Download Proposed Approach – Underlying Cluster

Autonomous Market-Based Approach for
Resource Allocation
in A Cluster-Based Sensor Network
Wei Chen, Heh Miao
Department of Computer Science
Center of Excellence for Battlefield Sensor Fusion
Tennessee State University, United States
Koichi Wada
Nagoya Institute of Technology, Japan
IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, 2009
IEE MCDM 2009
Presentation Outline
 Introduction: Sensor network, Fusion, Resource Allocation
 Problem Statement
 Review of Market-Based Resource Allocation: Centralized
vs. Decentralized Approaches
 Proposed Market-Based Resource Allocation Approach for
Cluster-based Sensor Networks
 Implementation and Experiment Results
 Future work
IEE MCDM 2009
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
Introduction
Resource Allocation
How to assign the resources for achieving the requested data with
smallest delay while keeping the network alive as long as possible?
Problem Statement
Given a task or tasks, how to assign sensors and
network resources for fulfilling 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?
Review of Market-Based Approaches
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 onehop Sensor Network
Review of Market-Based Approaches
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) if the action a is available
u (a)  
otherwise
0
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
Base Station
(Clients, Consumers)
Infrequently central control
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
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
Goal:
(1) providing promise solution of resource
allocation for given tasks with less delay
and high QoS; and
(2) extending network lifetime
Base Station
(Clients, Consumers)
Infrequently central control
Cluster head
Cluster
LRM
IRM
IRM
LRM
LRM
IRM
IRM
Sensor Network
Proposed Approach – Assumptions
Underlying sensor network: cluster-based
sensor network
Most sensor networks nowadays are built with
hierarchical and reconfigurable structures that introduce
efficient sensing, computing and networking, and long
network lifetime. One of the most well used hierarchical
structures is cluster-based structure.
Market-Based Approach
Instead of low-level sensor programming that manually
tunes sensor and other resource usage, we use a marketbased approach for dynamic allocation of system
resources.
Proposed Approach – Principles
Goods and Actions
In the HRA approach, the actions that sensor nodes take depend on the task,
but typically can include sampling a sensor, aggregating multiple sensor
readings. An available action set is decided at each timeslot. Production of one
good may have dependencies on the availability of others. For example, a node
cannot aggregate sensor readings until it has acquired multiple readings.
Taking an action may or may not produce a good of value to the sensor
network as a whole. For example, listening for incoming radio messages is
only valuable if a node hears a transmission from another node. We suppose
that nodes can determine locally whether a given action deserves a payment.
Resource Constraints
There are tradeoffs between the network resources and the quality of the
service. Especially, a node’s energy constrains the actions that it can take. In
the IRM, a payment-possibility threshold is used. When the estimated
probability of payment from an action is smaller than the threshold, the action
is not scheduled for the current timeslot. It is expected that the energy can be
saved by reducing unnecessary actions and the quality of the service can be
maintained by giving no energy constraint to useful actions.
Proposed Approach – Design Details
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
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 can select an action among a set of actions
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. Nodes can determine locally which actions were useful
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 the
node r takes action a;
budget is 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
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.
Proposed Approach – Underlying
Cluster-Based Sensor Network
 Underlying Networking Architecture : cluster-based hierarchical networking
architecture for supporting hierarchical routing and resource allocation.
 Data Dissemination/Collection Algorithms: distributed routing algorithm for time
and energy efficient broadcast, multicast, unicast and data gathering
 Network Self-Organization Functions: network self-construction/reconfiguration
Networking Serices
Data query and dissemination
Data collection and integration
Data fusion via routing
Management services
Synchronization
Localization
Node and event failure detection
Architecture reconfiguration
Configurable Service level
Data fusion on a
group via routing
sink
Hierarchical Architecture level
backbone
cluster
A group of
specified nodes
A Flat WSN level
Proposed Approach – Underlying
Cluster-Based Sensor Network
Broadcasting Network
Flat
(unstructured)
Clustering-based (structured) Network
 Clustering-based Network
Architecture : Combining the
centralized control in local with
the decentralized control in global
 Efficient Routing Algorithms for
Broadcast/Multicast, and Data
Gathering
 Network Self-Organization for
maximizing network lifetime: head
rotation, node move-in and move
out – Physical layer dependent
a
e
b
c
d
Euclid circuit traveling
Implementation and Simulation
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; (3) Processor
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
Experimental Results
Flat Sensor Network
sink
Experimental Results
Cluster-based Sensor Networks
sink
Experimental Results
Latency (one mobile target)
In 20 seconds, DRA received 77 messages, HRA received 119 messages
HRA (With Local Optimization)
DRA (Without Local Optimization)
Test field
Test field
Latency of Messages (One Target, NOPT)
11; 2%
16; 3% 24; 4%
26; 5%
9; 7%
2; 1%
46; 39%
0 - 5 sec
0 - 5 sec
5 - 10 sec
458; 86%
Latency of Messages (One Target, OPT)
19; 16%
5 - 10 sec
10 - 15 sec
10 - 15 sec
15 - 20 sec
15 - 20 sec
>20 sec
>20 sec
45; 37%
Experimental Results
Latency (two mobile targets)
DRA (Without Local Optimization)
HRA (With Local Optimization)
Test field
Test field
Latency of Messages (Two Targets, NOPT)
149; 40%
134; 37%
Latency of Messages (Two Targets, OPT)
9; 5%
0 - 5 sec
9; 5%
5 - 10 sec
10 - 15 sec
15 - 20 sec
45; 24%
0 - 5 sec
5 - 10 sec
10 - 15 sec
15 - 20 sec
>20 sec
>20 sec
20; 5%
40; 11%
25; 7%
123; 65%
Experimental Results
After tracking a mobile target 200 seconds
Experimental Results
The closer to the target, the more accurate sensor
readings a sensor node can get
Experimental Results
Experimental Results
Observation: change the price of sending only may not work well.
Future Work
Fusion missions: Target tracks, target identification,
…
Upper-level fusion
Fusion Service Level
Mission management:
decomposing mission, assigning
priority, allocating task, …
Task and sensor management
identifying network service, specifying
resource and service quality
Customer
/Base Station
Ask for
Return back
data/information
sensed/fused data
Management services
Networking Serices
Synchronization
Data query and dissemination
Localization
Data collection and integration
Node and event failure detection
Data fusion via routing
Architecture reconfiguration
Configurable Service level
Data fusion on a
group via routing
sink
Hierarchical Architecture level
backbone
cluster
A group of
specified nodes
A Flat WSN level
Homework and assignment
1. Discuss the tradeoff between DRA and HRA on latency, energy
consumption, and network maintenance, respectively.
2. Who adjusts the prices of actions? Is it centralized control or
distributed control? How to make the HRA more efficient by
adjusting the price of actions?