Autonomous Market-Based Approach for Resource Allocation in A Cluster-Based Sensor Network

1. 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

2. 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

3. Lower-level fusion sink Introduction Sensor Network & Sensor Fusion Fusion missions:Target tracks, target identification, environment monitoring … Base Station Upper-level fusion Ask for data/information Return back sensed/fused data Sensor Network

4. 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?

5. 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?

6. Base Station (Clients, Consumers) Central Sensor manager Single-platform or one-hop Sensor Network 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. • Computation intensive • Not suitable to a multi-hop sensor network, where communication cost of relaying data are the dominant cost.

7. IRM IRM IRM IRM IRM IRM IRM 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. • 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 allocation • Overlap on sensing, computation, and networking Base Station (Clients, Consumers) Infrequently central control Sensor Network Individual Resource Manager

8. Base Station (Clients, Consumers) Infrequently central control Cluster head Cluster LRM IRM IRM LRM LRM IRM IRM Sensor Network 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: • providing promise solution of resource allocation for given tasks with less delay and high QoS; and • extending network lifetime

9. 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 market-based approach for dynamic allocation of system resources.

10. 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.

11. Proposed Approach – Design Details • Autonomous Scheduling • Rather than static scheduling, individual nodes tune their schedules over time • Cluster-heads do local optimization in their clusters • Nodes avoid wasting energy • Using the feedback to tune node behavior: nodes receive rewards when they take useful actions • Reinforcement learning to select best actions • Action model at nodes: • Nodes can select an action among a set of actions • Each action has an associated energy cost • 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 • Each node attempts to maximize its reward • Nodes can determine locally which actions were useful

12. 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 the budget is allowed. Utility function Proposed Approach –Design Details Algorithm of the IRM at a node r for each timeslot (scheduling cycle) do (1) with 1-ε probabilityselect an action a from the available action set which has largest utility value; (2) withε 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 thenβ(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;

13. 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.

14. 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 backbone Hierarchical Architecture level cluster A group of specified nodes A Flat WSN level

15. Proposed Approach – Underlying Cluster-Based Sensor Network Broadcasting Flat (unstructured) Network Clustering-based (structured) Network • Clustering-based Network Architecture : Combining the centralized control in local with the decentralized control in global a e b • Efficient Routing Algorithms for Broadcast/Multicast, and Data Gathering c d • Network Self-Organization for maximizing network lifetime: head rotation, node move-in and move out – Physical layer dependent Euclid circuit traveling

16. 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

17. Experimental Results Flat Sensor Network sink

18. Experimental Results Cluster-based Sensor Networks sink

19. 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

20. Experimental Results Latency (two mobile targets) DRA (Without Local Optimization) HRA (With Local Optimization) Test field Test field

21. Experimental Results After tracking a mobile target 200 seconds

22. Experimental Results The closer to the target, the more accurate sensor readings a sensor node can get

23. Experimental Results

24. Experimental Results Observation: change the price of sending only may not work well.

25. Future Work Fusion missions: Target tracks, target identification, … Mission management: decomposing mission, assigning priority, allocating task, … Upper-level fusion Task and sensor management identifying network service, specifying resource and service quality Fusion Service Level Customer /Base Station Ask for data/information Return back sensed/fused data Management services Synchronization Localization Node and event failure detection Architecture reconfiguration Networking Serices Data query and dissemination Data collection and integration Data fusion via routing Configurable Service level Data fusion on a group via routing sink backbone Hierarchical Architecture level cluster A group of specified nodes A Flat WSN level

26. Homework and assignment Discuss the tradeoff between DRA and HRA on latency, energy consumption, and network maintenance, respectively. 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?