Enomoto, M., Shibata, N., Yasumoto, K., Ito, M. and Higashino, T.: A demand-oriented information retrieval method on MANET, International Workshop on Future Mobile and Ubiquitous Information Technologies (FMUIT'06). http://ito-lab.naist.jp/themes/pdffiles/060510.makoto-e.fmuit06.pdf In urban areas including shopping malls and stations with many people, it is important to utilize various information which those people have obtained. In this paper, we propose a method for information registration and retrieval in MANET which achieves small communication cost and short response time. In our method, we divide the whole application field into multiple sub-areas and classify records into several categories so that mobile terminals in an area holds records with a category. Each area is associated with a category so that the number of queries for the category becomes the largest in the area. Thus, mobile users search records with a certain category by sending a query to nodes in the particular area using existing protocol such as LBM (Location-Based Multicast). Through simulations supposing actual urban area near Osaka station, we have confirmed that our method achieves practical communication cost and performance for information retrieval in MANET.

1. A Demand-Oriented Information Retrieval Method on MANET Makoto ENOMOTO ， Naoki SHIBATA † ， Keiichi YASUMOTO ， Minoru ITO ， Teruo HIGASHINO † † Graduate School of Information Science, Nara Institute of Science and Technology † Department of Information Processing and Management, Shiga University † † Graduate School of Information Science and Technology, Osaka University Presenter: Makoto ENOMOTO

6. User’s perspective （ when search ） Searching Example ： Time ＝ 19:00-20:00, Location ＝ less than 500m from Osaka Station, Category ＝ Japanese Style Restaurant Searching record Preserved record

10. Proposed Method STEP1: Classify records into classes Record: ID ， timestamp ， location ， main data(text ， images),keyword, [prices ，… ] All records are divided into multiple sets Class1: On Sale Class2: Event Class3: Train Delay

11. Proposed Method STEP2: whole application field is divided into sub-regions (hereafter area s) A B C 1 2 3 A1 B1 A2 A3 B2 B3 C1 C2 C3

12. Proposed Method STEP3:All nodes count the number of matched queries for each class Demand ; measured by the count of query sent to the area Count Class 10 Train 11 Event 23 On Sale Count Class 17 Train 7 Event 4 On Sale Count Class 21 Train 86 Event 70 On Sale Count Class 1 Train 11 Event 5 On Sale Count Class 3 Train 10 Event 8 On Sale Count Class 32 Train 119 Event 54 On Sale Count Class 16 Train 11 Event 32 On Sale Count Class 9 Train 11 Event 66 On Sale Count Class 23 Train 150 Event 68 On Sale

13. Proposed Method STEP4:Find the most demanding area A1’s query count table (i) Area ‘A1’ flood the its query count table (ii) Area ‘A2’ receive the table (iii) Area ‘A2’ compares entries of the table and its own table, and updates the table so that each table entry has higher value between two tables and flood again A2’s query count table 3 10 8 Count Train Event On Sale Class A1 A1 A1 Area Area Count Class 3 11 8 Train Event On Sale A1 A2 A1 Area Count Class 1 11 5 Train Event On Sale A2 A2 A2 C3 B3 A3 C2 B2 A2 C1 B1 A1

14. Proposed Method STEP5:Replicate records in the most demanding area A B C 1 2 3 Data sending node Example 1.registering a record which ClassID = 3 2.most demanding area of the Class is ‘C1’ 3.Sending registering message to ‘C1’

15. Proposed Method STEP6:Send query to the most demanding area A B C 1 2 3 Query sending node Example A node in ‘A2’ sends a query to ‘C1’

17. Experiment - implemention Protocols Flooding used when sending a query or a record to nodes in a particular area used when a message is sent to the entire ad-hoc network Location-Based Multicast (LBM)

18. Experimental Result – Average Response Time

19. Experimental Result – Number of Messages

20. Experimental Result – Reply Loss