Related Work

1. Related Work • 2 major areas: • Controlled, density aware flooding algorithms for wireless and multicast networks • Epidemic and gossiping algorithms for data consistency in distributive systems • Both assume cheap communication, end-to-end transport • Network broadcasts: • Prior works: want to deliver a piece of data to as many nodes as possible with a time period • Simple broadcast retransmission leads to broadcast storm problem. • Best effort attempt to send a message to all nodes in a network and stop • Probabilistic broadcasts • Adaptive dissemination

2. Related Work • Ni et al: counter based algorithm to prevent broadcast storm • Operates in a single interval • Insufficient for sensor network code propagation • E.g.: mote rejoins 3 days after the broadcast -> rebroadcast the entire network • Propagating data updates through distributive system • Demers et al: epidemic algorithms for managing replicated databases • PlanetP: epidemic gossiping for a distributive peer-to-peer index • Trickles use techniques drawn from these efforts: only local wireless broadcast

3. Related Work • Gossiping -> SPIN’s three way handshaking protocol • Reijers et. Al : efficient code distribution • Distributes only changes to the current code • Compute and update changes to a code image through memory manipulation • Doesn’t address how to distribute the code and to validate the updates • TinyDB sensor network: • Query system uses epidemic style of code forwarding • Depends on periodic data collection with embedded metadata

4. Discussion and Conclusion • Trickle can: • Quickly propagate new code with a small overhead • Uses a very simple mechanism • Scales logarithmic with density. • Reprogram the entire network in 30s with an overhead of less than 3 packets/h • Ignored policy used to propagate code: Maté broadcasts code routines 2 times

5. Discussion and Conclusion • Limitations: Motes are always on • Long-term deployments have very low duty-cycle • Communication scheduling schemes • Trickle’s scalability stems from randomization and idle listening • Transmission scalability suffers under a CSMA protocol • Low power listening • Extra plus: • Could be used to disseminate any sort of data • Change the propagation scope by adding predicate to summaries