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Updates found with 'trouble'

IEEE 2016-2017 BIG DATA PROJECTS ABSTRACT:- SELF-HEALING IN MOBILE NETWORKS WITH BIG DATA ABSTRACT:- Mobile networks have rapidly evolved in recent years due to the increase in multimedia traffic and offered services. This has led to a growth in the volume of control data and measurements that are used by self-healing systems. To maintain a certain quality of service, self-healing systems must complete their tasks in a reasonable timeThis article reviews the data that self-healing uses as input and justifies its classification as big data. Big data techniques applied to mobile networks are examined, and some use cases along with their big data solutions are surveyed. EXISTING SYSTEM:- To better understand the meaning of the data, experts often use statistical techniques to simplify the representation of the collected variables. For instance, to detect degradations on PIs or KPIs, thresholding is often used, that is, the value is considered degraded if it is above or below a certain threshold. Thresholding is especially important in the detection stage, but it is also used to discretize the value of PIs, classifying them as either good or bad values. With discretized variables, heuristic rules of the “if … then form are often used in the diagnosis process. This pattern is generally used by experts in their observation of low-level PIs, and sometimes these rules are coded as programs and used in the database containing the PM, FM, and CM variables to test for known fault states. Another technique that is sometimes used to test the relations among variables is correlation. For instance, alarms or engineer actions are correlated with PIs to see to what extent they are responsible for the observed behavior. PROPOSED SYSTEM:- The main objective of self-healing is to automate the troubleshooting task described earlier by using programs that replicate the manual processes. Commercial requirements (the demand for QoS) create the need for a fast and reliable troubleshooting system that minimizes downtime. Therefore, automation in troubleshooting is required. Self-healing algorithms are usually implemented using knowledge-based systems (KBSs) that imitate the process of human experts in order to accomplish a task. KBSs are algorithms composed of two parts: Knowledge base (KB): a codified representation of the field knowledge, that is, the knowledge that the experts need in order to complete the task. To generate and improve the KB, a continuous data mining (DM) process is run in the batch layer. Inference engine (IE): the procedures that use the KB in order to complete the task.For an algorithm to be parallelizable, its design must guarantee that the final result is the samewhen it is run as a single process and when the task is divided among multiple instances.ADVANTAGES:-Communication has enhanced to convey the information quickly to the consumers.Working professionals can work and access Internet anywhere and anytime without carrying cables or wires wherever they go. This also helps to complete the work anywhere on time and improves the productivity.  Doctors, workers and other professionals working in remote areas can be in touch with medical centres through wireless communication. Urgent situation can be alerted through wireless communication. The affected regions can be provided help and support with the help of these alerts through wireless communication. Wireless networks are cheaper to install and maintain.H/W SYSTEM CONFIGURATION:- System - Pentium –IV 2.4 GHz Speed - 1.1 Ghz RAM - 256MB(min) Hard Disk - 40 GB Key Board - Standard Windows Keyboard Mouse - Logitech Monitor - 15 VGA Color.S/W SYSTEM CONFIGURATION:-Operating System : Windows/XP/7.Application Server : Tomcat 5.0/6.0 Front End : HTML, Java, Jsp Scripts : JavaScript.Server side Script : Java Server Pages.Database : MongoDBDatabase Connectivity : Robomongo-0.8.5-i386. CONCLUSION: - Automation of the O& M of mobile networks is a need for mobile operators due to several forces driving costs up: increased traffic volume, variety of services, number of subscribers, demand for QoS, and competitiveness among operators. Modern networks (especially LTE and the future 5G networks) produce large volumes of O& M data, which contain the information needed for the self-healing functions that operators need. In fact, the volume of this data, along with the variety and time restrictions, calls for the use of the big data paradigm. Big data is a set of new techniques that exploit the ever increasing processing power of computer networks. Specifically, in this article, the main focus is parallelization. Several use cases have been displayed as examples where data processing algorithms can extract information contained in big-data-compliant data sources 
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IEEE 2016 NS2 PROJECTS ABSTRACTSOFTWARE DEFINED GREEN DATA CENTER NETWORK WITH EXCLUSIVE ROUTING ABSTRACT: The explosive expansion of data center sizes aggravates the power consumption and carbon footprint, which has restricted the sustainable growth of cloud services andseriously troubled data center operators. In recent years, plenty ofadvanced data center network architectures have been proposed. They usually employ richly-connected topologies and multi-path routing to provide high network capacity. Unfortunately, they also undergo inefficient network energy usage during the traffic valleytime. To address the problem, many energy-aware flow scheduling algorithms are proposed recently, primarily considering how to aggregate traffic by flexibly choosing the routing paths, with flows fairly sharing the link bandwidths. In this paper, we leverage software defined network (SDN) technique and explore a new solution to energy-aware flow scheduling, i.e., scheduling flows in the time dimension and using exclusive routing (EXR) for each flow, i.e., a flow always exclusively utilizes the links of its routing path. The key insight is that exclusive occupation of link resources usually results in higher link utilization in high-radix data center networks, since each flow does not need to compete for the link bandwidths with others. When scheduling the flows, EXR leaves flexibility to operators to define the priorities of flows, e.g., based on flow size, flow deadline, etc. Extensive simulations and testbed experiments both show that EXR can effectively save network energy compared with the regular fair-sharing routing (FSR), and significantly reduce the average flow completion time if assigninghigher scheduling priorities to smaller flows.
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IEEE 2016-2017 EMBEDDED PROJECTS ABSTRACTA REAL-TIME ELECTRICALLY CONTROLLED ACTIVE MATCHING CIRCUIT UTILIZING GENETIC ALGORITHMS FOR WIRELESS POWER TRANSFER TO BIOMEDICAL IMPLANTS ABSTRACT:- This project discusses the feasibility of a real-time active matching circuit (MC) for wireless power transfer applications, especially for biomedical systems. One prototype of low-cost real-time automatic MC, utilizing a variable circuit topology, including discrete passives and p-i-n diodes, has been implemented and the principle has been verified by measurements. One genetic algorithm was introduced to optimize the design over a wide range of impedances to match. As a result of preliminary operation verification tests, the proposed real-time MC system results in improving the transfer coefficient. Similar performance improvement results were observed in additional tests under misaligned conditions, as well as for nonsymmetrical Tx–Rx coil configurations further verifying the potential applicability of the proposed system to practical biomedical devices.EXISTING SYSTEM:- Wire connections between electronic products already are accustomed to everyone. Wire connections could sometimes, become trouble and inconvenience us. Existing technologies can be classified into two categories: electromagnetic (EM) radiation and magnetic resonant coupling.  Higher loss in the measurement results is assumed to be associated with greater radiation loss because of the fabrication error.PROPOSED SYSTEM:- Wireless energy transfer (WET) unique capability of HF waves and microwaves to transfer power to sealed devices in a contactless/cable less way is a major advantage. Efficiency is very high due to transfer power without a man-made conductor connected between power source.HARDWARE REQUIREMENT:- PIC16F877A WPT HEART BEAT SENSOR BLOOD PRESSURE SENSOR TEMPERATURE SENSOR ZIGBEE PERSONAL COMPUTERSOFTWARE REQUIREMENT:- EMBEDDED C MP LAB IDE
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2017 - 18 Bigdata titles and abstractsData Management in a Private Cloud Storage Environment Utilizing High Performance Distributed File SystemsAbstract: One of the key goals in mishap information investigation to recognize the fundamental elements related to a street and car crash. In any case, heterogeneous nature of street mishap information makes the examination errand troublesome. Information division has been utilized generally to defeat this heterogeneity of the mishap information. In this paper, we proposed a system that utilized K-modes grouping strategy as a preparatory errand for the division of 11, 574 street mishaps on street system of Dehradun (India) in the vicinity of 2009 and 2014 (both included). Next, affiliation lead mining are utilized to recognize the different conditions that are related to the event of a mischance for both the whole informational collection (EDS) and the groups distinguished by K-modes bunching calculation. The discoveries of bunch based investigation and whole informational collection examination are then looked at. The outcomes uncover that the blend of k mode grouping and affiliation administer mining is extremely rousing as it produces critical data that would stay covered up if no division has been performed preceding create affiliation rules. Encourage a patterninvestigation have likewise been performed for each bunches and EDS mishaps which find diverse patterns in the various group while a positive pattern is appeared by EDS. Drift examination additionally demonstrates that earlier division of mishap information is vital before investigation.GanesanContact-9865862045g14ganes@gmail.com
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