Academic Insights: Data Management Techniques and Tools in Mobile Environments

1. Weekly Report Brett Geren

2. Academic Notes • Several sites provide methods for the acquisition of academic papers: • Google Scholar • CiteSeer • ACM Digital Library (ACM Portal) • Online MST Library (http://library.mst.edu/) • LaTex provides a bibliography engine called BibTex. It’s pretty sweet (so says Dylan) • CiteULike can be used to auto generate bibliography stuff.

3. TinyOS • TinyOS.net is the “go-to-guy” • Uses NesC, C for networked, embedded systems • Non-blocking and doesn’t preempt like larger OS’s • Normally blocking operations are done asynchronously through callback events • Tasks are provided to allow preemptable, long-running background tasks

4. Data Compression • Data compression allows us to minimize the amount of data sent, a costly endeavor • Methods: • Pipelined In-Network – chop off shared data • LZW/LSZW – replace character sequences with indices into a dictionary • GAMPS – group like signals, possibly scaling some data • Delta Huffman Compression – express data as changes from a previous values; compress using Huffman coding

5. Data Management in Mobile Environments • Highly reminiscent of distributed file systems or databases • Good database systems follow the ACID properties: • Atomicity • Consistency • Isolation • Durability • How do we manage data on many mobile devices without violating any ACID properties? • Inconsistent state across devices • Isolating changes is difficult

6. Data Management in Mobile Environments (Cont.) • Consistency Solutions: • Two Tier Replication • Tentative local update • Lazy update primary • Pitoura’s Method • Cluster “strongly connected” devices, nominate a leader • Intra-cluster requires mutual consistency • Group leaders store system-wide values (core copies) • Inter-cluster allows inconsistency • Group followers can have conditionally committed data (quasi copies)

7. MANET • Mobile Ad-Hoc Networks (MANET’s) play crucial roles in many scenarios (such as disaster relief) • How do we effectively distribute needed data: • SAF – every MH replicates highly sought after data • DAFN – neighbors take care not to have duplicate data • DCG – “stable” groups are formed, groups do not have duplicate data • Consistency: • Establish quorums – every two quorums must share at least one common node • Updated data is routed through these common nodes

8. Dynamic Social Grouping • We are interested in dynamically grouping nodes for routing and other purposes • Bubble-rap • Form cliques with “full contact” nodes • Merge sufficiently close groups • Socialcast Model • Calculate utility value based on your “closeness” to a group. • Probabilistic Routing • Successful deliveries increase probability • Timeouts decrease probability

9. End Notes…. • Torvalds has an ‘s’ in it • VIM is pretty cool • Questions?