International Journal of Networking and Virtual Organisations (23 papers in press)
Pricing and Coordination with Contract for the Green Supply Chain of Chinese Home Appliances Industry with Retailers Promotional Efforts
by Ai Xu, Yijia Gao
Abstract: This study focuses on the decision making about the pricing issues for home appliances companies when recycle waste home appliances. We suppose three collection methods for the recycle of waste home appliances, i.e., manufacturer collection (Model M), retailer collection (Model R) and third-party collection (Model 3P). Considering the effect of retailers promotional efforts and the effective recycle behavior on the home appliances supply chain, game models are constructed respectively according to different collection methods. The optimal recycle prices for the waste home appliances and the optimal retailers promotional efforts can be determined by these models. To improve the game effectiveness, revenue and expense-sharing contract models are designed in order that both the revenues and expenses related to the manufacturing, retailing, promotion and recycle operations in the green supply chain can be allocated among all participant players, thus the profits and effectiveness of the whole supply chain can be better improved. Lastly, all the pricing and contract models are successfully tested by using the green supply chain of GREE freon-free and inverter air-conditioner as a case.
Keywords: Pricing strategies; coordination with contract; green supply chain; promotional efforts; recycling waste home appliances; game models.
DSEM-SW: a data-service enhanced scientific workflow middleware for cloud computing
by Kun Huang, Ning Han
Abstract: As more and more scientific workflows have been deployed and executed in cloud environments, how to optimize the execution efficiency and quality of workflow applications has become an important issue that needs to be addressed. Although many workflow management systems have been proposed in recent years, the performance bottleneck when running scientific workflows is still the low-efficiency of data-related services. In this paper, we present a workflow-oriented optimization plug-in system, namely Data-Service Enhanced Middleware for Scientific Workflow (DSEM-SW), which provides a set of mechanisms to improve the efficiency and quality of data processing when running scientific workflows. Extensive experiments are conducted to evaluate the performance of the proposed middleware in a real-world cloud test-bed. The experimental results indicate that our DSEM-SW outperforms many existing systems in terms of different performance metrics, and can significantly improve the execution efficiency of data-intensive workflows.
Keywords: workflow; cloud computing; resource virtualization; quality of data.
Reduce Memory Consumption for Internet Traffic Classification
by Alhamza Munther, Ali Abdulrazzaq, Mosleh Abu- Alhaj, Ghada Almukhaini
Abstract: Application level traffic classification is an essential requirement for stable network operation and resource management. However, classifications processing tends to face low resources when high volumes of traffic are being classified in high-speed networks in real time. Memory consumption considered to be serious issue during classification processing time. In this paper, a data reduction method proposed to decrease redundant data entry during preprocessing phase with regard to accuracy classification. In the proposed active build model random forest (ABRF) eliminates redundant data-entry by utilizing feature selection algorithm during the preprocessing phase. The proposed system successfully reduces the memory space of entire classification process. System evaluated by comparing the proposed system against four classifiers (RF, NB, SVM and C5.0) and five features selection techniques (FCBF, SFE, Chi2 and GR). DR reported excellent results amongst the NB, C5.0, and RF. The results were optimized due to the data excluding 314216 out of 774013. Moreover, C5.0 consumed less memory space due to the decreased depth of C5.0 tree model. In conclude, The DR was most effective on the RF model due to the nature of ensemble classifier.
Keywords: Internet traffic classification; Machine learning; feature selection techniques; supervised learning; Random forest.
Analysis Of Routing Protocols For Software Defined Vehicular Ad Hoc Networks
by Annapurna P. Patil, Lalitha Chinmayee MaheshKumar Hurali
Abstract: With the rapid development of smart cities, the significance of Intelligent Transport Systems (ITS) has revolutionized the functioning of the modern transportation system. Vehicular Ad hoc Networks (VANETs) are trending to be the backbones of the ITS. These VANETs, which are in the family of Mobile Ad Hoc Networks (MANETs) are characterized by high mobility and dynamically changing topology, which in turn leads to frequent link breaks. With many of the open challenges in VANETs to be addressed, it is strongly observed that routing is an essential aspect of VANETs to study and experiment with, as it majorly contributes to the overall network performance. Some of the challenges that arise with respect to routing in VANETs are diminished Quality of Service (QoS), heterogeneous networks, and security threats. These issues are efficiently addressed by a networking paradigm called Software-Defined Networking (SDN). The primary goal of this study is to bring out the current state of art in SDN based routing in VANETs with the following two objectives. The first objective of this study is to summarize the merits of SDN based routing in VANETs and provide the researchers with an intricate knowledge of the SDN based routing protocols for VANETs. The second objective is to propose a novel taxonomy of such routing protocols based on the organization of the SDN controller, QoS metric, security, heterogeneity, and type of communication. Our study has conclusive analysis, which shows that the SDN based routing in VANETs has significant benefits over the traditional network-based VANETs concerning QoS, Security, and heterogeneity in the networks. This analysis would help the researchers to venture on newer and more precise implementation in the area of SDN based routing in VANETs.
Keywords: software defined vehicular ad hoc networks; vehicular ad hoc networks; intelligent transportation systems; smart cities; software defined networking; routing; VANETs; SDN; security; heterogeneity.
The Effect of the Electronic Word of Mouth (EWOM) on Purchase Intention via the Brand Image as a Mediating Factor: An Empirical Study
by Hani Al-Dmour, Ahmad Aloqaily, Rawan Al-qaimary, Malak Al-hassan
Abstract: This study aims to investigate the impact of electronic word of mouth (eWOM) on the brand image and purchase intention in the telecommunication sector in Jordan. A quantitative-based approach was used for data collection, and a research model was suggested and tested empirically using a sample of 354 respondents who had experiences with the Internet and social media. Measurement items are adopted and modified from existing scales found in the relevant prior studies to fit the studys objectives. Using multiple regression analysis, the findings show that eWOM has a positive significant effect on both purchase intention and brand image; the brand image has a significant impact on purchase intention, and eWOM has a fully indirect impact on purchase intention through the brand image as a mediating factor. The current study suggested that telecommunication service providers should wisely and professionally focus on eWOM's favourable contacts to promote a brand image that will later promote the customers purchase intention.
Keywords: Regression Analysis; eWOM; Brand Image; Purchase Intention; mobile and Internet; Jordanian telecommunication sector.
The increased use of WhatsApp
by Pith Soh Beh, Suat Chin Ng, Amanda Nikolic, Matija Radojcic, Grace Chew, Nilmini Wickramasinghe, Phil Smart
Abstract: Background: The aim of this study was to analyse the volume and pattern of usage, media types and message contents of a smartphone group communication app within an acute surgical unit. Methods: WhatsApp
Keywords: Smartphone; WhatsApp®; Information science; Communication; Surgical Unit.
eGovernment Whole-of-Government Approach for Good Governance: The Case of the System GID in Morocco
by Azelmad Said
Abstract: Public administration reforms, in Morocco, called for the use of ICTs tornconnect administrations in a Whole-of-Government Approach, building a culturernof collaboration and Good Governance. Indeed, Coordination Theory supportsrnthe networked bureaucracy, as a catalyst for heterogeneous multi-stakeholderrnmanagement, internal auditing and synergy. Institutional networkedrncoordination is believed to increase interoperability and integration among offices for transparency and mutual accountability. A mutual collaboration (G2G) that is deemed to better governance, streamline workflows and reduce duplication of workflows and tasks. However, stove-piped management remains always a badrnomen for networked governance for its single-purpose management. It isrnbelieved to separate offices in corrupted dispersed silos and bureaucracies,rnoperating in ambiguity, red tape and bad governance. Results from an onlinernsurvey of public agents, using the GID system in Morocco (n =253) was used to empirically analyze the effect of eGovernment Whole-of-Government Approach on Good Governance ethics in back offices, using PLS-SEM.
Keywords: eGovernment; Good governance; Whole-of-Government Approach; Coordination Theory; GID system; Morocco.
A Double-controller Fuzzy Scheme for Intelligent Resource Discovery on IaaS Cloud Systems
by Mohammad Samadi Gharajeh, Babak Jahangir
Abstract: The resource discovery process discovers appropriate computing resources (e.g., shared memory) in Infrastructure as a Service (IaaS) cloud systems according to user requirements. Since fuzzy systems use intelligent procedures based on humanistic experiences, they can be used in cloud computing to increase the number of completed jobs and improves the success rate. This paper proposes a double-controller fuzzy scheme for intelligent resource discovery in IaaS cloud systems, called DOCFIR. This scheme applies two fuzzy controllers to perform the intelligent resource discovery across the network. The first controller determines the number of virtual machines in the deployment phase based on the most important characteristics of the physical machines. The second controller discovers the appropriate computing resources for the users job in the service phase based on characteristics of the physical machines and user requirements. The simulation results show that the proposed scheme surpasses some of the existing related works in terms of the number of completed jobs and success rate.
Keywords: Cloud System; Resource Discovery; Intelligent Strategy; Fuzzy Controller; Number of Completed Jobs; Success Rate.
IMPROVED GREY WOLF OPTIMIZATION ALGORITHM FOR HETEROGENEOUS CLOUD ENVIRONMENT TASK SCHEDULING
by Vignesh V., Santhosh R
Abstract: The attraction towards cloud computing by industry and individuals increases everyday as the benefits and advantages are much reliable and convenient to user to make process simple. Software and data giants like Google, Microsoft, Apple are efficiently utilizing the cloud features and the research towards improving its efficiency and utilization is going on worldwide. Cloud computing has large computational data intensive task and by reducing the complexity of task scheduling the efficiency could be improved. This research identifies the issues the existing task scheduling model and provides an optimized scheduling algorithm. Conventional models such as particle swarm optimization and PBEES algorithm are compared with proposed improved Greywolf optimization model experimentally to achieve 96% of utilization efficiency. This reduces he computation cost and provides high performance computing with reliability among the clients and service providers.
Keywords: Cloud computing; Learning-based Grey wolf; Reliability.
A Survey on Multipath Routing Techniques in Wireless Sensor Networks
by Shilpa Chaudhari
Abstract: Wireless Sensor Networks (WSN) usually consist of tiny sensor nodes to sense the environmental data that are transferred to the sink node via route discovered using unicast/multipath routing protocol. The multipath routing protocols improve load balancing and Quality of Service in addition to the reliable transfer of sensed data to the sink simultaneously by reducing delay and congestion. This survey gives a brief introduction about the existing multipath routing protocols in the literature and its classifications into four categories as follows. (1) Distributed meta-heuristic based route discovery uses intelligent algorithms for path discovery. (2) Local-heuristic knowledge based route discovery uses node level statistics to discover the route (3) Route discovery specific to multimedia applications (4) Route discovery for secure transmission of data. A comparison between these protocols in terms of various routing parameters for path discovery, traffic distribution, and path maintenance is described for each class of multipath routing protocols.
Keywords: Wireless sensor network; routing; multipath routing; path discovery.
Radio Frequency Identification and Internet of Things based Smart Library Management System
by Phani Gannamraju, Satyanarayana Yarramsetti, Lakshmi Sutha Kumar
Abstract: Libraries are essential parts of educational institutes that provide teaching resources and information. There is a loss of library resources due to the inefficiency of manual library systems which are not updating the information regularly. This paper aims to improve the existing library management systems at the university level using Radio Frequency Identification Technology (RFID) and the Internet of Things (IoT). RFID is a fast emerging technology as it enhances authenticity and reliability. The proposed smart library system uses RFID tag, RFID tag reader, ESP8266, and back-end database that stores the required content. It automates the issue and return of books with minimum human intervention. The designed library portal gives the availability of a variety of library resources, book transaction information, and fine details for both students and staff. The data analysis of book transaction details is done and data visualization is also made available to both user and admin.
Keywords: Smart library; Internet of Things; Library management; RFID; ESP8266; MYSQL; OTP; database server; data analysis.
A Web of Clues: Can Ecosystems Be Profiled Similarly to Criminals?
by Ninni Ylönen, Matti Rissanen, Antti Ylä-Kujala, Tiina Sinkkonen, Salla Marttonen-Arola, David Baglee, Timo Kärri
Abstract: The concept of an ecosystem has raised interest in many areas of research over the decades. There is a growing need to model and guide the operations, not just in single companies but also on ecosystem-level. To be able to do this, new and better tools are needed. This paper explores the ecosystems of organisations and presents the process for building an Ecosystem Profile. To understand ecosystems better, managers can utilise Ecosystem Profiles like criminal investigators utilise criminal profiles. Design Science Research is used to create the process, and the process steps utilise methods from web farming and Social Network Analysis. The created process is tested with an illustrative case. The process and the profiles created with it can be used by both researchers and managers. The Ecosystem Profile makes sense of the complex structure and gives data-based insights into the ecosystem under review.
Keywords: Ecosystem Profile; Business Ecosystem; Profiling; Design Science Research; Web Farming; Social Network Analysis; Visualisation; Ecosystem Map; Ecosystem Management.
The Implications of College Students Online Travel Information Seeking Behavior: A Case Study of Why They Use Ctrip OTA Website in China
by Ai Xu, Junsheng Kuang, Zongqing Zhou
Abstract: College students are becoming important consumers of online travel products and services. Consequently, it is important to try to understand their online travel information seeking behavior. This paper reports a study in which a survey was conducted among college students from several Chinese universities. Data were collected using an online survey tool and posted on social media websites. A total of 206 valid responses were obtained. The descriptive statistics and the results from stepwise regression analysis show that social group influence, website information quality, website back-end support capability, website security and monthly living expenses are positively correlated to the students use of the Ctrip website. Interestingly, this study shows no significant relationship between website usability and the use of the Ctrip website. This study's findings are important for tourism businesses seeking to improve their website design, marketing strategies and online services.
Keywords: Online travel and tourism; travel behavior; information seeking; tourism website; college students; Ctrip.
Special Issue on: ICICCT 2020 Sustainable Computing and Wireless Networks
PRIORITY-BASED ENERGY EFFICIENT MULTI QUEUE HEURISTIC SCHEDULING FOR INTENSIVE DATAIN CLOUD COMPUTING (PB-EES)
by Vignesh V., Santhosh R
Abstract: In general the problem-solving strategies used in heuristic approaches is different compared to conventional algorithms. They differ in handling the issues in different operating conditions thereby it enhances the overall performance of the system. In this research paper we have also implemented a heuristic scheduling approach in cloud computing environment to improve the energy efficiency. Since cloud needs a better scheduling module for intense data applications this proposed priority-based energy efficient multi queue heuristic scheduling model is suitable and provides efficient scheduling with minimum energy. Experimental results are compared with greedy and load balancing scheduler algorithms to prove that proposed model achieves 93.45% of energy efficiency.
Keywords: Cloud computing; Heuristic model; energy efficiency; scheduling.
Identifying DDOS Attacks in 4G Networks using Artificial Neural Networks and Principal Component Analysis
by Nagesha A G, Mahesh G, Gowrishankar S
Abstract: Abstract: Denial-of-Service (DoS) attack is one in which attackers make certain queries by sending messages to the remote or target servers with an intention to stop or shutdown the servers. Those messages causes such an impact to the servers that it makes no response for the users. When this DoS attack is performed using number of systems that are compromised for attacking a single system, then it is called as Distributed Denial-of-Service (DDoS) attack. In this paper an Artificial Neural Network (ANN) combined with Principal Component Analysis (PCA) is used to identify the traffic as normal or a DDoS attack in 4G networks. The feature space dimension is reduced using PCA and the dimensionally reduced features are given as input to the feed forward neural network for training. The experiment is conducted using KDD dataset. The recognition accuracy of the proposed system is improved when compared to the existing systems using RBF Networks, Naive Bayes and Random Forest.
Keywords: Artificial Neural Network; Distributed Denial-of-Service; Principal Component Analysis.
FORECASTING INTRADAY STOCK PRICE USING ANFIS AND BIO-INSPIRED ALGORITHMS
by Kumar Chandar S.
Abstract: Forecasting in financial markets is to estimate the future behaviour of stock price.The main focus of this study is to explore the predictability of stock price with variants of Adaptive Neuro Fuzzy Inference System (ANFIS) and suggests a hybrid model to enhance the prediction accuracy.Two variants of ANFIS model is designed which include Genetic Algorithm-ANFIS(GA-ANFIS) and Particle Swarm Optimization-ANFIS (PSO-ANFIS) to forecast stock price more accurately.The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators were calculated from the historical prices and used as inputs to the developed models.Using the designed models, experiments were conducted for the period from January 2018 to February 2018 utilizing intraday stock price.Prediction ability of the developed models are analyzed by varying number of samples such as one day, five days and ten days data as an input. Prediction errors are measured and compared to find the suitable model.Numerical results obtained from the simulation confirmed that the proposed PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier models taken for comparison.
Keywords: Adaptive neuro fuzzy inference system; bio inspired algorithm; genetic algorithm; intraday day; minute price and particle swarm optimization.
A SECURE ENERGY EFFICIENT EVENT DETERMINATION ALGORITHM FOR UNDERWATER WIRELESS SENSOR NETWORKS
by INDULEKSHMI S. KAIMAL, BINU G S
Abstract: Effective routing in Under Water Sensor Networks (UWSNs) is a highly demanding task because of the weak radio channels in water and the changing topology of sensors that maneuver indifferently with water. Energy efficient Derivative Based Prediction (DBP) approach has been proposed to reduce the amount of messages required for transferring the data samples from a wireless sensor node to a base station. However, predicting sensor data is not effortless for underwater channels as they are prone to wormholes attack due to the inconsistent propagation delays. In this paper, an enhanced empirical data predicting method that not only predicts sensor data but also the topographical movements of the sensor nodes is presented. Further, a security aware locally restrained algorithm RDV-HOP is proposed to assess the influence of paths to other broadcaster nodes and to transmit the collected information to the network. The Advanced Encryption Standard (AES) algorithm is used to protect the confidentiality of data. The proposed method demonstrates a significant benefit in managing the dynamic networks with better energy efficiency even with extensive number of attackers. Extensive simulations were performed with variable number of wormhole attackers and the results show higher packet delivery ratio with reduced delay.
Keywords: Underwater Wireless Sensor Network; Derivative based Prediction; Depth Based Routing; Encryption; and Prevention.
Special Issue on: The Impact and Importance of Networking and Virtualisation in a Post-COVID-19 Scenario
Analysis of low-cost electronic device for diagnosis of Covid-19
by Akshaya Nidhi Bhati, Himanshu Maharshi, Arun Kumar
Abstract: This paper presents the design of a low-cost electronic device that can be used to diagnose Corona Virus Disease 2019 (COVID-19) at home with the help of symptoms. The device will check whether the patient has a fever or not with the help of a thermal sensor, oxygen saturation in the blood (Spo2) with the help of a Pulse-Oximeter, and cough through artificial intelligence. The remaining symptoms will be diagnosed using a survey-based system, where respondents will be invited to self-report various symptoms. The estimate, conception and development of this device can greatly contribute to the creation, and assist in breaking off the spread of the disease, getting the timely treatments and potentially save lives.
Keywords: COVID-19; Oximeter; OLED Screen; Thermal Sensor; Spo2.
PlasmaBlock: A Plasma donation Blockchain system in COVID-19
by Riya Sapra, Parneeta Dhaliwal
Abstract: COVID-19 has brought the whole world to a still. The extensive spread of the disease has adversely affected the life of the common man and the nation as a whole. Various vaccine trials for the disease are being done by scientists and researchers in different countries. Plasma therapy is one of the treatments, medical professionals are using for treating critical patients. This treatment requires a valid plasma donor who can donate plasma after its recovery from COVID-19. In this paper, a decentralized platform for plasma donation registration and plasma matching has been proposed using blockchain technology to speed up the process of convalescent plasma therapy. It uses smart contract to find appropriate plasma matches for the critical patients to locate the plasma of required blood group. It will help in tracking the donations and the procedures afterward.
Keywords: Convalescent plasma; Plasma therapy; Blockchain; donor-recipient; plasma donors; smart contract; corona virus; COVID-19.
COVID 19 and its Impact on Global Virtual Teams: Exploring the Unexplored
by Archana Shrivastava, Pooja Misra
Abstract: Based on our understanding, most of the research related to the outbreak of pandemic focus on health sector and economy. Our research article examines how internationally disruptive events like COVID 19 pandemic influence global virtual teams, particularly those in which team members have never met in person. Research also explored participants perception of the higher education institutions towards online learning and attitude of corporate organisations towards remote working in the coming years. Results of the study are promising in throwing light on the insight created via narratives.
Keywords: COVID19; corona virus; virtual teams; higher education; online learning; remote working.
An IoT and Artificial Intelligence based Patient Care System focused on COVID-19 Pandemic
by Vishal Kumar Goar, Nagendra Singh Yadav, Chiranji Lal Chowdhary, Kumaresan P, Mohit Mittal
Abstract: World Health Organization has declared COVID-19 a pandemic. The spread rate of COVID-19 outbreak is much faster over past outbreaks of the Ebola and SARS-COV. Deaths and illnesses started to increase exponentially, and nations around the globe struggled to control the spread of the COVID-19 virus. Like many other epidemic outbreaks, COVID-19 faces significant challenges, like the identification of the epidemic's source of disease, control the spread rate, and adequate healthcare for all patients. Digital technology, particularly the IoT, could be used as an essential tool to combat and control the spread of these pandemics to minimize the economic loss and disruption. The digital technology allowed health care professionals in identification and isolation to the source of the infection to prevent community transmission of the virus by remotely monitor the COVID-19 infected patients. We proposed a machine learning prediction model using the Orange Canvas Program by creating a local instance dataset of eight suspected individuals measured body parameters. The body parameter values are extracted by the caretaker in a clinical system. Furthermore, six machine learning classifiers such as KNN, DT, SVM, Random Forest, Neural Network and Na
Keywords: Internet of Things; Artificial Intelligence; COVID-19; Pandemic; Wearablernsensors; Cloud interface; Machine learning.
AHP-based evaluation of Critical Barriers for Social Distancing in India during COVID-19
by Hemant Upadhyay, Abhinav Juneja, Sapna Juneja, Deepak Gupta
Abstract: COVID-19 has made the world realize, the uncertainty of our existence on the planet. There have been unprecedented consequences of the spread of this deadly pandemic. New methods and theories are being proposed by research community to save the humans from being affected by this disease. The aim of the presented research work is to analyse the critical barriers in social distancing in India during COVID-19 using AHP technique. Experts opinions are utilized to identify critical barriers in self-distancing in India during COVID-19. Twelve critical barriers have been compared and evaluated using Analytical Hierarchical Process Method to get ranked in terms of priorities. The results may be utilized by the policy manufacturers for nurturing adequate reforms and policies to effectively deal with current pandemic considering the relative importance of these critical barriers in social distancing management.
Keywords: COVID-19; Analytic Hierarchy Process; AHP; Critical Barriers; Social Distancing.
Special Issue on: Exploring Emerging Verticals in the Future of Wireless Technology
AN EFFICIENT 3D IMAGE EXTRACTION TECHNIQUE FOR BUILDING INFORMATION MODELING IMPORT
by Feng Cui
Abstract: Building Information Modellingalso popularly known as the BIM is emerging to be one of the most rapidly advancing modelling techniques used in a wide range of applications. BIM drastically reduces the time involved in manual work which is characteristic of the past and completes automates the process of managing and disbursing required data for modelling and construction of civic structures. There has been a widespread investment in BIM by clients prior to commencement of new projects, pre construction analysis, interior designing and post construction. Since BIM is a recent trend, the motivation for the proposed work has been derived an exhaustive literature survey in key areas for research contributions which could greatly increase the quality and importance of BIM. The proposed work focuses on the input side of BIM system involved with acquisition and processing of information before being exported into the database management system. 3D to 2D modelling and feature extraction has been proposed in this paper with dimensionality reduction technique which drastically reduces the feature data set before being exported in to the BIS management system. A Principal component analysis (PCA) has been implemented in this paper and the two dimensional features have been exported into the BIM database. The most appropriate texture image is selected from aerial images according to geometry between building faces and external parameters of thephotos. The method has been tested with LIDAR data of two building images and extraction accuracy determined.
Keywords: 3D image modelling and extraction; Polgonization; multi resolution approximation; feature import in BIS.