Data Collection and Dissemination Learning Objectives Understand Trickle

Data Collection and Dissemination

Learning Objectives • Understand Trickle – an data dissemination protocol for WSNs • Understand data collection protocols in lowduty-cycled WSNs with unreliable links

Prerequisites • Module 5 • Basic concepts of Computer Networks

Outline • Data Dissemination – Estrablishing eventual consistency on a shared variable – Trickle – Address single packet • Data Collection – DSF

Data Dissemination - Trickle [Dissemination_1] 5

Simple Broadcast Retransmission • Broadcast Storm Problem – Redundant rebroadcasts – Severe contention – Collision

Trickle • Motivation – WSNs require network code propagation • Challenges – WSNs exhibit highly transient loss patterns, susceptible to environmental changes – WSNs network membership is not static – Motes must periodically communicate to learn when there is new code • Periodical metadata exchange is costly

Trickle Requirement • Low Maintenance • Rapid Propagation • Scalability

Trickle • An algorithm for code propagation and maintenance in WSNs • Based on “Polite Gossip” – Each node only gossip about new things that it has heard from its neighbors, but it won’t repeat gossip it has already heard, as that would be rude • Code updates “trickle” through the network

Trickle • Within a node time period – If a node hears older metadata, it broadcasts the new data – If a node hears newer metadata, it broadcasts its own metadata (which will cause other nodes to send the new code) – If a node hears the same metadata, it increases a counter • If a threshold is reached, the node does not transmit its metadata • Otherwise, it transmits metadata

Trickle – Main Parameters • Counter c: Count how many times identical metadata has been heard • k: threshold to determine how many times identical metadata must be heard before suppressing transmission of a node’s metadata • t: the time at which a node will transmit its metadata. t is in the range of [0, τ]

Trickle Maintenance – One Example • Assume – No packet loss – Perfect interval synchronization • How to relax these assumptions? [Dissemination_1]: Figure 3 12

Trickle – Impact of Packet Loss Rates

Trickle Maintenance without Synchronization – Short Listen Problem • Mote B selects a small t on each of its three intervals – Although other motes transmit, mote B’s transmissions are never suppressed • The number of transmissions per intervals increases significantly [Dissemination_1]: Figure 5 14

Trickle – Impact of Short Listen Problem

Solution to Short Listen Problem • Instead of picking a t in the range [0, τ], t is selected in the range [τ/2, τ]

Propagation • Tradeoff between different values of τ – A large τ • Low communication overhead • Slowly propagates information – A small τ • High communication overhead • Propagate more quickly • How to improve? – Dynamically adjust τ • Lower Bound τl • Upper Bound τh [Dissemination_1]: Section 5 17

Trickle Complete Algorithm

Data Collection

Data Collection • Link-Quality based Data Forwarding – Wireless communication links are extremely unreliable – ETX: to find high-throughput paths on multiple • Sleep-Latency Based Forwarding – Duty Cycling: sensor nodes turn off their radios when not needed • Idle listening waste much energy [Collection_2] 20

Sleep Latency in Low Duty-Cycle Sensor Networks Sleep now. Wake up in 57 seconds Sleep now. Wake up in 35 seconds D B 35 s latency C 57 s latency A 13 s latency 4 s latency E Sleep now. Wake up in 4 seconds Sleep now. Wake up in 13 seconds [Collection_2] 21

Unreliable Radio Links D B 70% 90% C A 95% 50% E

State-of-the-art Solutions: ETX = 1/0. 5 + 1/0. 5 = 4 B 50%, 100 s ETX only considers link quality 50%, 100 s Expected E 2 E qualitydelay based is 400 s solutions Sole link cannot help Areduce E 2 E delay in extremely low-duty cycle D Expected E 2 E sensor networks! delay is 50 s 40%, 10 s C ETX = 1/0. 4 + 1/0. 4 = 5

State-of-the-art Solutions: DESS = 10 + 10 = 20 s B DESS only considers sleep latency 10%, 10 s Expected E 2 E is 200 s solutions latencydelay based Sole sleep cannot help D A reduce E 2 E delay. Expected in extremely low-duty cycle E 2 E sensor networks!delay is 40 s 100%, 20 s C DESS = 20 + 20 = 40 s

End-to-End Delay vs. Duty Cycle • Suppose one fixed forwarding node – Suffer excessive delivery delays when waiting for the fixed receiver to wake up again if the ongoing packet transmission fails

End-To-End Delay vs. Average Link Quality • Given bad link quality, the end to end delay increases dramatically

Sensor States Representation • Scheduling Bits – (10110101)* • Switching Rate – 0. 5 HZ 16 s round time 10110101 Off On

Data Delivery Process (1 0 0 0 0 0)* (0 1 0 0 0 0)* 1 Sleep latency is 1 (0 0 0 1 0 0 0)* 2 Sleep latency is 2 3 (0 0 0 1 0 0 0)* 4 Sleep latency is 3 End to End (E 2 E) Delay is 6

Main Idea Sleep latency is 1 We should try a sequence of forwarding nodes (0 1 0 0 0 0)* (0 0 0 1 0 0 0)* instead of a fixed forwarding node! (1 0 0 0 0 0)* 1 2 3 4 (0 0 1 0 0 0 0)* Dynamic Switching-based Forwarding (DSF) is important 5 1 st attempt: Sleep latency is 1 in extremely low duty-cycle sensor networks. nd ith attempt: Sleep latency is 1 + 10 * (i-1) 2 2 nd attempt: Sleep latency is 1 + 1 =2 attempt: Sleep latency is 1 + 10 =11

Optimization Objectives • EDR: Expected Delivery Ratio • EED: Expected End-to-End Delay • EEC: Expected Energy Consumption

Optimization Objectives(1) : EDR Forwarding Sequence (1000)* 1 2 (0100)* EDR = 70% 3 (0010)* EDR = 80% EDR: Expected Delivery Ratio. 60% 50% 40% EDR for node 1 is (EDR 1): 0. 6*0. 7 + (1 -0. 6)*0. 5*0. 8 + (1 -0. 6)*(1 -0. 5)*0. 4*0. 9=0. 652 4 (0001)* EDR = 90% See Equation (3)

Optimization Objectives(2) : EED Forwarding Sequence (100000)* 1 60% , 2 50% , 3 40% , 5 2 (001000)* EDR = 70%, EED = 10 3 (000010)* EDR = 80%, EED = 12 4 (000001)* EDR = 90%, EED = 9 EED: Expected E 2 E Delay. EED for node 1 is (EED): 0. 6*1/0. 7 * (2 + 10) + (1 -0. 6) * 0. 5 * 1 /0. 8 * (3 + 12) + (10. 6)*(1 -0. 5)*0. 4*1/0. 9 * (5 + 9) See Equation (4)

Optimizing EDR Shall we try all available neighbors? (100)* 2 100% 1 (010)* EDR = 70% If both node 2 and node 3 are selected as forwarding nodes: We should only choose EDR 1 a=subset 1 * 0. 7 =of 0. 7 neighboring nodes as forwarding nodes! 100% 3 (001)* EDR = 80% If only node 3 is selected as forwarding node: EDR 1 = 1 * 0. 8 = 0. 8

Optimizing EDR with dynamic programming Try or skip (100)* 1 2 60% 50% (010)* EDR = 70% Try or skip 3 40% Node 4 has to be selected (001)* EDR = 80% Try or drop 4 Select only a subset of neighbors as forwarders (100)* EDR = 90% Then we attempt to add more nodes into the forwarding sequence backwardly.

Distributed Implementation • EDRb(Ø) = 1 – The sink node has no packet loss • EEDb(Ø) = 0 – The sink node has no delay • EECb(Ø) = 0 – The sink node has no energy consumption

Distributed Implementation EDR = 99%, EED = 15, EEC = 2 EDR = 98%, EED = 2, EEC = 1 1 3 EDR = 100%, EED = 0, EEC = 0 sink 2 EDR = 97%, EED = 20, EEC = 5 4 EDR = 90%, EED = 90, EEC = 12

Complete Protocol Implementation at Node e

Assignment • 1. Please give one example to illustrate what the short -listen problem in the context of Trickle algorithms is. • 2. How does Trickle algorithms solve the short-listen problem? • 3. Why do not we use a constant τ in Trickle algorithm? • 4. Why do we need to consider both link quality and the low-duty-cycle nature of wireless sensor networks when we design suitable applications?

Project • This is a group project. Each group can have up to 3 students • Project Description • In this project, you will develop a multi-hop data collection tree protocol based on Tiny. OS 2. x. 1. Development of a multi-hop data collection tree protocol 1. 1 The protocol to form a multi-hop data collection tree The base station locally broadcast a tree construction message, which includes its own ID and its depth to be 0; a. When a node, say A, receives a tree construction message from node B at its first time (i. e. , node A has not joined the data collection tree yet), node A assigns its depth to be the depth of node B plus one, and its parent to be node B. After this, node A rebroadcasts the tree construction message. 40

Project - continue b. When a node, say A, already joins the data collection tree and receives a tree construction message from node B, node A just simply disregards the tree construction message. c. When a node, say A, already joins the data collection tree (suppose that node A’s parent is node B and node A’s depth is n) and receives a tree construction message from node C: 1. suppose that if node A selects node C as its parent, node A’s depth is m; 2. suppose m < n; node A will change its parent to node C. 41

Project - continue one example multi-hop data collection tree 42

Project - continue • Also see attached slide tree. ppt for a dynamic view about how to construct a multi-hop data collection tree. 1. 2 After the multi-hop data collection tree is formed, each node senses and transmits light intensity to the base station every one second. For each received message, the base station displays the following information: – Node ID which originates the message; – Tree depth of the Node; – Sensed light value 43

Project - continue • Basic Steps: Please follow the steps listed below: 1. Setting up Tiny. OS environment Xubun. TOS can be downloaded here: http: //toilers. mines. edu/Public/Xubun. TOS 2. Go through the Tiny. OS tutorials at http: //docs. tinyos. net/index. php/Tiny. OS_Tutor ials 44