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!