1. Chapter 14:
Wireless Sensor and Actor Networks

2. 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 , Oct
Task Manager
Node
Sink
Sensor/Actor Field
Sensors
Actors

3. 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

4. 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)

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

6. Robotic Mule: Autonomous Battlefield
Robot designed for the Army

7. Mini-Robot
(developed at Sandia National Laboratories)

8. Helicopter Platform

9. 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

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

11. 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.

12. 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

13. 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

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

15. 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

16. 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

17. 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)

18. 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

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

20. 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

21. 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

22. 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

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

24. 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

25. 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 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

26. 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?

27. 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

28. 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?

29. 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

30. 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

31. 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!

32. 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

33. 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

34. 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

35. 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

36. 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.

37. 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)

38. 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

39. 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)

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

41. 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

42. 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

43. 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

44. 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?

45. 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

46. 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!

47. 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

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

49. 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

50. 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

51. 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

52. 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

53. 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

54. 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)

55. 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

56. 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)

57. 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