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LASEC: A LOCALIZED APPROACH TO SERVICE COMPOSITION IN PERVASIVE COMPUTING
ENVIRONMENTS
Ms.K.Suganya
1, Ms.A.Kiruthika
2, Ms.S.Sudha
31,3
PG Scholar,
2Assistant Professor, Department of Computer Science,
Dhanalakshmi Srinivasan College of Arts & Science for Women, Perambalur, Tamil Nadu (India)
ABSTRACT
Pervasive computing environments (PvCE) are embedded with interconnected smart devices which provide users with services desired. To meet requirements of users, smart devices with different kinds of functions may need to be associated together to provide the service described in the user requirement, which is called service composition. As the service composition environment may be dynamic and large scale, centralized service composition algorithm is usually inefficient due to message cost. On the other hand, a decentralized approach, which employs pre-determined coordinators to search and compose service, may have high cost as well. In this paper, we discuss a localized approach for service composition based on the Ubiquitous Interacting Object (UIO) model we have proposed earlier. UIO is an abstraction of physical devices in PvCE with ability to find and collaborate with other devices through exposing their capabilities as services. In our localized service composition algorithm (LASEC), UIOs collaborate with each other in a bottom-up, localized manner to compose required service without requiring global knowledge. To solve the problem of blind compositions in LASEC, we propose a novel mechanism called Alien- information-based Acknowledging (A-Ack), in which a UIO decides on collaborating with another UIO only after obtaining some additional information from the collaboration candidate. Specifically, this information refers to ability of a given UIO to compose another part of the service. Proposed LASEC is message-efficient and quality- guaranteed. Extensive simulations of LASEC as well as existing decentralized and pull-based centralized algorithms have been conducted. The results show the relatively low communication cost and composition time of LASEC.
Moreover, we demonstrate feasibility of our approach with a prototype implementation.
Keywords: Central Approach, Decentralized Approach, Localized Service Composition Algorithm (Lasc), Pervasive Computing Environment, Ubiquitous Interacting Object Model.
I. INTRODUCTION
The widespread deployment of pervasive computing devices is transforming the physical world into a pervasive computing environment (PvCE). Devices with sensing, memory, computing and communication capabilities are immersed into our living environments. For example objects at home can serve as interfaces for controlling robots
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mirrors can help with shopping and wearable computing enables trend of computing on the body. PvCE becomes a medium that provides user with all the functionality he needs to satisfy his requirements. It is built on physical objects in the real world with embedded computing devices interconnected according to an underlying communication network. We define the physical objects having intelligent abilities of pervasive computing as Ubiquitous Interacting Objects (UIO). UIOs are supposed to be able to intelligently collaborate with each other. For this purpose, objects can be augmented with sensing, processing actuating, or communication devices. These capabilities are implemented as services and exposed to the potential requestors. Through services users can interact with a PvCE. UIOs can provide hardware or software services. Examples of software services can be weather forecast, stock quotes, and language translation. Services provided by the hardware UIOs can be for example controlling a temperature in the room or printing a document. UIOs that expose their functionalities in the form of services are called service providers, and UIOs that need some functionality are called service requestors.Requestors of services can be users, other objects or middleware that facilitate interaction in PvCE. Service providers in PvCE are characterized by their heterogeneity and dynamicity. Service composition mechanisms are necessary when no single service provider can provide a requested service and several service providers have to collaborate. Service composition application in pervasive computing environment. Consider that user specifies the requirement of watching the movie, for which following services must be satisfied: file with the movie, movie player, device to output sound of the movie, and device to output its display. During runtime usually multiple instances of requested functionalities can be found. And apart from the services specified in the requirement, other services exist in the environment as well. According to requirements, we need to select one instance for each service type.
II. PROBLEM DESCRIPTION 2.1 Existing System
In the existing system To remedy the problem of maintaining global information, we have opted to adopt a localized approach, which naturally tends to select compositions with high degree of composition locality. We have learned that localized approach can significantly decrease the communication cost as well as response delay. However, we are unaware of any localized algorithms for service composition in pervasive computing environments.
2.1.1 Disadvantage 1. Less Security 2. Les Response 3. Les User Queries
2.2 Proposed System
In proposed a localized algorithm named LASEC for service composition. The LASEC algorithm is suitable for service composition applications deployed in large scale and dynamic environments. In LASEC, no coordinator collects global information, nodes and links between them are mapped in subsequent stages and nodes construct only local part of the solution, while together achieving global goal. Comparing with existing decentralized and pull
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based centralized approach, the proposed localized algorithm is delay-constrained and message-efficient. With an efficient composition process, the proposed algorithm can be easily combined with the existing calculation methods on composition QoS to achieve high quality of composed service.2.2.1 Advantages 1. High Security 2. More Keywords 3. More Accuracy
III. METHODOLOGY
1. Big Data
2. Data pre-processing 3. Query Based Clustering 4. Collaborative filtering 5. Exhaustive search 6. Rating
III. MODULE DESCRIPTION 3.1 Big Data
Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business, society and the Internet. Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big Data is a new term used to identify the datasets that due to their large size and complexity, we cannot manage them with our current methodologies or data mining soft- ware tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years.
Collection of Data
Traditional data processing
Extracting
Information
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Data pre-processing is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data-gathering methods are often loosely controlled, resulting in out-of-range values, missing values, etc. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, the representation and quality of data is first and foremost before running an analysis. The query submitted by the user contains parts of speech and special characters which are not required for analysis as they do not truly reflect the relevance of a search result. If this query is used for analysis, it may give inconsistent and inaccurate results. Therefore, the user query will be pre-processed to identify the root words.
3.3 Query Based Clustering
Agglomerative Hierarchical Clustering to cluster the pseudo – documents. Name of the algorithm refers to its way of working, as it creates hierarchical results in an "agglomerative" or "bottom-up" way, i.e. by merging smaller groups into larger ones. Algorithm takes as input a matrix of pairwise similarities between objects. In case of documents this matrix is created by calculating all pairwise similarities between documents using cosine similarity. It returns a binary tree of clusters, called dendrogram. Clustering is a preprocessing step to separate big data into manageable parts. There are many clustering algorithms available to us. The main characteristics that guide our choice are the following ones: The algorithm should not require manual setting of the resulting form of the clusters, e.g. the number of clusters. It is unreasonable to determine these parameters manually in advance.
3.4 Collaborative Filtering
Collaborative filtering (CF) is a technique. Collaborative filtering is a method of making automatic predictions. All services are stored in a table which is called service table. The corresponding elements will be drawn from service table during the process of CF. Collaborative filtering has two senses, a narrow one and a more general one. In general, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or
Files
Clustering Algorithm
s
Group of
clustering View
Informatio n Data
Source
Analysis Data
Identifying
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multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data, etc.3.5 Exhaustive Search
The search proceeds by generating and testing each node that is reachable from a parent node before it expands any of these children. Exhaustive systematic search which is referred to a breadth-first search. The system retrieves the stored records into memory. We find that the fastest method is loading all data (about 320 MB) at once with one SQL query, instead of fetching one by one. For discrete problems in which no efficient solution method is known, it might be necessary to test each possibility sequentially in order to determine if it is the solution. Such exhaustive examination of all possibilities is known as exhaustive search, direct search, or the "brute force" method. Unless it turns out that NP-problems are equivalent to P-problems, which seems unlikely but has not yet been proved, NP- problems can only be solved by exhaustive search in the worst case
3.6 Rating
A rating is the evaluation or assessment of something, in terms of quality (as with a critic rating a novel), quantity (as with an athlete being rated by his or her statistics), or some combination of both. Then, the system upgrades the search results which include the emphasized term or sentence. The system rating the search results according to the user intention and shows the rating results to the user.
IV.CONCLUSION
A proposed a localized algorithm named LASEC for service composition. The LASEC algorithm is suitable for service composition applications deployed in large scale and dynamic environments. In LASEC, no coordinator collects global information, nodes and links between them are mapped in subsequent stages and nodes construct
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only local part of the solution, while together achieving global goal. Comparing with existing decentralized and pull based centralized approach, the proposed localized algorithm is delay-constrained and message-efficient. With an efficient composition process, the proposed algorithm can be easily combined with the existing calculation methods on composition QoS to achieve high quality of composed service.V.ACKNOWLEDGEMENT
The author deeply indebted to honorable Shri A.SRINIVASAN(Founder Chairman), SHRI P.NEELRAJ(Secretary) Dhanalakshmi Srinivasan Group of Institutions,Perambalur for giving me opportunity to work and avail the facilities of the College Campus . The author heartfelt and sincere thanks to Principal Dr.ARUNA DINAKARAN, Vice Principal Prof.S.H.AFROZE, HoDMrs.V.VANEESWARI,(Dept. of CS & IT) Project Guide Ms.G.Priyadharshni ,(Dept. of CS & IT) of Dhanalakshmi Srinivasan College of Arts & Science for Women,Perambalur. The author also thanks to parents, Family Members, Friends, Relatives for their support, freedom and motivation.
REFERENCES
[1]. Andrew Troelsen Pro C# 2008 and the .NET 3.5 Platform, Fourth Edition [2]. Herbert Schildt C# 3.0: The Complete Reference www.w3cschools.com [3]. MSDN- www.msdn.microsoft.com (The Microsoft Developer Network)
BIOGRAPHICAL NOTES
Ms. K.SUGANYA is presently pursuing M.Sc.,Final year the Department of Computer Science From Dhanalakshmi Srinivasan College of Arts and Science for Women, India.
KIRUTHIKA A - Received M.C.A., M.Phil Degree in Computer Science. She is currently working as Assistant Professor in Department of Computer Science in Dhanalakshmi Srinivasan College of Arts and Science for Women, Perambalur Tamil Nadu, India.She has Published papers in IJSTM & IJIRCCE journals and also Published two books Namely ” Computer Basics and Internet ” and “Introduction to Languages C,C++,Java” Her research areas are Networking,Web Technology and Cloud Computing
Ms. S.SUDHA is presently pursuing M.Sc.,Final year the Department of Computer Science From Dhanalakshmi Srinivasan College of Arts and Science for Women, India.
