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Cross Project Model For Churn Prediction In Telecom Sector Customer churn is an important and critical issue in telecommunication sector. With acquiring new customers, the high cost is associated, so due to this customer churn prediction is one of the most important activities for a project manager and has indispensable part of industry’s strategic decision making and planning process. Unlike traditional customer churn prediction models that identify customer churn, cross projects just in time prediction is relative new and more practical alternate to traditional churn prediction techniques, providing immediate feedback while design decisions are still fresh in the minds of the project managers. The proposed model requires a large size of training data, usually such amount of data not available when the companies are at initial stage. To address this challenge in traditional churn prediction, prior studies have proposed cross-project models (CPM). Cross Project Model learned from previous projects of same nature with sufficient history. However, only few studies have focused on transferring prediction models from one project to another. This research do an early attempt which makes the use of just-in-time approach needed for customer churn prediction with cross-project model. Along with this there is always the problem of accuracy in CPM which are addressed by embedding ensemble technique. Ensemble application has shown tremendous increase in the accuracy of prediction for customer churn. With ensemble technique, genetic algorithm outperforms other classifiers by achieving an optimized accuracy of 68% which is 11% more than the previous technique that is without ensemble technique for cross project model.
Enhancing Buffer Management In Delay Tolerant Networks Via Novel Message Drop Policy Delay Tolerant Network is referred to such network in which end to end connectivity is rarely exists. Delay Tolerant networking is an approach that pursues to report the problems that reduces communication in disrupted networks. DTN works on Store-Carry and Forward mechanism in such a way that a message may be stored by a node for a comparatively large amount of time and carry it until a proper forwarding opportunity appears. To store a message for long delays a proper buffer management scheme is required to select a message for dropping upon buffer overflow. Every time dropping messages leads toward the wastage of valuable resources which the message already consumed. The proposed solution is a size based policy which determines an inception size for the selection of message for deletion as buffer becomes overflow. The basic theme behind this scheme is that by determining the exact buffer space requirement one can easily select a message of an appropriate size to be discarded. By doing so, it can overcome unnecessary message drop and ignore biasness just before selection of specific sized message. The proposed scheme Spontaneous Size Drop (SS-Drop) implies a simple but intelligent mechanism to determine the inception size to drop a message upon overflow of buffer. After simulation in ONE (Opportunistic Network Environment) simulator the SS-Drop outperforms the opponent drop polices in terms of high deliver ratio by giving 66.3% delivery probability value and minimize the overhead ratio up to 41.25 %. SS-Drop also showed a prominent reduction in dropping of messages and buffer time average.
FOG-ASSISTED CONGESTION AVOIDANCE SCHEME FOR INTERNET OF VEHICLES Internet of Vehicles (IoVs) is an emerging research area. It has wide ranging applications such as traffic management, vehicle security and communication among vehicle etc. Most of these applications require vehicles to continuously update their information to a centralized repository or server in order to gain various services. IoV message dissemination schemes are identified with congestion issues due to large number of messages populated by vehicles in the area. However, frequent transmission of messages by a large number of vehicles may not only overwhelm a centralized server but also causes a congestion which may be dangerous in emergency situations. The aim of this research is to minimize congestion for smooth communication. This work presents a fog-assisted congestion avoidance scheme for IoV named Energy Efficient Message Dissemination (E2MD). To capitalize on the merits of fog computing and minimize delay, E2MD uses a distributed approach by employing a fog server to balance services in IoVs. In E2MD, vehicles continuously update their status to a fog server either directly or through intermediate nodes. In case of an emergency, the fog server will inform upcoming traffic to slow down the speed, dispatch rescue teams to provide necessary services, and coordinate patrolling missions to clear the road. Proposed scheme considers a reality based model having intercity highways as well as roads in urban areas. Each road consists of three lanes where left most is slowest and in the right lane vehicles are moving at high speed. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance supremacy of E2MD compared to contemporary schemes in terms of delay, message overhead and packet delivery ratio. E2MD consume 5 microseconds while contemporary schemes cause delays in milliseconds. E2MD improves message delivery cost by 108% and decrease message overhead cost by 73% and 98% respectively than other schemes. In future need to work for the scenario if AV is blasted and unable to inform nearby vehicles.
SECURE AND DE-DUPLICATION BASED DATA AGGREGATION IN WIRELESS BODY AREA NETWORKS Wireless Body Area Networks (WBAN) are helpful for monitoring, diagnostic, and therapeutic levels. These networks gather real time medical information by using various sensors with secure communication links. It facilitates doctors to observe a patient’s health conditions by monitoring patient’s vital signs away from the hospital. Sensors sense the data and forward it to the head node. The collector node consumes power to process this redundant information. It wastes too much power by sending same kind of data to next level repeatedly. During data aggregation, the collector node receives input data packets, process them and transmits it as a single packet that causes communication, energy and storage overhead. A data de-duplication approach has been proposed to remove redundancy and ensure single instantiation of data. In this work, we have proposed a de-duplication based data aggregation mechanism that includes adaptive chunking algorithm (ACA). It identifies a cut-point between two windows. It includes fixed size and variable sized window that is identified as per minimum threshold for windows size. Our algorithm locates a second level variable length chunk based on the delimiter to improve the size of variable length window. The algorithms have been simulated using NS-2.35 on Ubuntu where TCL code is used for deploying sensing devices and message initiation. C language is used for implementing the algorithms, message receiving and sending among sensors, head nodes and sink nodes. Test results show that increase in variable sized window is measured by 65.6%, 68% and 71.2% in case of RAM, AE and proposed ACA, respectively. It results in better de-duplication identification. In this case, collector nodes consume 64% more energy as compared to sensor nodes. Results show better performance of proposed scheme over counterparts in terms of cut-point identification failure, fixed and variable length chunk size, average chunk size, number of chunks, cut-point identification failure and energy consumption.
PROBLEMS, CONSEQUENCES AND THEIR SOLUTIONS FOR EMOTION BASED REQUIREMENT ENGINEERING IN GLOBAL SOFTWARE DEVELOPMENT – A GUIDELINE Software Requirement Engineering (SRE) is a valued domain of software engineering. The success of a software project is mainly dependent on good requirement engineering practices. Emotion based requirement engineering is said to increase the credibility of requirement engineering. When requirement engineering is taken to a bigger scenario of global software development (GSD), it becomes more tricky and difficult to handle. There is a lack of studies focusing on emotion based requirement engineering in GSD. Due to lack of such studies academicians, researchers and practitioners are unaware of the problems and their consequences on successful software development. The proposed study identifies the problems due to lack of emotion based requirement visualization, consequences of these problems, overcoming strategies / solutions for these problems. The systematic literature review (SLR), expert evaluation and survey are used as methodology instrument. Twenty three (23) problems were identified through SLR. Besides, the consequences and solutions of the identified problems are also found out by SLR and are evaluated through experts. In SLR conduction, ak9t first 60 papers were collected which reduced to 30 after assessing their quality. For extraction of potential problems from the literature, their consequences and solutions, grounded theory was applied. Furthermore, a survey is conducted to evaluate the practicality of the identified problems, consequences and overcoming solution strategies in real working environment. The study provides a comprehensive guideline for the practitioners, academicians and researchers for performing better visualization of emotion based requirement engineering in GSD environment which increases ratio of success. The visualization support of requirements may best be achieved in this way.
POSITION BASED ROUTING IN VEHICULAR AD HOC NETWORKS Internet of Things (IoT) involves a large number of smart gadgets along with sensing capabilities to exchange the information across multiple networks. IoT enabled Vehicular Ad-hoc Network (I-VANET) comprises of a large number of vehicles that are connected with neighboring vehicles to exchange data with central repositories. In this scenario, network has a dynamic nature due to high mobility of vehicles or nodes in a smart city environment. Present routing protocols do not meet the challenging requirements for this scenario and position based routing protocols are considered to be a suitable solution. Position based routing protocols also encounter problems in city environment due to obstacles like buildings, trees that block line of sight communication among vehicles within a small area. In this research work, we have proposed a Dynamic Position Based Routing (D-PBR) scheme. It considers the vehicle’s position coordinates along with direction of movement parameters to decide about the next node towards the destination. In this scenario, we have considered the road junctions where different vehicles can join or leave to bring a change in the neighboring vehicle set. We have presented a Dynamic Next-hop Identification (DNI) algorithm that selects the best suitable next-hop vehicle available at the junction to forward the packet towards the destination vehicle. It calculates the distance and direction of neighboring nodes and then identifies the vehicles that can transmit the message in the direction of destination vehicle. It also maintains array-lists to store expected next-hop vehicles and then select the one vehicle. It considers least distance and more accurate direction as per current position of the vehicle that contains the packet for forwarding to the destination vehicle. The work has been validated by simulations using NS 2.35 with TCL scripts and C code along with AWK scripts to extract results from trace files. Results show on the improvement over the existing RIDE protocol regarding end-to-end delay, residual energy, mean hop count, average throughput and average number of vehicles. The average number of vehicles for different densities decreases by 42.86% and mean hop count used for message exchange is decreased by 60% as compared to RIDE.
ENERGY EFFICENCY IN LINEAR WIRELESS SENSOR NETWORK FOR AUTONOMOUS MONITORING AND MAINTENANCE OF LIFELINE INFRASTRUCTURES Abstract Recently, linear wireless sensor networks (LWSNs) have been eliciting increasing attention because of their suitability for applications such as protection of critical infrastructures. Most of these applications require LWSN to remain operational for a longer period. However, the non-replenishable limited battery power of sensor nodes does not allow them to meet these expectations. Therefore, a shorter network lifetime is one of the most prominent barriers in large-scale deployment of LWSN. Unlike most existing studies, in this study, we analyze the impact of node placement and clustering on LWSN network lifetime. This research work has implemented and analyzed conventional clustering protocols such as Distributed Energy-efficient Clustering (DEEC), Developed Distributed Energy-Efficient Clustering (DDEEC), and Energy Efficient Scheme for Clustering Protocol Prolonging the Lifetime of Heterogeneous Wireless Sensor Networks (TDEEC) in context LWSN. First, existing node placement and clustering schemes have been categorized and classified for LWSN and various node placement schemes have been introduced for disparate applications. Then, we highlight the peculiarities of LWSN applications and discuss their unique characteristics. The research work has implemented and analyzed different node placement schemes for linear wireless sensor network. Simulation results use MATLAB clearly indicates that, Grid-Triangular node placement scheme, enhances network lifetime as compared linear sequential and linear parallel node placement scheme. The performance metric used in all node placement schemes is similar to DEEC, DDEEC and TDEEC based conventional clustering schemes. Grid Triangular node placement scheme improves 51 % network lifetime compared to linear sequential and linear parallel node placement schemes. Other than this, it has also been observed that, node placement and clustering schemes significantly affect LWSN lifetime. Keywords Linear wireless sensor networks, node placement, clustering, network lifetime, energy efficiency, performance analysis.
Hybrid Indoor Position Estimation Technique using Fingerprinting and MinMax Approach Position estimation means locating position with reference to some coordinate system, i.e. two dimensional (x, y) or with reference of an object to some known land mark. This thesis focuses on indoor position estimation using Bluetooth, which is a low cost, easily available Radio Frequency (RF) based wireless technology. Most of the latest indoor positioning systems use Bluetooth due to its low cost and wide spread use in most of the wireless gadgets including smart phones, digital watches, and other handheld devices. Accuracy is one of the most challenging issues in position estimation, which depends on accurate signal transmission and reception, conversion of received signal to distance estimates and modeling of distance estimates to localize object position. Position estimation consists of two main steps, signal measurements and position estimation based on signal. In this thesis, we have focused on both steps, i.e. signal modeling and localization or position estimation. In step one, we perform real time experiments to collect Bluetooth signal measurements, i.e. Received Signal Strength Indicator (RSSI), which is a parameter widely used for distance and position estimation. Experimental and simulation results conclude that there is 10 dBm variation in RSSI due to additive noise, multipath, shadowing, interferences with physical objects and hence affect distance estimation, which ultimately results in position estimation error. Real time experimental results validate this variation, and conclude that if optimized radio propagational constants are chosen, position estimation accuracy up to 1.32 m can be achieved in the presence of 10 dBm variation in the radio signal. In step two, we propose a new hybrid position estimation approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea in our proposed hybrid approach is use of Euclidian distance formulation instead of indoor radio propagation model to convert the signal to distance. We have tested our proposed hybrid position estimation technique in Matlab 7.1 using real time experimental data and compared its results with fingerprinting and lateration based position estimation techniques. Simulation results show that, the proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach. Keywords: Localization, Distance Estimation, Fingerprinting, K-NN, MinMax, Trilateration