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Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks. Kai-Wei Fan Sha Liu Prasun Sinha Arun Sudhir. Agenda. Background & Related Work The proposed protocol Performance analysis 7 Evaluation Large-scale simulation using ns2 Conclusion. - PowerPoint PPT Presentation
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Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks
Kai-Wei Fan Sha Liu Prasun Sinha
Arun Sudhir
Agenda
Background & Related Work The proposed protocol Performance analysis 7 Evaluation Large-scale simulation using ns2 Conclusion
Background & Related Work
Data Aggregation
Active research area in sensor networks. Why? Raw data from sensors has an inherent
redundancy Aggregation reduces this redundancy by
forwarding only the extracted information. Thus, it reduces communication cost and
energy.
Data Aggregation Techniques
Can be structured or unstructured. What to use depends on the nature of the
application – data gathering or event-based.
Structured or Unstructured ?
Data gathering applications call for structured approach. ( why ? )
Data gathering – low traffic, low maintenance overhead.
Example: environment monitoring Event-based applications call for unstructured
approach. (why? ) Event-based – source nodes change
dynamically. Example: intrusion detection, hazard detection.
Goal of the proposed protocol
Focused on event-based applications. Design goals are:
Scalability Low maintenance overhead
Semistructured approach Design challenge: determine a scalable packet
forwarding strategy for early aggregation.
Keep these in mind
Good spatial and temporal convergence of packets is key for aggregation.
Spatial – all packets at the same place
Temporal – at the same time too.
DAA – Data Aware Anycast - first structureless protocol which improves spatial and temporal aggregation.
Anycast – A routing scheme where a packet is forwarded to the best or any of a group of destinations based on some metrics.
ToD – Tree on DAG (explained later)
Skip the next two slides if you know what a graph, spanning tree, DAG and stretch is.
Some (simple) Graph Theory
Graph (greyed) Spanning Tree (solid black) Shortest Path Spanning Tree Dijkstra's Algorithm Stretch = XY in Tree / XY on the Graph.
Low stretch => Spanning Tree is good
DAG – Directed Acyclic Graph
Edges have directions
No cycles
Graph with spanning tree
DAG
Some (simple) Graph Theory Shortest path spanning tree provides a path from its root to any other
node.
But, it may provide longer paths for other pairs of nodes compared to the original graph.
So how do we know if the spanning tree we have is good to follow for going for any X-Y path?
One answer is stretch
The maximum or average stretch can serve as a metric
A tree minimising the max stretch is minimum max stretch tree (MMST)
A tree minimising the avg stretch is minimum max stretch tree (MAST)
Related Work Structured approaches focus on effective tree construction techniques.
Like Steiner Minimum Tree or Multiple Shared Tree
These are useful ONLY IF source is known in advance. (not for event-based)
Also suffer from the long stretch problem.
DCTC: A structure-based protocol for event-based applications
dynamically forms a tree with the event source as root and acheives good aggregation.
Has heavy message exchanges: tree creation and maintenance takes up upto 33% of data collection!
DAA : Structureless
Aggregation without tree overhead with good spatial and temporal aggregation.
Forwards packets to one-hop neighbours and aggregates well at source
No guarantee that all packets are aggregated
Cost of forwarding non-aggregated packets limits scalability
A closer look at DAA
Spatial convergence: Uses anycast. In wireless radio transmission, nodes can tell if they have packets to be aggregated with the sender's packet.
Temporal : Randomized waiting is employed and a node just waits a random amount of time before transmitting the packet.
If a node has no neighbors with packets for aggregation, it simply forwards its packet towards the sink using geographical routing.
This can have a higher overhead if there are many unaggregated packets and if the distance from the source to sink is large.
The Proposed Protocol:Dynamic Forwarding over ToD
Proposed Protocol- Dynamic Forwarding over ToD
Two phases:
DAA
Dynamic Forwarding
DAA phase : Packets are forwarded, aggregated using DAA
Dynamic Forwarding phase: Unaggregated packets in DAA phase are now dynamically forwarded using a structure ToD (Tree on DAG) and NOT routed to sink directly using geographic routing thus decreasing overhead compared to DAA.
Why ToD ?
Using a fixed tree will have the long stretch problem
Using a dynamic tree (DCTC) has high message exchange overhead
ToD is an implicit structure over which Dynamic Forwarding is done.
ToD in a one-dimensional network
F-Tree: F-cells -> F-aggregators F-Cluster : A pair of F-cells
S-Tree: S-cells -> S-aggregators S-Cluster : A pair of S-cells
F-Tree overlapped with S-Tree : A DAG christened as ToD
How aggregation works here DAA is first used to aggregate as many packets as possible.
DAA
Dynamic Forwarding
Nodes then forward their packets to their F-aggregators for aggregation.
If an event only triggers nodes within an F-cluster, the packets travel up the F-Tree to the sink. eg: (A,B) -> F1
If nodes of adjacent F-clusters are involved, the F-aggregator then forwards packets to the S-aggregator. eg: (C,D) -> (F4,F5) -> S4
Thus aggregation involves one or at most two steps in a single dimension
ToD in a two-dimensional network
F-Tree: F-cells -> F-aggregators F-Cluster : 4 F-cells
S-Tree: S-cells -> S-aggregators S-Cluster : 4 S-cells
A,B,C,D.. -> F-clusters
F-Tree overlapped with S-Tree : ToD
How aggregation works here DAA is first used to aggregate as many packets as possible.
DAA
Dynamic Forwarding
Nodes then forward their packets to their F-aggregators for aggregation.
Case 1: If an event only triggers nodes within an F-cluster, the packets travel up the F-Tree to the sink. eg: (C1,C2) -> X
Case 2: If nodes of adjacent F-clusters are involved, the F-aggregator then forwards packets to the S-aggregators.
Thus aggregation involves one or at most three steps in a single dimension
Case 2:(C1,C2) ->X, C3 ->YX -> S1 Y -> S2S1 -> S2
Clustering & Aggregator selection Principle: The size of a cell should be greater than or equal to the
maximum size of the event.
Any clustering method would work (hexagonal, triangular)
Why choose grid ?
Size of grid can be parametrized as a grid parameter easily
The cell, F-cluster and S-cluster can be determined from geographic location easily. (assumption: nodes have a GPS)
Clustering & Aggregator selection
Nodes take turns to play aggregator to evenly distribute energy cost as aggregator emans extra energy consumption.
A good metric for aggregator election can be residual energy. Nodes elect themselves as aggregator and then advertise to all other nodes in F-cluster.
In case of tie, node ID is used.
Alternative approach is to hash time in days or hours (why?)
Aggregator change frequqncy is very low
hash(current time) = k , 1 <=k <= n where n= number of nodes in cluster.
Then, node k is elected as aggregator (read cluster-head)
Clustering & Aggregator selection
Choosing F-aggregators and then doing the same process for electing S-aggregators involves extra overhead.
The solution is the concept of Aggregating Cluster
The Aggregating Cluster of an S-cluster is that F-cluster which is closest to the sink among all F-clusters that the S-cluster overlaps with.
IMPORTANT: If an F-aggregator needs to forward packets to two S-aggregators, it forwards it to the F-cluster closer to itself (might be itself!) as the aggregating cluster for the first S-aggregator.
Clustering & Aggregator selection Benefits of using aggregating clusters for S-aggregators
No leader election for S-clusters (additional overhead)
Scalable since nodes only need to know F-aggregators
Change in F-aggregator need not be propagated to other F-clusters
Hashing function for leader selction easier to use
No overhead for computing aggregating clusters (static)
Avoiding Voids
In a real-world scenario, not all regions will have sensors
Uncovered regions are called voids Case 1: Only one aggregating cluster.
Dark Grey : void F-cluster Light grey: cell containing data
Avoiding Voids
Case 2: Two aggregating clusters – nearer one is a void.
Avoiding Voids
Case 3: Two aggregating clusters – farther one in void.
Avoiding Voids Solution to Case 1 & 3: If the first S-aggregator is in a void,
forward to the top-right F-cluster from that void. (figure a )
What if that also is a void ? (figure b)
Try forwarding to other near F-clusters Or forward directly to sink (F-tree) (Solution for case2 too! )
Performance Analysis & Evaluation
Analysis of the worst case
Worst case distance = (How?)
Performance Evaluation Involved comparative evaluation of:
Dynamic Frwarding over ToD
DAA (Structureless)
SPT (opportunistic, structural)
SPT-D (SPT with a fixed wait time before forwarding)
The test bed was:
Comprised of Kansei Sensors
105 Mica2 based motes each hooked to a Stargate
Stargate is 32-bit CrossBow device running Linux
All stargates connected via wired ethernet
Transmission power was such that each node could have maximum 12 neighbours
Anycast MAC Protocol on top of Mica MAC layer
Had only two F-clusters in ToD, a cell had 9 nodes
Normalized number of transmissions
ToD has minimum number of transmissions even when event size > cell size
Large-scale simulation using ns2
Evaluation using Simulation Involved comparative evaluation of:
Dynamic Forwarding over ToD
DAA (Structureless)
SPT (opportunistic)
OPT (Optimal Aggregation tree)
The simulation was run on:
A 2000 X 1200 grid network with 35 m node separation
1,938 nodes in the network
Data Rate of 38.4 Kbps
Transmission range of a node slightly > 50 m
Event moves at 10 m/s for 400 seconds using the random waypoint mobility model
Event size is 400m diameter and sink was a t (0,0)
Perfect aggregation was the aggregation function under evaluation
Event Size
ToD better than DAA and SPT but OPT performs best in fig 1
But its overhead was ignored
In 2 and 3, DAA and ToD have better aggregation and hence better performance.
Scalability
In 1 and 2, ToD and OPT are steady but SPT and DAA dont scale well
In 3, the number of packets ar not equal to 1, maybe due to protocol-imposed delay.
Also, ToD has more packets if the event is nearer to sink because then sink is used a F-aggregator.
Aggregation Ratio
As aggregation ratio decreases, packet size increases and soon reaches payload limit.
OPT had high drop rate. So DAA and TOD are better than OPT.
Cell Size
ToD downgrades to DAA for extremely small and large cell sizes
ToD peroformance clearly has an optimum cell size.
Conclusion
ToD: Semistructured : structurelss with Dynnamic
Forwarding over a ToD Scalable to a very higher extent than DAA Avoids the long stretch problem with structured
approaches Suited for extended life sensor networks
Thank You!
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