Fault-Tolerant Data Collection in Fault-Tolerant Data Collection in Heterogeneous Intelligent Monitoring Networks Networks Heterogeneous Intelligent Monitoring Jing Deng Department of Computer Science University of North Carolina at Greensboro jing.deng@uncg.edu http://www.uncg.edu/~j_deng/ Joint work with Profs. Meikang Qiu and Gang Wu 1
Wireless Networks • Networks formed by wireless devices – All communications are sent through wireless channels. – Wireless devices with limited resource Battery energy, memory space, computation power • Many interesting problems: – How to lower communication/computation cost for network activities? Communication takes time/energy. Computation requires memory space and energy. – How to protect systems from node failure? Small wireless devices could easily fail or run out of battery. 2
Failure Models • Fail-stop – Device simply stops working. – No information will be sent or received. – Similarly to one with dead-battery • Byzantine failure – Device can virtually do anything that it is capable of • Dropping packets from others • Sending out fabricate packets • Modifying packets from other nodes • Deviating from communication protocols – Much more difficult to address We will use the fail-stop model 3
Intelligent Monitoring Networks (IMNs) • Wireless sensor networks – Networks with (possibly numerous) wireless micro-sensors • A special type of wireless sensor networks – Likely to be deployed for building structure monitoring, forest monitoring, levee monitoring, industrial plant monitoring, etc. • Two key characteristics – Node failures expected – Heterogeneous architecture • Mostly small devices to collect/report data • Some larger and more powerful devices to process/fusion data • These power nodes send results to observer (data sink). 4
Illustration of Large Wireless Networks 5
Low-power Low-Cost Devices • Devices usually use low-power transceivers – Goal: to lower energy consumption and to extend lifetime • Forming multi-hop communication topology – Relying on other devices’ help to deliver data • Interference can easily disrupt communication – Network topology changes – Data collection paths change – Data loss 6
BitTorrrent - P2P File Sharing Technique • Swarm: collection of nodes with the file (even partially). • Tracker provides swarm information • Client downloads pieces from nodes in swarm. • At the same time, uploading pieces to other nodes • Finished clients serve as seeds (upload only) Even when some of the nodes in the swarm fail (or left), file sharing continues. 7
BitTorrent Strategies • Two strategies in BitTorrent make it surprisingly efficient – Optimistic un-choking – Rarest-first • Optimistic un-choking is the strategy to choose peers to download pieces – Suppose there are 100 peers (with ever-changing D/L speed). – Which of these 100 peers should the client choose? • Using all of them is impractical. • Choosing the top N peers w.r.t. download speed (N=5) • However, there might be new peers offering higher speed. • -> dropping one of the current N peers and randomly testing another peer (un-choke one of the unselected peers) – Benefits • Utilizing most of the peers with highest D/L speeds. 8
BitTorrent Strategies (Cont’d) • Rarest-first strategy governs how to choose pieces for download – Suppose a peer has M of the pieces – Which of these pieces should the client choose? • Random selection or sequential selections? • -> Always choose the rarest piece among all peers (requiring piece information from other peers). • So that this piece can be offered to other peers. – Benefits • Increases piece redundancy • Maintaining torrent health • Improves chance of successful download 9
IMN and BitTorrent? • Data collection in IMNs shares striking similarities with P2P file sharing IMN P2P File Sharing Observer (data sink) tries to A client tries to download a full collect data from monitoring set of pieces from a swarm of nodes, which generate the data nodes Monitoring data redundancy Redundancy of file pieces among among different nodes peers Peers may go offline without Nodes may fail at any time. warning 10
IMN - Connectivity Overview • Lines connect nodes who can hear each other (N=100). • Darker squares mark more powerful nodes (M=10). • Result of random node placement 11
Fault Tolerant Data Collection • Powerful nodes collect data from regular nodes – Announcements are made from the powerful nodes. – Multiple trees are formed with data forwarding nodes. • Usually data forwarding nodes only need to forward data from nodes on their own tree – In order to provide fault tolerance, they will choose α of other overheard transmissions – α is termed support ratio 12
Illustration of Data Collection • Multi-level data collection • We show the [avg, max] record on the powerful nodes • α =0.4 • Some nodes fail (marked with red x) • Big red dot represents a fire burning • None of the powerful nodes sees any temperature anomaly. 13
Illustration of Data Collection (Cont’d) • The same topology and data collection trees. • α =0.4 • With the same failed nodes, two powerful nodes receive the temperature anomaly (N max =2). 14
Performance Results - Reading Abnormal Temp. • Similar simulations were run and average N max computed • p e is node failure probability • N max lowers as p e increases. • With larger α , N max increases. 15
Data Loss due to Failed Sensors • Failed nodes lead to data loss • Support ratio α can dramatically reduce data loss. 16
Conclusions • Wireless monitoring networks can provide robust environment monitoring. • We have proposed a fault-tolerance data collection technique for IMNs: – Multiple multi-level data collection trees (forest) – Data forwarding nodes process overheard data. – Support ratio α • Benefits of our scheme have been demonstrated – Low cost – Fault tolerant toward node failures 17
Future Work • Byzantine failure model – Instead of failed delivery, failed nodes may send wrong data! • Investigating our scheme under different data processing algorithms – Average – Maximum – Minimum – Counting • Analyze data loss for different support ratios 18
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