Distributed Resource Management and
Parallel Routing for Data Acquisition in
Heterogeneous Sensor Networks
W. Chen, H. Miao, S. Z. Sabatto, H. A. Adas, K. Suzan
Dr Wei Chen, Professor
College of Engineering, Technology and Computer Science
Center of Excellence for Battlefield Sensor Fusion
Tennessee State University
International Conference on Sensor Networks and Application, 2009
SNA’09 -1
Presentation Outline
 Introduction: Sensor network, Fusion, Resource Allocation
 Problem Statement
 Review of Centralized & Decentralized Market-based
Approach for Resource Allocation
 Proposed Hierarchical Market Approach for Resource
Allocation
 Parallel Routing in Heterogeneous Sensor Networks
 Implementation and Experiment Results
 Future Work
SNA’09 -2
Introduction
Sensor Network & Sensor Fusion
Fusion missions: Target tracks,
target identification, environment
monitoring …
Upper-level fusion
Return back
sensed/fused data
Base
Station
Ask for
data/information
Lower-level fusion
sink
Sensor
Network
SNA’09 -3
Problem Statement
Given a task or tasks, how to assign sensors and
network resources to fulfill the task/tasks with
the goal of less delay, high QoS, and long
network lifetime?
For example, a task of mobile target tracking can be fulfilled
by a sequence of node actions: sampling, listening,
transmitting, aggregation, sleeping, and each action uses
some resources. What action each node should take at each
timeslot to fulfill the task that best matches the above goal?
SNA’09 -4
Problem Statement
Resource Allocation
How to assign the resources for achieving the requested data with
smallest delay while keeping the network alive as long as possible?
SNA’09 -5
Review of Market-Based
Resource Allocation
Centralized Resource Allocation (CRA)
(Dr. T. Mullen and others, Penn State Univ.)
Using an auction mechanism for a
single-platform or single-hop sensor
network
 A winner has to be decided from
resource bids during each round of
scheduling according to the current
status of all resources and requirements
of given tasks.
Base Station
(Clients, Consumers)
Central Sensor manager
Computation intensive
Not suitable to a multi-hop sensor
network, where communication cost
of relaying data are the dominant cost.
Single-platform or one-hop
Sensor Network
SNA’09 -6
Review of Market-Based
Resource Allocation
Decentralized Resource Allocation (DRA)
(G. Mainland & others, Harvard Univ.)
At each timeslot, the IRM at each node locally
selects an action that can maximize the utility
function.
 (a)  price (a)
u (a)  
0
Base Station
(Clients, Consumers)
Infrequently central control
if the action a is available
otherwise
 (a) is the estimation of probabilit y of receiving a payment
Tuning node behavior: when action is “successful,”
the utility function receives a reward. Nodes can
determine locally which actions were “successful”.
Central control: adjusting the price of resource
infrequently
IRM
IRM
IRM
IRM
IRM
IRM
IRM
Sensor Network
No control points, hardly achieving optimal
resource allocation
Overlap on sensing, computation, and networking
Individual
Resource
Manager
SNA’09 -7
Proposed Approach- Framework
Hierarchical Resource Allocation (HRA)
in Cluster-Based Sensor Networks
• Local Resource Manager (LRM) at
cluster-head nodes is local centralized
• Individual Resource Manager (IRM) at
cluster-member nodes is decentralized.
• Simple central control by adjusting the
price of resource infrequently
• Using the routing protocols and
reconfiguration functions of the
underlying cluster-based sensor network
Underlying Network: Most sensor
networks nowadays are built with a
hierarchical structure by clustering
that introduce efficient sensing,
computing and networking, and long
network lifetime.
Base Station
(Clients, Consumers)
Infrequently central control
Cluster head
IRM
Goal:
(1) providing promise solution of resource
allocation for given tasks with less delay
and high QoS; and
(2) extending network lifetime
Cluster
LRM
IRM
LRM
LRM
IRM
IRM
Sensor Network
SNA’09 -9
Proposed Approach –Detail Design
Autonomous Scheduling
1. Rather than static scheduling, individual nodes tune their schedules over time
2. Cluster-heads do local optimization in their clusters
3. Nodes avoid wasting energy by using a payment-possibility threshold.
4. Using the feedback to tune node behavior: nodes receive rewards when they take
useful actions
5. Reinforcement learning to select best actions
Action model at nodes:
1. Nodes select an action among a set of actions at each timeslot
2. Each action has an associated energy cost
3. When an action is “successful,” the node earns a reward
Examples of actions: Sample a sensor, Listen for incoming radio messages,
Transmit a radio message, Aggregate multiple sensor readings into a single value
4. Each node attempts to maximize its reward
5. Taking an action may or may not produce a good of value to the sensor network.
6. The nodes can determine locally whether a given action deserves a payment.
SNA’09 -11
Proposed Approach –Design Details
Algorithm of the IRM at a node r
for each timeslot (scheduling cycle) do
(1) with 1-ε probability select an action a from the
available action set which has largest utility value;
(2) with ε probability randomly select an action
a from the action set //exploring action space to avoid falling to local minima//
(3) if β(a) < payment-possibility threshold
G. Mainland’s algorithm: An
then node r goes to sleep //saving energy//
energy budget is used for each fixed
else
period. Nodes take the actions that
begin
can maximize the utility function
even the profit is very small when he
node r takes action a;
budget allowed.
if action a receives a payment
then β(a) =α+(1- α)β(a) //estimated probability of success gets larger //
else β(a) =(1- α)β(a); //estimated probability of success gets smaller //
end;
(4) if node r runs out of the energy
then call the network reconfiguration functions;
 (a)  price (a)
Utility function u (a)  
0
if the action a is available
otherwise
SNA’09 -12
Proposed Approach –Design Details
Algorithm of the LRM at a cluster-head
for each timeslot (scheduling cycle) do
begin
(1) collect status of each member node in the cluster;
(2)determine the optimal resource allocation according to the current
status in the cluster and the given tasks;
(3) inform the decision to the cluster member nodes;
(4) if the head runs out of the energy
then call the network reconfiguration functions;
end;
Price Selection and Adjustment at the Central Controller
• Prices are propagated to sensor nodes from the GRM through data dissemination
algorithm.
• The client can adjust prices to affect coarse changes in system activity.
Routing Protocols
Broadcast protocol and data gathering protocol for the underlying cluster-based
sensor network are used.
Reconfigurable Function
When a node runs out of battery, the network will be self-reconfigured.
SNA’09 -13
Implementation
Application: Tracking Mobile Targets
Field: 105m×105m
Nodes: 800 MICA2/Crossbow motes
Resource: (1) Radio: member – 15 m, head – 30 m; (2) Magnet sensor: sensing range – 11m;
Buffer: 2 buffers (2256 byte) with totally 14 packages
Sample reading: 29 byte (one buffer can save 17 samples)
Moving target: one or two with speed 1.5 m/s or 3 m/s moving on random straight routes
Packet size: 35 byte (payload 29 byte with header 6 byte)
Data rate: 38.4 kbps
Timeslot for an action: 0.25 second
Initial energy at each node: e = 3.88 J (energy in an Nickel Cadmium AA battery = 4320 J)
MAC protocol: CAMA/CA
Local optimization at LRM: cluster-head select the best radio messages (most accurate message)
when it receives multiple overlap messages from its member nodes
Routing protocols: data dissemination – broadcast protocol by using the backbone tree, message
collection – data gathering protocol which relays data back to the base station from sensor nodes by
using the backbone tree from children to the parent
Energy consumption for actions at each time slot
Action 1: Sending, 2.33 mJ, Action 2: Listening, 6.56 mJ, Action 3: Sampling, 84.1 uJ
Action 4: Aggregation, 84.1 mJ, (Action 5): sleeping, 12 uJ
SNA’09 -14
Experimental Results
Flat Sensor Network
sink
SNA’09 -15
Experimental Results
Cluster-based Sensor Networks
sink
SNA’09 -16
Experimental Results
Latency (one mobile target)
In 20 seconds, DRA received 77 messages, HRA received 119 messages
DRA (Without Local Optimization)
HRA (With Local Optimization)
Test field
Test field
Latency of Messages (One Target, NOPT)
11; 2%
16; 3% 24; 4%
26; 5%
9; 7%
0 - 5 sec
5 - 10 sec
458; 86%
Latency of Messages (One Target, OPT)
2; 1%
46; 39%
0 - 5 sec
19; 16%
5 - 10 sec
10 - 15 sec
10 - 15 sec
15 - 20 sec
15 - 20 sec
>20 sec
>20 sec
45; 37%
SNA’09 -17
Experimental Results
After tracking a mobile target
200 seconds
SNA’09 -18
Experimental Results
Observation: change the price of
sending only may not work well.
SNA’09 -19
Parallel Routing for Heterogeneous
Sensor Networks
Recently deployed sensor networks are increasingly following
heterogeneous designs. For example, a sensor network can include
large number of small MICA sensors with a few of more powerful
Garcia micro robotic nodes.
In order to solve the performance bottleneck for data acquisition
we consider a parallel routing architecture induced from the highend nodes.
SNA’09 -20
Formation of Parallel Routing
Architecture
Heterogeneous Sensor Network: Suppose there are k high-end nodes
and n L-nodes in the heterogeneous sensor network, where k << n.
Formation of the Parallel Routing Architecture
with k H-nodes (PRA(k))
1. For each H-node u, u broadcasts its ID at
different timeslot.
2. For each L-node v, if v receives the IDs from at
least one H-node, it assigns itself to the closest
H-node. If v doesn’t receive the ID from any Hnode, it assigns itself to a group called as temp.
3. Each group forms a cluster-based routing tree
structure with the H-node as its root (for group
temp, the root can be any low-cost node) by
using any existing algorithms.
4. Merge temp to another group.
5. H-nodes (the roots of all groups) form a tree
structure with the sink as the root.
Each group forwards data to the Hnode using a cluster-based routing
tree; the H-nodes forward data back
to the sink using the backbone tree
SNA’09 -21
Experimental Results
Parallel routing architecture induced by 4 high-end nodes (PRA(4))
SNA’09 -22
Experimental Results
Tracking a mobile target by HRA scheme (left), and by
PRA(4) scheme (right), where white, yellow, dark blue, red,
green, and orange dots are the locations that the vehicle is
detected and reported back to the sink in 5, 10, 15, 20, 30 and
60 seconds, respectively.
SNA’09 -23
Experimental Results
Latency and Network Throughput
Data Quality
200
80%
70%
160
140
120
DRA
100
80
HRA
60
PRA(4)
40
PRA(8)
20
number of received data
Number of received data
180
60%
50%
DRA
40%
HRA
30%
PRA(4)
20%
PRA(8)
10%
0%
0
5
10 15 20 25 30 35 40 45 50 55 60 > 60
Seconds
0m-3m
3m - 6m
6m - 9m
9m - 11m
Distance of the sensed target and the sensing sensor node
SNA’09 -24
Experimental Results
Propotion of Actions
Rate of Dead nodes
7.00%
500000
6.00%
number of actions
600000
400000
DRA
5.84%
5.09%
5.00%
300000
HRA
4.00%
200000
PRA(4)
3.00%
100000
PRA(8)
2.00%
1.30%
1.34%
PRA(4)
PRA(8)
1.00%
0
Send
Listen
Sample Aggregate
Sleep
0.00%
DRA
HRA
SNA’09 -25
Conclusion and Future Work
• Hierarchical resource allocation (HRA) scheme can largely
improve latency, QoS and network lifetime.
• Parallel resource allocation (PRA) induced from high-end nodes
in heterogeneous sensor network can relay back more qualified
data before data overflowing from buffers.
• Future work: resource allocation for multiple customers and
multiple tasks.
SNA’09 -26