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 FusionTennessee State University

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International Conference on Sensor Networks and Application, 2009

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

sink

Sensor Network & Sensor Fusion

Return back sensed/fused data

Ask for data/information

Fusion missions: Target tracks, target identification, environment monitoring …

Upper-level fusion Base Station

Lower-level fusion

Sensor Network

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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?

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How to assign the resources for achieving the requested data with smallest delay while keeping the network alive as long as possible?

Resource Allocation

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Problem Statement

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.

Computation intensive

Central Sensor manager

Base Station (Clients, Consumers)

Single-platform or one-hop Sensor Network

Not suitable to a multi-hop sensor network, where communication cost of relaying data are the dominant cost.

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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.

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

No control points, hardly achieving optimal resource allocationOverlap on sensing, computation, and networking

Review of Market-Based Resource Allocation

payment a receiving ofy probabilit of estimation theis )(

otherwise 0

available is action theif )()()(

a

aapriceaau

IRM

IRM

IRM

IRM

IRM

IRM

IRM

Sensor Network

Base Station(Clients, Consumers)

Infrequently central control

Individual Resource Manager

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Proposed Approach- Framework

• 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

Hierarchical Resource Allocation (HRA) in Cluster-Based Sensor Networks

Cluster head

IRM

IRM

LRM

LRM

IRM

LRMCluster

Sensor Network

IRM

Base Station(Clients, Consumers)

Infrequently central control

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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.

Autonomous Scheduling1. Rather than static scheduling, individual nodes tune their schedules over time2. 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 actions5. Reinforcement learning to select best actions

Action model at nodes:1. Nodes select an action among a set of actions at each timeslot2. Each action has an associated energy cost3. 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 reward5. 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.

Proposed Approach –Detail Design

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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 then node r goes to sleep //saving energy// else begin node r takes action a; 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;

otherwise 0

available is action theif )()()(

aapricea

au

Utility function

G. Mainland’s algorithm: An energy budget is used for each fixed period. Nodes take the actions that can maximize the utility function even the profit is very small when he budget allowed.

Proposed Approach –Design Details

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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 ProtocolsBroadcast protocol and data gathering protocol for the underlying cluster-based sensor network are used.

Reconfigurable FunctionWhen a node runs out of battery, the network will be self-reconfigured.

Proposed Approach –Design Details

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Implementation

Application: Tracking Mobile TargetsField: 105m×105m Nodes: 800 MICA2/Crossbow motesResource: (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/CALocal optimization at LRM: cluster-head select the best radio messages (most accurate message) when it receives multiple overlap messages from its member nodesRouting 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 uJAction 4: Aggregation, 84.1 mJ, (Action 5): sleeping, 12 uJ

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Experimental Results

Flat Sensor Network

sink

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Experimental Results

Cluster-based Sensor Networks

sink

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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)

Latency of Messages (One Target, OPT)

46; 39%

45; 37%

19; 16%

9; 7% 2; 1%

0 - 5 sec

5 - 10 sec

10 - 15 sec

15 - 20 sec

>20 sec

Latency of Messages (One Target, NOPT)

16; 3%11; 2% 24; 4%26; 5%

458; 86%

0 - 5 sec

5 - 10 sec

10 - 15 sec

15 - 20 sec

>20 sec

Test field Test field

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After tracking a mobile target 200 seconds

Experimental Results

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Experimental Results

Observation: change the price of

sending only may not work well.

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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 high-end nodes.

Parallel Routing for Heterogeneous Sensor Networks

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Heterogeneous Sensor Network: Suppose there are k high-end nodes and n L-nodes in the heterogeneous sensor network, where k << n.

Formation of Parallel Routing Architecture

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 H-node, 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 H-node using a cluster-based routing tree; the H-nodes forward data back to the sink using the backbone tree

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Experimental Results

Parallel routing architecture induced by 4 high-end nodes (PRA(4))

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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.

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Experimental Results

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Experimental Results

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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.

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