P. Uma Maheswari

Dynamic Detection and Prevention of Clone Attack in Wireless Sensor Networks

Wireless sensor networks are often deployed in adverse environments where an attackers can physically capture some of the nodes, first can reconstruct the programme, and then, can replicate them in large number of clones, easily takeover the control of network. Wireless Sensor Networks highly indispensable for securing network protection. Various kinds of major attacks have been documented in wireless sensor network, till now by many researchers. The Clone attack is a massive harmful attack against the sensor network where large number of genuine replicas are used for illegal entry into a network. Discerning the Clone attack, Sybil attack, sinkhole, and wormhole attack while multicasting is a excellent job in the wireless sensor network. The existing method Randomised, Efficient, and Distributed (RED) has only a scheme of self-healing mechanism, which just verifies the node identities by analyzing the neighbours. A survey was done on a Clone attack on the objective of dissolving this problem. The overview of survey has proposed a combined PVM (position verification method) with MVP (Message Verification and Passing) for detecting, eliminating, and eventually preventing the entry of Clone nodes within the network.Wireless sensor networks are often deployed in adverse environments where an attackers can physically capture some of the nodes, first can reconstruct the programme, and then, can replicate them in large number of clones, easily takeover the control of network. Wireless Sensor Networks highly indispensable for securing network protection. Various kinds of major attacks have been documented in wireless sensor network, till now by many researchers. The Clone attack is a massive harmful attack against the sensor network where large number of genuine replicas are used for illegal entry into a network. Discerning the Clone attack, Sybil attack, sinkhole, and wormhole attack while multicasting is a excellent job in the wireless sensor network. The existing method Randomised, Efficient, and Distributed (RED) has only a scheme of self-healing mechanism, which just verifies the node identities by analyzing the neighbours. A survey was done on a Clone attack on the objective of dissolving this problem. The overview of survey has proposed a combined PVM (position verification method) with MVP (Message Verification and Passing) for detecting, eliminating, and eventually preventing the entry of Clone nodes within the network.

Automatic assessment of descriptive answers in online examination system using semantic relational features

The revolution in technology reduces the effort of manpower in many of the areas. The boon of the technology and rapid advancements in education industry has provided a good learning environment. It offers qualification and credits at the desktop through online courses and evaluation. The prevailing system has its own pause in terms of volume, staffing, variation in the strategies of assessing. As of now, the objective-type questions alone can be practiced and assessed through online examinations. Researchers strive to build systems for evaluating descriptive answer as it is challenging and could not take up its full sway for complete automation. The challenge lies in recognizing the natural language answers and extracting the precise meaning so as to appropriately evaluate the knowledge obtained by the student. The proposed method contains stages such as question classification, answer classification and answer evaluation for the answers given by the student and grade them with appropriate score. A syntactical relation-based feature extraction technique is proposed for automatic evaluation of descriptive-type answers. The system has also adopted a cognitive-based approach where the student answers are judged for its correctness based on the phrases used for answering the questions. The score and feedback are provided to make aware of the understanding level of the subject. The experimental analysis shows .85% higher precision and recall when compared to the earlier systems.

An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment

Cloud Computing is an emerging Technology which provides IT services. Using cloud resources, computational resource requirement and ubiquitous nature are available at ease and also to pay only for availed resources are achieved through Cloud Computing environment. Here scheduling the job to the appropriate resource is an NP hard problem. Ensuring QoS during job scheduling to the users is a most prominent need. As scheduling takes place in third party’s boundary, assuring its security is an important criterion. To provide QoS such as makespan, cost, reducing task migration during the schedule and enforcing the security are the significant objective of the proposed work. Using the proposed improved efficient—Artificial Bee Colony algorithm these objectives are achieved. The implementation results prove that the proposed system achieves the objective of secure job scheduling with assured QoS.

A framework for measuring similarity between Terms in Short Text Categorization

Due to the increase in the information availability on the World Wide Web, it becomes too tough for the search engine to provide the precise results for the user. Some information on the web pages is ambiguous in nature. Semantic similarity is worn to measure the similarity score for the text and it improves the efficiency of the search by obtaining the user query and process them consistent with the searchers intent and it produces the contextual meaning of terms which generates similar results for the query. The semantic search system considers the position of words, user intention, synonyms and relationship between words to produce the correct results for the user. Generating similarity between two ideas is essential for various applications and those applications are used for producing the user satisfactory results. However, the existing approach is additionally appropriate for semantic similarity between words instead of Multi-Word Expressions (MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type of classification is suited for opinion mining with the tweets from twitter, topic content discovery etc.

Precision Irrigation using Wireless Sensor Networks for Indian Agriculture -A Survey

Wireless sensor Networks (WSNs) are wide utilized in motoring and collecting interests of environment information. Besides the challenging environmental conditions, India needs efficient and cost effective Wireless sensor and actuator Network (WSAN) to assist the farmers and improve the crop production. This paper surveyed varied international automated irrigation approach and compared it with the Indian scenarios to adopt the most effective irrigation technique. Existing approaches requires an efficient decision support system that can work well with Indian conditions.

The C2hg Model based Multi Attribute Crop Yield Estimation and Water Regulation System for Indian Soil using Artificial Neural Network

The environmental change of world affects the growth of crops in many ways where any crop has its own environmental conditions and needs other factors like hydrology, climate, crop pattern and geological information. This also affects the crop yield in many ways, and the hydrological factors are the most affecting reasons for plant growth. To solve the problem of crop yield and to improve the plant growth, a C2HG-(Climate-Crop-Hydrology-Geology) model has been discussed. The method maintains the pattern of crop yield at different time window in the past, and each pattern has been initialized with a neuron with various factors and functions. The patterns are submitted to the artificial neural network where each pattern has been initialized with neuron and performs various functions to produce the final result. Each neuron estimates possible crop yield and computes the amount of water to be regulated using multi attribute water regulation scheme. The estimated values are passed through neurons of different layers of ANN. The method estimates the plant growth using the patterns of hydrology, climate, crop and geological information. The method produces a more efficient result in helping the plant growth for the development of agricultural industries.

An Optimized Multi-Objective Job Scheduling in Cloud Environment

Cloud computing is designed for multiple users who can access services, accommodate large volumes of data and build applications. Managing all the resources and to provide on-demand access among several users is complex and difficult. All the inter-dependent tasks need to be properly scheduled and managed so that the resources are utilized effectively and tasks are executed on a lesser time with low cost. Several scheduling algorithms were proposed to schedule tasks and satisfy objectives such as either cost improvement or execution time improvement or server utilization. To improve the scheduling efficiency and to obtain multiple objectives like reduced execution time, cost and server utilization a framework for effective scheduling of tasks in cloud environment is proposed based on the Artificial Bee Colony (ABC) The proposed framework is analyzed in terms of execution time, cost and server utilization and the results are optimal for an effective cloud environment.

Optimal Fragment Selection for Data Security and Efficient Content Alteration Mechanism in Cloud using IABC

Cloud computing is an emerging technology which attracts customers by offering reduced cost, space and virtually unlimited dynamic resources for storage, computation etc. User shares the sensitive data over the cloud which gives rise to security issues in cloud computing. So, to protect users data a secure methodology of fragmentation and replication of data is used in this paper. Optimal Threshold value is chosen to perform fragmentation of the data file by means of Improved Artificial Bee Colony (IABC) algorithm. Based on the optimal threshold value, data file is fragmented. Once the fragmentation is done, the proposed methodology selects the cloud nodes for fragment placement. The nodes are selected in such a way that the access time is always reduced by giving equal preference to both security and performance in terms of the access time. The centre most nodes are carefully chosen to provide better access time using centrality measure in the cloud network. Based on these measures, our data resides secured in cloud. Replication strategy provides reliable access to data fragments that improves the trustworthiness of the system.