Chapter 14: Wireless Sensor and Actor Networks

Wireless Sensor and Actor Networks I. F. Akyildiz and I. H Wireless Sensor and Actor Networks I.F. Akyildiz and I. H. Kasimoglu,“Wireless Sensor and Actor Networks: Research Challenges” Ad Hoc Networks Journal (Elsevier), pp.351-367, Oct. 2004. Task Manager Node Sink Sensor/Actor Field Sensors Actors

Actuators vs. Actors Why do we call them actors? Actuator (Texas Instruments Technical Glossary): “An actuator is a device to convert an electrical control signal to a physical action. Actuators may be used for flow-control valves, pumps, positioning drives, motors, switches, relays and meters.” The mobility of a robot may be enabled by several actuators (motors, servo-mechanisms, etc) However, the robot represents one single network entity which we refer to as actor Hence, one actor can be endowed with multiple actuators

Wireless Sensor and Actor Networks Sensors Passive elements sensing from 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)

Sub-Kilogram Intelligent Tele-robots (SKITs): Networked Robots having Coordination & Wireless Communication Capabilities

Robotic Mule: Autonomous Battlefield Robot designed for the Army

Mini-Robot (developed at Sandia National Laboratories)

Helicopter Platform

Components of Sensor & Actor Nodes Sensing Unit Processor & Storage Transceiver ADC Sensor Node Power Unit Actuation Unit Controller (Decision Unit) Processor & Storage Transceiver DAC Actor Node Power Unit

Integrated Sensor & Actor Nodes Processing Sensing Unit Actuation Controller Decision Process Power Unit Transceiver

WSAN Applications Environmental Applications: Detecting and extinguishing forest fire. Microclimate control in buildings: In case of very high or low temperature values, trigger the audio alarm actors in that area. Distributed Robotics & Sensor Network: (Mobile) robots dispersed throughout a sensor network alarm actors in that area.

WSAN Applications Parking Airport Safety City Maintenance Sewage and Contamination Control Battlefield Applications: Sensors detect mines or explosive substances Actors annihilate them or function as tanks

WSANs vs. Wireless Sensor Networks Real-Time Requirements for Timely Actions Rapidly respond to sensor input (e.g., fire application) To perform right actions, sensor data must be valid at the time of acting Heterogeneous Node Deployment Sensors Actors Densely deployed Loosely deployed due to the different coverage requirements and physical interaction methods of acting task

WSANs vs. Wireless Sensor Networks Coordination Requirements Sensor-Actor Coordination Actor-Actor Coordination

WSAN Communication Architecture Semi-Automated Architecture Sink Sensors  Sink  Actors Requires manual intervention at sink No sensor-actor and actor-actor coordination needed Similar to the conventional WSN architecture Event Area

WSAN Communication Architecture Automated Architecture Sink Event Area Sensors  Actors No intervention from sink is necessary Localized information exchange Low latency Distributed sensor-actor and actor-actor coordination required

SENSOR-ACTOR COORDINATION Challenges: Which sensor(s) communicate with which actor(s) (Single or Multiple Actors) How should the communication occur? (i.e., single-hop or multi-hop) What are the requirements of the communication (i.e., real-time, energy efficiency)

Sensor-Actor Coordination Which sensor(s) communicate with which actor(s)? CASE 1: Minimum number of sensors to report the sensed event CASE 2: Minimum set of actors to cover the event region Both cases above The entire set of sensors and actors in the vicinity of the region The set of actors whose acting regions do not overlap

Sensor-Actor Coordination SINGLE ACTOR Event Area Selection of the most appropriate actor To select, sensors need to coordinate with each other

Sensor-Actor Coordination SINGLE ACTOR Selecting a single actor node may be based on: The distance between the event area and the actor The energy consumption of the path from sensors to the actor The action range of the actor

Sensor-Actor Coordination MULTI ACTORS Event Area Actor Clustering is required Sensors only need to coordinate with sensors within some neighborhood to form clusters or groups

Sensor-Actor Coordination MULTI ACTORS Clusters may be formed such a way that The event transmission time from sensors to actors is minimized The events from sensors to actors are transmitted through the minimum energy paths The action regions can cover the entire event area

ACTOR-ACTOR COORDINATION Challenges: Which actor(s) should execute which action(s)? How should multi-actor task allocation be done?

Actor-Actor Coordination Single-Actor Task vs. Multi-Actor Task Single-Actor Task How is the single actor selected? Multi-Actor Task What is the optimum number of actors performing actions? Selection of most fit actors among the capable actors for that task Only a subset of actors covering the entire event region may perform the task to save action energy

A Distributed Coordination Framework for WSANs T. Melodia, D A Distributed Coordination Framework for WSANs T. Melodia, D. Pompili, V. C. Gungor, I. F. Akyildiz, ACM MOBIHOC’05, May 2005. Also in IEEE Transactions on Mobile Computing, 2007. Comprehensive framework for coordination problems SENSOR-ACTOR COORDINATION Optimal Event-driven Clustering A Distributed Scalable Protocol ACTOR-ACTOR COORDINATION Optimal Solution Real-time Localized Auction

Coordination Requirements 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? Which action should be performed? How to share the workload among actors?

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? Solution: Event Driven Clustering with Multiple Actors

Event-Driven Clustering with Multiple Actors Event Area 1. Event Occurs 2. Sensor-Actor Coordination: Event-Driven Clustering What is the optimal clustering strategy? Distributed algorithm?

Reliability Definition 1. The latency bound B is the maximum allowed time between sampling of the physical features of the event and the moment when the actor receives a data packet describing these event features

Reliability 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 it is received within the latency bound B

Reliability 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 reliability threshold rth is the minimum event reliability required by the application OBJECTIVE: Comply with the event reliability threshold (r>rth) with minimum energy expenditure!

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?)  a joint Clustering and Routing problem

Event-Driven Clustering with Multiple Actors Requirements of the Optimal Solution: Provide reliability above the event reliability threshold (r>rth) Minimize overall Energy Consumption Optimal solution obtained by  Integer Linear Programming formulation

Event-Driven Clustering with Multiple Actors ILP Formulation is provided -> allows finding the optimal solution BUT NP-Complete problem: Not scalable (<100 nodes) Centralized solution Helps gaining insight in the properties of the optimal solution Performance benchmark for distributed, more scalable solutions

A Distributed Protocol Find the optimal working point of the network, i.e.: r>rth ( reliability over the threshold) Minimum energy consumption Based on the feedbacks from actors: Actor calculates reliability r and broadcasts its value to the sensors

A Distributed Protocol If the reliability r is complied with (r>rth), a certain portion of the sensors switch in the aggregation state to save energy (lower energy consumption, higher delay) Equilibrium is reached when reliability threshold is met (r ≈ rth) with minimum energy consumption.

A Distributed Protocol BASIC IDEA: When the event is first sensed, sensors all begin in the start-up state and establish data paths to the actors If reliability is advertised to be low (r<rth) Certain portion of the sensors switch to speed-up state, which shortens the end-to-end paths (lower delay, higher energy consumption)

A Distributed Protocol Sensors probabilistically switch among three different states according to feedback from the actors: Start-up State: Quickly establish a data path from each source to one actor Compromise between energy consumption and latency

A Distributed Protocol Speed-up State: Reduce the number of hops in sensor-actor paths so as to reduce the end-to-end delay Obtained by sending packets to “far” neighbors (closer to the destination actor)

A Distributed Protocol Aggregation state: Reduce the overall energy consumption when compliant with event reliability Send packets to closer neighbors (higher number of hops)

Example: Path Establishment nodes establish paths (start-up state) idle start-up state an event occurs Another actor is too far away and thus not energy efficient for any of the nodes in the event area

Example: Low Reliability Some sensors switch to the speed-up state (probabilistically) and select as next hop the closest node to the actor  reduce latency The actor advertises low reliability (r<rth) idle start-up state speed-up state

Example: High Reliability Some sensors switch to the aggregation state (probabilistically) and select as next hop the closest node already in the da-tree  reduce energy consumption The actor advertises high reliability (r>rth) idle start-up state speed-up state aggregation state

Actor-Actor Coordination Objective: Selecting the best actor(s) in terms of action completion time and energy consumption so as to perform the action! Challenges: Which actor(s) should execute which action(s)? How should multi-actor task allocation be done?

Actor-Actor Coordination Model DEFINITIONs: Overlapping Area: Area can be acted upon by multiple actors Non-Overlapping Area: Area can be acted upon only by one actor

Actor-Actor Coordination Model Action Completion Time Bound: The maximum allowed time from the moment when the event is sensed to the moment when the action is completed Power Levels: Discrete levels of power for performing the action  A higher power level corresponds to a lower action completion time!

Actor-Actor Coordination Problems 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

Actor-Actor Coordination Problems For a Non-Overlapping Area, actor-actor coordination problem: Adjusting action power levels  Maximize the residual energy

Actor-Actor Coordination Optimal Solution: Actor-actor coordination problem formulated as a Residual Energy Maximization Problem using Mixed Integer Non-Linear Programming (MINLP) Distributed Solution: Real-Time Localized Auction-Based Mechanism

Real-Time Localized Auction-Based Mechanism Inspired by the behaviors of agents in a Market Economy  Interactions between buyers and sellers Possible Roles of the Actors: Seller: Actor receiving the event features Auctioneer: Actor in charge of conducting the auction Buyer: Actor(s) that can act on a particular overlapping area

Real-Time Localized Auction-Based Mechanism For overlapping areas: 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! Seller informs each auctioneer about the auction area and the action time bound

Real-Time Localized Auction-Based Mechanism Auctioneer determines the winners of the auction based on the bids received from the buyers. Bids consists of available energy, power level and action completion time

Real-Time Localized Auction-Based Mechanism The auctioneer finds the winners by calculating the optimal solution of the Residual Energy Maximization Problem For Non-Overlapping areas The corresponding actor is directly assigned the action task

Sensor-Actor Coordination Start-up (speed-up) configuration: all nodes are in the start-up (speed-up) state Comparison between the optimal solution of the event-driven clustering problem and the energy consumption of start-up, speed-up, aggregation configuration with varying event ranges (60 sensors; 4 actors)

Sensor-Actor Coordination Comparison of the energy consumption of different configurations The energy consumption in the aggregation configuration is much lower that in the start-up and speed-up configuration

Sensor-Actor Coordination Comparison of average number of hops for start-up and speed-up configuration. The speed-up configuration shows paths with lower delay (less hops)

Cyber Physical Systems Integration of computation with physical processes. Embedded computers and networks monitor and control physical processes in feedback loops where physical processes affect computations and vice versa.   CPS will blend sensing, actuation, computation, networking, and physical processes as action networks. "Networked Information Technology Systems Connected With The Physical World", also referred to as cyber-physical systems, are cited as the top technical priority for networking and IT research and development. President's Council of Advisors on Science and Technology