. Five performance parameters; packet delivery ratio, packet drop ratio, control overhead, average delay, and throughput have been taken to analyze the results. Simulation results have shown that NIBC algorithms improve the performance of VANET and the proposed algorithm has the best performance among them.
Keywords: Vehicular Ad-Hoc Network; Nature Inspired Biological Computing; Artificial Bee Colony (ABC); Bacterial Foraging Optimization (BFO); Particle Swarm Optimization (PSO),AODV; Performance Parameters; Performance Improvement; Fuzzy Logic.
Energy Efficient Service Differentiated QoS aware Routing in Cluster Based Wireless Sensor Network
by Yogita Patil
Abstract: In today's technological era, WSNs has gained attention worldwide with its miniature size and low cost. Data transmission demands both energy and quality of service (QoS) to ensure efficient use of the sensors and adequate access to collecting information. Lot of research work in literature has been focused on QoS provisioning by differentiated service technique that differentiates and priorities different traffic classes to meet the user requirements. Our work toward scheduling is based on packet type providing high priority to emergency data to facilitate reliable transmission of emergency data in critical situations. We proposed an approach for effective sensing by use of stochastic scheduling to increase the energy efficiency of sensor nodes for intracluster communication. The proposed technique in this work outperforms when compared with the existing protocol in the literature in terms of minimized energy consumption, delay, and high throughput by offloading of the energy-intensive tasks.
Keywords: Clustering;Energy efficiency; Proxy Server;IEEE 802.15.4; IEEE 802.15.6; Time Division Multiple Access; Differentiated service.
A New Energy-Preserving Cloud Offloading Algorithm for Smart Mobile Devices
by Samar A. Said, Sameh A. Salem, Samir G. Sayed
Abstract: The advent of mobile devices becomes the way for various technological developments in mobile communication and information technology. However, mobile users expect to access computational intensive applications through resource constrained mobile devices. Consequently the growing demands for boosting the computations, storage and memory resources became essential for mobile devices. A new trend to incorporate mobile devices and cloud resources with the existence of the network connectivity is named as mobile cloud computing (MCC). MCC is the greatest solution to increase the application processing capabilities on mobile devices by migrating the application to the cloud servers with on demand and unlimited resources. This paper proposes a new energy-preserving cloud offloading algorithm. The proposed algorithm estimates the application computational time and uses multiple weighted parameters to give accurate offloading decisions. Simulation results on various applications clarify that the proposed algorithm is capable of estimating an applications computation time with high correlations compared with the real execution time. This improves the offloading decision which actually preserves the energy and reduces the execution time of mobile applications.
Keywords: Mobile cloud; Offloading decision; Cloud computing; Energy Preserving.