Self-enforcing Game Theory-based Resource Allocation for LoRaWAN Assisted Public Safety Communications

Public safety networks avail to disseminate information during emergency situations through its dedicated servers. Public safety networks accommodate public safety communication (PSC) applications to track the location of its utilizers and enable to sustain transmissions even in the crucial scenarios. Despite that, if the traditional setups responsible for PSCs are unavailable, it becomes prodigiously arduous to handle any of the safety applications, which may cause havoc in the society. Dependence on a secondary network may assist to solve such an issue. But, the secondary networks should be facilely deployable and must not cause exorbitant overheads in terms of cost and operation. For this, LoRaWAN can be considered as an ideal solution as it provides low power and long-range communication. However, an excessive utilization of the secondary network may result in high depletion of its own resources and can lead to a complete shutdown of services, which is a quandary at hand. As a solution, this paper proposes a novel network model via a combination of LoRaWAN and traditional public safety networks, and uses a self-enforcing agreement based game theory for allocating resources efficiently amongst the available servers. The proposed approach adopts memory and energy constraints as agreements, which are satisfied through Nash equilibrium. The numerical results show that the proposed approach is capable of efficiently allocating the resources with sufficiently high gains for resource conservation, network sustainability, resource restorations and probability to continue at the present conditions even in the complete absence of traditional Access Points (APs) compared with a baseline scenario with no failure of nodes.Comment: 16 Pages, 11 Figures, 2 Table

The Enhancement of Communication Technologies and Networks for Smart Grid Applications

The current electrical grid is perhaps the greatest engineering achievement of the 20th century. However, it is increasingly outdated and overburdened, leading to costly blackouts and burnouts. For this and various other reasons,transformation efforts are underway to make the current electrical grid smarter. A reliable, universal and secure communication infrastructure is mandatory for the implementation and deployment of the future smart grid. A special interest is given to the design of efficient and robust network architecture capable of managing operation and control of the next generation power grid. For this purpose new wired and wireless technologies are emerging in addition to the formerly applied to help upgrade the current power grid. In this paper we will give an overview of smart grid reference model, and a comprehensive survey of the available networks for the smart grid and a critical review of the progress of wired and wireless communication technologies for smart grid communication infrastructure. And we propose end to end communication architecture for Home Area Networks (HANs), Neighborhood Area Networks (NANs) and Wide Area Networks (WANs) for smart grid applications. We believe that this work will provide appreciated insights for the novices who would like to follow related research in the SG domain

Empirical Big Data Research: A Systematic Literature Mapping

Background: Big Data is a relatively new field of research and technology, and literature reports a wide variety of concepts labeled with Big Data. The maturity of a research field can be measured in the number of publications containing empirical results. In this paper we present the current status of empirical research in Big Data. Method: We employed a systematic mapping method with which we mapped the collected research according to the labels Variety, Volume and Velocity. In addition, we addressed the application areas of Big Data. Results: We found that 151 of the assessed 1778 contributions contain a form of empirical result and can be mapped to one or more of the 3 V's and 59 address an application area. Conclusions: The share of publications containing empirical results is well below the average compared to computer science research as a whole. In order to mature the research on Big Data, we recommend applying empirical methods to strengthen the confidence in the reported results. Based on our trend analysis we consider Volume and Variety to be the most promising uncharted area in Big Data.Comment: Submitted to Springer journal Data Science and Engineerin

A Review of Research on Devnagari Character Recognition

English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. Rapidly growing computational power may enable the implementation of Indic CR methodologies. Digital document processing is gaining popularity for application to office and library automation, bank and postal services, publishing houses and communication technology. Devnagari being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. This article is intended to serve as a guide and update for the readers, working in the Devnagari Optical Character Recognition (DOCR) area. An overview of DOCR systems is presented and the available DOCR techniques are reviewed. The current status of DOCR is discussed and directions for future research are suggested.Comment: 8 pages, 1 Figure, 8 Tables, Journal pape

Barrier Free Internet Access: Evaluating the Cyber Security Risk Posed by the Adoption of Bring Your Own Devices to e-Learning Network Infrastructure

The adoption of Bring Your Own Device (BYOD), also known as Bring Your Own Technology (BYOT), Bring Your Own Phone (BYOP), or Bring Your Own Personal Computer (BYOPC), is a policy which allows people access to privileged resources, information and services available on the private computer network of an organization using their own personal computer devices. BYOD, since its emergence in 2009, courtesy of Intel, is now a common practice in many organizations. Academic institutions that attempt to implement BYOD, can derive many benefits as well as many risks to its network infrastructure, largely security-based. This paper presents an assessment of a WLAN network which has been deployed for a campus-wide data centric e-learning platform at Kwame Nkrumah University of Science and Technology (KNUST) towards the overall objective of achieving a barrier free internet access to enhance the teaching and learning process at the university. The paper subsequently evaluates the WLAN infrastructure, its accompanying BYOD set-up, and associated likely security risks and threats, and recommends appropriate solutions

Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast

Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertaining non-payer before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a banker. This allow computer science researchers to drill down efficient research works through evaluating different classifiers and finding out the best classifier for such predictive problems. This research work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction and compares their fitness through various measures. German credit dataset has been taken and used to predict the credit risk with a help of open source machine learning tool.Comment: arXiv admin note: text overlap with arXiv:1310.5963 by other author

Analysis of Topology Based Routing Protocols for Vehicular Ad-Hoc Network (VANET)

Now-a-days vehicles are one of the most important parts of our life. We need them to cross distances in our everyday life. In this paper we discuss Vehicular AdHoc Network (VANET) technology that can ensure the maintenance of traffic rules and regulation. By applying this technology we can save life, save time, corruption, vehicle security, avoid collision and so on. Vehicular Ad Hoc Network (VANET) is a part of Mobile Ad Hoc Network (MANET). Every node or vehicle can move freely and they will communicate each other by wireless technology in coverage. The main goal of this research is to study the existing routing protocols for ad-hoc network system and compared between AODV (Reactive) and DSDV (Proactive). We have studied different types of routing protocols such as topology based, position based, cluster based, geo-cast based and broadcast based. We have simulated and compared AODV (Reactive) and DSDV (Proactive) to find out their efficiency and detect their flaws.Comment: 09 pages, available in International Journal of Computer Applications (IJCA) Volume 107 December 2014. arXiv admin note: text overlap with arXiv:1204.1201 by other author

Computational Intelligence in Sports: A Systematic Literature Review

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.Comment: 11 Pages. International Journal of Computer Applications February 201

A Robust Client Verification in cloud enabled m-Commerce using Gaining Protocol

The proposed system highlights a novel approach of exclusive verification process using gain protocol for ensuring security among both the parties (client-service provider) in m-commerce application with cloud enabled service. The proposed system is based on the potential to verify the clients with trusted hand held device depending on the set of frequent events and actions to be carried out. The framework of the proposed work is design after collecting a real time data sets from an android enabled hand set, which when subjected to gain protocol, will result in detection of malicious behavior of illegal clients in the network. The real time experiment is performed with applicable datasets gather, which show the best result for identifying threats from last 2 months data collected