A Distributed Coordination Framework for Wireless Sensor and Actor Networks Tommaso Melodia, Dario Pompili, Vehbi C.Gungor, Ian F.Akyildiz (MobiHoc 2005) Presented by Taehee Kim. This is based on a presentation file of ‘Tommaso Melodia’ at Mobihoc

Wireless Sensor and Actor Networks (WSANs) Sensors –Passive elements sensing the environment –Limited energy, processing, and communication capabilities Actors –Active elements acting on the environment –Higher processing and communication capabilities –Less constrained energy resources (longer battery life or constant power source) 1/26

WSAN Application Distributed Robotics & Sensor Network: –(Mobile) robots dispersed throughout a sensor network Environmental Applications: –Detecting and extinguishing foresest fire Battlefield Applications: –Sensors detect mines or explosive substances –Actors annihilate them or function as tanks Microclimate control in buildings: –In case of very high or low temperature values, trigger the audio alarm actors in that area 2/26

WSANs vs. Wireless Sensor Networks Real-Time Requirements for Timely Actions –Rapidly respond to sensor input –To perform right actions, sensor data must be valid at the time of acting Heterogeneous Node Deployment –Sensor Densely deployed –Actor Loosely deployed Coordination Requirements –Sensor-Actor Coordination –Actor-Actor Coordination 3/26

WSAN Communication Architecture Sensors Actors –No intervention from the sink is necessary –Localized information exchange –Low latency –Distributed sensor-actor and actor-actor coordination required 4/26

WSANs vs. Wireless Sensor Networks Need for a distributed coordination mechanism: –Sensor-Actor Coordination Establish data paths between sensors and actors Meet energy efficiency and real-time requirements –Actor-Actor Coordination Decision: does an action need to be performed? What is the optimal strategy for the actors to divide the workload? 5/26

Sensor-Actor Coordination

Objectives: –Establish data paths between sensors and actors –Meet energy efficiency and real-time requirements Question: –To which actor does each sensor send its data? –What is the optimal tree from sensors to actors? Our Solution: Event Driven Clustering with Multiple Actors 6/26

Reliability (1/2) Definition 1. The LATENCY BOUND B is the maximum allowed time between sampling of the physical features of the event and the instant when the actor receives a data packet describing these event features Definition 2. A data packet is EXPIRED (UNRELIABLE), if it does not meet the latency bound B Definition 3. A data packet is UNEXPIRED (RELIABLE), if if is received within the latency bound B 7/26

Reliability (2/2) Definition 4. The EVENT RELIABILITY r is the ratio of reliable data packets over all packets received in a decision interval Definition 5. The EVENT RELIABLITY THRESHOLD r th is the minimum event reliability required by the application OBJECTIVE: Comply with the event reliability threshold (r > r th ) with minimum energy expenditure! 8/26

Event-Driven Clustering with Multiple Actors Objective: –Find the optimal strategy for event-driven clustering (To which actors is data sent? Which paths are used?) Solution Approach: –Optimal solution obtained by means of mathematical programming –Integer Linear Programming formulation –NP-Complete Not Scalable(< 100 nodes), centralized solution 9/26

Distributed Protocol (1/3) Objectives of the distributed protocol: –Establish sensor-actor data paths –Cluster the sensors in the event area –Find the optimal working point of the network r> r th (reliability over the threshold) Minimum energy consumption Based on Geographical Routing Decisions taken based of feedbacks from actors Actor calculated reliability r and broadcasts its value to the sensors Sensors switch among start-up, speed-up, aggregation state 10/26

Distributed Protocol (2/3) Sensors probabilistically switch among three different states according to feedback from the actors: –Start-up State: When the event occurs, all sensors switch in the start-up state and establish data paths to the actors according to two-hop rule Quickly establish a data path from each source to one actor Compromise between energy consumption and latency –Actor calculated reliability r and broadcasts its value to the sensors –Speed-up State: ( If r < r th ) Reduce the number of hops of sensor-actor paths so as to reduce the end-to-end delay (lower delay, higher energy consumption) Obtained by sending packets to “far” neighbors (closer to the destination actor) 12/26

Distributed Protocol (3/3) –Aggregation State: ( If r < r th ) Reduce the overall energy consumption when compliant with event reliability (lower energy consumption, higher delay) Send packets to closer neighbors (higher number of hops) –The probability of changing state my depend on the lack/excess of reliability 13/26

Example: path establishment Event occurs ! Nodes establish paths according to the two-hop rule (start-up state) Idle state Start-up state 14/26

Example: low reliability The actor advertises low reliability (r < r th ) idle state start-up state speed-up state Some sensors switch to the speed-up state and select as next hop the closest node to the actor -> reduce latency 15/26

Example: high reliability The actor advertises high reliability (r > r th ) idle state start-up state speed-up state aggregation state Some sensors switch to the aggregation state and select as next hop the closest node already in the node -> reduce energy consumption 16/26

Actor-Actor Coordination

Objective: –Select the best actor(s) in terms of action completion time and energy consumption to perform the action Challenges: –Which actor(s) should execute which action(s)? –How should the multi-actor task allocation be done? 17/26

Actor-Actor Coordination Model Definitions: –Overlapping Area An area that can be acted upon by multiple actors –Non-Overlapping Area An area that can be acted upon by only one actor –Action Completion Time Bound The maximum allowed time from the instant when the event is sensed to the instant when the action is completed –Power Levels Discrete levels of power to perform the action. A higher power level corresponds to a lower action completion time 18/26

Actor-Actor Coordination Problem For an Overlapping Area, actor-actor coordination problem: –Selecting a subset of actors –Adjusting action power levels Maximize the residual energy and complete the action within the action completion bound For a Non-Overlapping Area, actor-actor coordination problem: –Adjust action power levels Maximize the residual energy 19/26

Actor-Actor Coordination Optimal Solution: –Actor-actor coordination problem formulated as a Residual Energy Maximization Problem using Mixed Inter Non- Linear Programming (MINLP) –NP-Complete Problem Distributed Solution: –Real-Time Localized Auction-Based Mechanism –Definitions Seller: Actor receiving the event features Auctioneer: Actor in charge of conducting the auction Buyer: Actor able to act on a particular overlapping area /26

Real-Time Localized Auction-Based Mechanism For the Overlapping areas: –The Seller selects one auctioneer for each overlapping area, i.e., the closest actor to the center of the overlapping area -> Energy spent for auction and auction time reduced! –The Seller informs each auctioneer of the auction area and of the action time bound –The Auctioneer determines the winners of the auction based on the bids received from the buyers. The bids consists of available energy, power level and action completion time –The Auctioneer finds the winners by calculating the optimal solution of the Residual Energy Maximization Problem For the Non-Overlapping areas: –The actor is directly assigned the action task 20/26

Performance Result

Sensor-Actor Coordination – Energy (1/2) 21/26

Sensor-Actor Coordination – Energy (2/2) 22/26

Sensor-Actor Coordination: Delays 23/26

Sensor-Actor Coordination: Path Length 24/26

Actor-Actor Coordination 25/26

Conclusions and Future Work First paper to deal with integrated networks of Sensors and Actors Unified framework for communication and coordination problems in WSANs Solutions for Sensor-Actor coordination and Actor-Actor coordination Focus on real-time and energy consumption Future work will incorporate mobility of actors and tuning of the network dynamics 26/26

Evolution of States for a Sensor