Data Access on Wireless Broadcast Channels using Keywords

Data Access on Wireless Broadcast Channels using Keywords Mr. Vijaykumar Mantri 1, Mr. Nagaraju A 2 Dept of IT, Padmasri Dr. B. V. Raju Institute of Technology, Narsapur, Dist.Medak, Andhra Pradesh, India. 1 vijay_mantri.it@bvrit.ac.in 2 raju_cs5200@yahoo.co.in Abstract-The rapid development of wireless network and powerful portable computer technologies has accelerated the development of mobile computing technologies and wireless information systems, thus resulting in the increased and wide spread use of mobile computing devices. Energy and latency efficiency are two critical issues in wireless data broadcast system. For measuring energy and latency efficiency, we have two basic metrics namely access time and tuning time. Several indexing techniques are there for accessing data items on broadcast channel which are useful to reduce the tuning time. In wireless broadcast channel, for text searches, index scheme using keyword search will be implemented with the help of inverted list index method and two level indexing methods to improve the latency performance and energy usage. We propose a change in algorithm to increase its functionality and efficiency of the analysis is demonstrated through graphs. Keywords: Wireless Data Broadcasting, Mobile computing, Full text search, Energy efficiency, Latency, Index Structure. I. INTRODUCTION The use of laptops, portable computers, and PDAs over mobile communication networks is increasing, and mobile applications such as monitoring stock market prices, traffic reports and weather forecasts are widely available in wireless environments. In such an environment, a large number of mobile clients may be querying databases over unreliable, slow, and expensive wireless communication links. Data broadcasting is an efficient method of data dissemination that can overcome the limitations of a wireless environment, such as its low communication bandwidth and the energy constraints of mobile devices. A data server can broadcast data periodically, and mobile clients can listen on one or more channels to access the data that they require. The rapid development of wireless networks and wireless devices along with the computer technologies have led to the concept of mobile computing. Through the wireless networks, the mobile users can access a large variety of information from anywhere and at any time. A large number of mobile clients may be querying databases over unreliable, slow, and expensive wireless communication links. Data broadcasting is an efficient method of data dissemination that can overcome the limitations of a wireless environment, such as its low communication bandwidth and the energy constraints of mobile devices [1]. A data server can broadcast data periodically, and mobile clients can listen on one or more channels to access the data that they require. The rapidly expanding technology of cellular communications and wireless local area networks gives mobile users the ability of accessing information anywhere and anytime. The types of information that become accessible through wireless are boundless and include stock quotes, weather and traffic information and news etc. [2], [5], [8]. Broadcast on mobile system has some restrictions compared to broadcast on wired system. Broadcast disks scheme has some efficient way of delivering data using air as broadcast medium. So the problem broadcast disks address is really finding an optimal broadcast scheduling policy. With server-side scheduling and caching at the client-side is also needed to make more optimal use of broadcast disks. In this broadcast disks technique mainly two groups are involving those are networking and Database. Networking group focuses on the minimizing average expected delay time and tuning time from broad cast data through dynamic scheduling. Tuning time is the amount of time spent in actively listening to the broadcast channel it has a direct implication on the energy consumption. Average expected delay time the amount of time client must wait for an average request. Database group focuses on data integrity, client-side cache management and indexing. Access efficiency and power conservation are two critical issues in any wireless data system [2]. Due to these issues, the traditional request-response system [7] is no longer suitable for data dissemination in the wireless environment. Access efficiency concerns how fast a request satisfied, and power conservation concerns how to reduce a mobile client s power consumption when it is accessing the data clients, which ranges from a few hours to about half a day under continuous usage of portable mobile devices. Two basic performance metrics, namely access time and tuning time are used to measure access efficiency and power conservation for a broadcast system, respectively [2], [6], [5]. Access time. It is the time elapsed between the moment when a query is issued and the moment when it is satisfied. IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 1

Tuning time. It is the time a mobile client says active to receive the requested data items. A. Index Broadcasting Original broadcast disks scheme do not use indexing. So the broadcast data has to be self-identifying and broadcast has to be done periodically for concurrent access to the data. Even with fixed cyclic broadcast, clients have to spend lots of time actively listening to the broadcast to acquire the information they need. The two index structures for efficient processing of fulltext searches on the wireless broadcast stream. Inverted list and inverted list+ index tree, although these data structures are popular ones in various disk-based computer applications, their use in the area of wireless broadcasting needs different considerations. The replication of index structures is studied and two extensions are developed: (1,α) and (1,α(1,β)) methods. In these methods, inverted lists and index trees are replicated and placed on the air channel intermixed with data, and enable mobile clients to find the data on the air more efficiently. The algorithms for full-text search on the wireless broadcast stream generated by (1, α) and (1, α (1, β)) methods are described. These algorithms are independent of the two replication parameters α and β. II. EXISTING SYSTEM In Broadcast disk method, data are sent to the mobile clients simultaneously in repeated and cyclic manner. Tuning time and access time are the two important performance metrics in any wireless data system. Access time will be improved by broadcast disk method which is one of the methods in push based data scheduling. Air indexing can be used to reduce the power consumption. Power consumption is directly proportional to the tuning time of the client. By reducing the tuning time, it is possible to reduce the power consumption of the mobile device. The tuning time of client can be reduced by broadcasting the index of the desired data through multiple channels. The performance of the index allocation over multiple channels is evaluated in terms of tuning time of the client. All the existing index approaches have been meant for reducing tuning time and access time. The full-text search is one of the most popular query types used in various information systems, and many indexing methods have been proposed for full-text search operations in disk storage. III. PROPOSED SYSTEM We are proposing some changes in algorithms of the inverted index & index tree methods. In the above methods, algorithm search for word given by user as input at client side and retrieves file from server side which has input word. But in these methods we can only broadcast single file at any given time and we can only request for single search text at a time. We are overcoming these limitations by changing some code. In our algorithm we can retrieve more than one file from server side at any time with the help of indexing methods. The efficiency issue of the full-text search in wireless broadcasting systems is explored. The text retrieval on the conventional disk storage and that on the air channel are completely different because data access procedures and performance metrics used are different. To adapt two index structures for efficient processing of full-text searches on the wireless broadcast stream: inverted list and inverted list + index tree have been discussed. Advantages: Energy efficient: Amount of energy required for receiving a packet is much smaller than that for sending one by making use of the novel indexing scheme. Latency efficient: With the index information, mobile clients can be informed of the address (i.e., time) of their target data. In this way, they can access the data without scanning the full wireless data stream. Developing Full-Text search application over Wireless broadcast channel Full-Text search is a popular query types that is widely used in document retrieval systems, and Full- Text search operations are performed by the mobile clients. This application is developed by using java technology. Firstly at server side, develop a inverted list from the different documents and place these Inverted lists in front of the broadcast data on a wireless channel. This is called inverted list index method. In order to reduce latency overhead another index structure is going to be add i.e., index tree+ inverted list which gives less tuning time. In addition replication strategy has been added to index tree method and inverted list index method to further improve the latency performance. Now at client side after receiving the broadcasted data mobile client is going to search the desired document. Finally comparison graphs can be drawn with respect to inverted list index method and index tree + inverted list method. A. Inverted List Method In order to maintain such many-to-many relationship between words and documents the inverted list structure has been popularly used in document retrieval IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 2

systems. Here the target of our application is wireless channel, not disks. Inverted List is constructed by extracting words from each document. Here, eliminating stop words or stemming can be executed optionally. Below Fig. 1 shows the inverted list consists of words and addresses of documents containing each word. After constructing this inverted list index the server broadcasts this inverted list and documents as a stream. Here Creation of inverted list has done using one class called Inverted Index. In this class by using parameterized constructor creation of Inverted index list is developed. The syntax of constructor for inverted index list is public Inverted Index. Query Need Accessing Documents Doc 1 Doc 3 Doc 4 Doc 6 1). Distribution and Replication policy Index replication policy has been known to improve the access time performance specifically, reducing Index Wait [3], [9], [10]. By adopting the index replication strategy in our method, we extend the inverted list method into the (1, α) method. Here, α denotes the number of replications of the inverted list. According to this notation, the no index method can be described as the (1, 0) method and the inverted list method without replication can be described as the (1, 1) method. Inverted List is extended to (1, α) method by replicating the index structure and broadcasting the Index and data buckets for α number of times in the stream. Hence the inverted list is placed before each 1/α fraction of the whole database which reduces the Index Wait to a greater extent [3]. The broadcast stream is represented by (an inverted list, a part of the data), (an inverted list, a part of the data) a part of the data means 1/α fraction of the database. Receive Doc 6 Doc 8 (1,α) Xp During Execute Declare Doc 3 Doc 10 Doc 9 Doc 7 Doc 8 Doc 6 Doc 8 Doc 2 Doc 7 L D 1 L D 2. L D α L D 1 L D 2.. L D α Broadcast Broadcast Fig.3. The replication of inverted list-(1, α) method. B. Inverted list+index tree method Fig.1. An example of inverted list. Two types of buckets are constructed. Inverted List Bucket (L). An inverted list bucket is assigned to several words and lists of pointers to the documents containing them. Data Bucket (D). It contains the documents that are to be broadcasted. Index List Bucket Header Query A (Data_Bucket Query B (Data_Bucket Data Bucket _ADDR, OFFSET) _ADDR, OFFSET). Header Doc 1 Doc 2 Doc 3 Doc 4 Doc 5 Doc 6... Fig.2.The Structure of an Index Tree Bucket. The inverted list index may occupy much space on the wireless channel and if we need to find the relevant index entities one half of the inverted list index needs to be sequentially scanned. If we construct a hierarchical structure with index words so searching for a particular word becomes easy, and tuning time can be reduced. 1). Index Structure Index tree has the same structure as the conventional B+TREE [4] except that leaf nodes have pointers to corresponding inverted list buckets. The server broadcasts a stream that consists of index tree buckets (T), inverted list buckets (L), and data buckets (D) repeatedly. The stream of index tree buckets is constructed by traversing the index. 2). Distribution and Replication policy (1, α) method is denoted by the (1, α (1, β)) method which means that the inverted list is replicated α times in broadcast stream and the index tree is replicated β times for each inverted list. In this method the index tree for the inverted list is replicated and placed before every 1/β IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 3

fraction of inverted list area. Also, since the index area is replicated α times over the stream, and the index tree is replicated total of αβ times. (1, α (1, β) T L 1 D 1. T L α D α T L 1 D α+1. T L α D αβ Broadcast Fig.4 The stream layout with replication- the (1, α (1, β)) method. The layout of the broadcast stream is represented as < (an index tree, a part of inverted list, a part of data), (an index tree, a part of inverted list, a part of data), (an index tree, a part of inverted list, a part of data)>. Here a part of inverted list indicates the 1/β fraction of the inverted list and a part of the data indicates one 1/αβ fraction of the entire database. C. ACCESS ALGORITHM Data access process steps shown below in Access Algorithm. If the first bucket downloaded by a mobile client is not the start of the two-level index structure, it waits until the start of the next two-level index structure is broadcasted (lines 3-5). Once the client meets the start of the index tree, it starts to process the index tree (lines 6-22). For each internal node of the index tree (lines 8-15, 24-41), the client checks if the index keyword is between start_word and end_word (line 25). If the index keyword is between them, the client compares the words in the bucket (lines 28 33). Otherwise, the client stops to process the stream because it means that there is no document containing the index keyword (lines 10-11). If the index tree bucket is a leaf node and contains the index keyword (lines 35-40), the client waits for the index list bucket pointed by the word s pointer to be broadcasted (lines 13-14) and process the bucket as described in Section 3.3 (lines 16-21). Otherwise, the client finds the time that the appropriate child bucket is broadcasted from PIS information, waits for the child bucket, and repeats the process described above (lines 13-14). Algorithm Data access (1, α (1,β)) steam Input: An index keyword IK; Output: A set of documents containing the index keyword; 1: Document Set :={}; 2: SB: = the searching bucket; 3: If (SB is not starting bucket of an two-level index structure) then 4: Wait for the starting bucket of the next two-level index structure to be broadcasted: 5: END-IF 6: LOOP 7: SB: = the searching bucket; 8: If (SB is an index tree bucket) THEN 9: PIS: = Process two-level index structure Bucket (SB, IK); 10: IF (PIS is null) THEN 11: EXIT LOOP; 12: ELSE 13: Wait for the bucket pointed by PIS to be broadcasted 14: CONTINUE; 15: END-IF 16: ELSE IF (SB is an index list bucket) THEN 17: IF (IK is contained in SB) THEN 18: Document set: =Process two-level index structure Bucket (SB); 19: EXIT LOOP; 20: END-IF 21: END-IF 22: END-LOOP 23: RETURN Document Set; /* End of data Access Process*/ Algorithm Processed two-level index structure Bucket Input: A bucket B, An index keyword IK; Output: A pointer pointing a bucket that should be visited next; 24: IF (B is an internal node) THEN 25: IF (IK is smaller than B.start_word or larger than B.end_word) THEN 26: RETURN null; 27: END-IF 28: FOR EACH B.Word DO 29: IF (IK is smaller than or equal to B.word) THEN 30: RETUEN B.PIS: 31: END-IF 32: END-FOR 33: RETURN the last PIS in B; 34: ELSE 35: FOR EACH B_word DO 36: IF (IK is the same as B.Word) THEN 37: RETURN B.PIS; 38: END-IF 39: END-FOR 40: RETURN null; 41: END-IF /* end of two-level index structure bucket process */ IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 4

IV. IMPLIMENTATION AND RESULTS A. Data Broadcasting: Server to Client Fig.6. inverted index list The Fig.6. shows inverted index list.it is created with the help of documents which are broadcasted to the mobile clients using the broadcast stream. At server side broadcasting can be done by sending inverted index and data as a stream. C. Mobile nodes: Client side Fig.5. documents broadcast The Fig.5. shows broadcasting of documents from server side to mobile clients. Broadcasting can be done in two ways i.e., with index and without index. In the case of with index method broadcasting can be done with the help of inverted index and index tree. Whereas in the case of without index method broadcasting can be done by sending only documents. And here α and β values should give and then broadcasting will be done. After broadcasting the documents and details regarding total number of documents, document size, distinct words, word size and tuple address size will be displayed on the screen. In this system broadcast stream contains 50 documents. The extracted words are dynamically generated to make an inverted list. We can broadcast more than 100 documents in a stream depending upon our system processor speed. Fig.7. Entering nodes Fig.7. shows that entering of mobile nodes at client side by giving the value in the label called enter total nodes we can assign number of nodes or clients. By clicking submit button the nodes will create according to whatever we have given in that field. D. Wireless Broadcast: Client side B. Inverted List: (1, α) method IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 5

Fig.8. nodes and searching for string The Fig.8. Shows that nodes are created and by choosing the node out of many nodes searching can be done at that node and particular document related to query string will be download at the client side. Fig.10. Access time and tuning time in (1, 2(1, 1)) method The Fig.10. Shows that access time and tuning time in (1, 2(1, 1)) method here access time = 6469659 ns and tuning time =852030 ns.by comparing fig 4.9 and 4.10 tuning time is reduced in (1, α (1, β)) method rather than in (1, α) method. E. Inverted List Method: (1, 2) method Fig.9. Access time and tuning time in (1, 2) method Fig.9. shows that access time and tuning time when α=2. That means replication of index can be done two times. Therefore with respect to the different values of α access time and tuning time are varies. In particular α increases tuning time decreases. V. CONCLUSION AND FUTUREWORK In Wireless environments data broadcasting is widely used for information delivery services due to its beneficial characteristics such as bandwidth efficiency, energy efficiency and scalability. In this paper a novel indexing scheme which is energy and latency efficient for text retrieval queries on the wireless broadcast data stream has been implemented. The Text retrieval on the disk storage is different from wireless one. First, a simple, inverted list-style index method is constructed. In order to reduce the tuning time overhead (i.e. Energy waste) caused by the sequential scan of the inverted list, an additional level of index structure, which is the index tree for the inverted list is added. For both methods, their extended versions have been devised, where in the inverted list and the index tree are replicated: (1, α) and (1, α (1, β)) methods. Access time and Tuning time was calculated in both in (1, α) method and (1,α(1,β)) method for different α and β value, the result was found as tuning time was less in (1,α(1,β)) method compare to (1, α) method. In (1, α) method when α was increasing access time was decreasing. Therefore by using two level index structures (inverted index list and tree index) Full Text search operation can be done faster compare to single level index structure (inverted index list). F. Inverted + Index Tree Method: (1, 2(1, 1)) IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 6

There is a scope for developing of different types of indexing methods for full text search applications in wireless environment which can be more efficient. ACKNOWLEDGMENT I would like to express my sincere gratitude and indebtedness to my Guide Mr. Vijaykumar Mantri, Associate Professor for his valuable guidance, suggestions, and keen personal interest throughout the course of this paper. I would like to take this opportunity to thank my family and friends for their support throughout this work. I also sincerely acknowledge and thank all those who gave directly or indirectly their support in completion of this work. REFERENCES [1] S. Acharya, R. Alonso, M. Franklin, and S. Zdonik, Broadcast Disks: Data Management for Asymmetric Communication Environments, Proc. ACM SIGMOD Conf., pp. 199-210, 1995. [2] M.S. Chen, K.-L. Wu, and P.S. Yu, Optimizing Index Allocation for Sequential Data Broadcasting in Wireless Mobile Computing, IEEE Trans. Knowledge and Data Eng., vol. 15, no. 1, pp. 161-173, Jan./Feb. 2003. [3] Y.D. Chung and M.H. Kim, An Index Replication Scheme for Wireless Data Broadcasting, J. Systems and Software, vol. 51, no. 3, pp. 191-199, 2000. [4] A. Silberschatz, H.F. Korth, and S. Sudarshan, Database System Concepts, fifth ed. McGraw Hill, 2006. [5] M.S. Chen, P.S. Yu, and K.-L. Wu, Indexed Sequential DataBroadcasting in Wireless Mobile Computing, Proc. IEEE Int lconf. Distributed Computing Systems (ICDCS), pp. 124-131, 1997. [6] T. Imielinski, S. Viswanathan, and B.R. Badrinath, Data on Air: Organization and Access, IEEE Trans. Knowledge and Data Eng.,vol. 9, no. 3, pp. 353-372, June 1997. [7] K.L Tan and B.C. Ooi. Batch Scheduling for Demand-driven Servers in Wireless Environment. [8] Y.D. Chung, An Indexing Scheme for Energy-Efficient Processingof Content-Based Retrieval Queries on a Wireless Data Stream, Information Sciences, vol. 177, no. 2, pp. 525-542, 2007. [9] Q. Hu, W.-C. Lee, and D.L. Lee, A Hybrid Index Technique for Power Efficient Data Broadcast, Distributed and Parallel Databases, vol. 9, no. 2, pp. 151-177, 2001 [10] Y. Yao, X. Tang, E.-P. Lim, and A. Sun, An Energy-Efficient and Access Latency Optimized Indexing Scheme for Wireless Data Broadcast, IEEE Trans. Knowledge and Data Eng., vol. 18, no. 8, pp. 1111-1124, Aug. 2006. IJCSIET-ISSUE3-VOLUME3-SERIES2 Page 7