Chapter 14:
Wireless Sensor and Actor Networks
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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
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Actors
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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
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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)
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Sub-Kilogram Intelligent Tele-robots (SKITs):
Networked Robots having Coordination & Wireless
Communication Capabilities
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Robotic Mule: Autonomous Battlefield
Robot designed for the Army
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Mini-Robot
(developed at Sandia National Laboratories)
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Helicopter Platform
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Components of Sensor & Actor Nodes
Sensing
Unit
Processor
& Storage
ADC
Transceiver
Sensor Node
Power Unit
Actuation
Unit
DAC
Controller
(Decision
Unit)
Processor
& Storage
Transceiver
Actor Node
Power Unit
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Integrated Sensor & Actor Nodes
Sensing
Unit
Actuation
Unit
Sensor
Processing
Controller
Decision Process
Transceiver
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Power Unit
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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.
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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
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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
Densely deployed
– Actors
Loosely deployed due to the different
coverage requirements and physical
interaction methods of acting task
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WSANs vs. Wireless Sensor Networks
 Coordination Requirements
Sensor-Actor Coordination
Actor-Actor Coordination
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WSAN Communication Architecture
Semi-Automated Architecture
Sink
Event Area
 Sensors  Sink  Actors
 Requires manual intervention at sink
 No sensor-actor and actor-actor coordination needed
 Similar to the conventional WSN architecture
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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
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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)
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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
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Sensor-Actor Coordination
SINGLE ACTOR
Sensor
Actor
Event Area


Selection of the most appropriate actor
To select, sensors need to coordinate with each other
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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
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Sensor-Actor Coordination
MULTI ACTORS
Sensor
Event Area

Actor
Clustering is required
Sensors only need to coordinate with sensors within
some neighborhood to form clusters or groups

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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
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ACTOR-ACTOR COORDINATION
 Challenges:
Which actor(s) should execute which action(s)?
How should multi-actor task allocation be done?
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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
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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
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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?
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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
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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?
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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
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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
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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!
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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
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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
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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
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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
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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.
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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)
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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
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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)
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A Distributed Protocol
Aggregation state:
Reduce the overall energy consumption when
compliant with event reliability
Send packets to closer neighbors (higher number of
hops)
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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
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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
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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
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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?
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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
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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!
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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
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Actor-Actor Coordination Problems
 For a Non-Overlapping Area, actor-actor coordination
problem:
 Adjusting action power levels  Maximize the residual
energy
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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
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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
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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
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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
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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
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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)
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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
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Sensor-Actor Coordination
Comparison of
average
number of
hops for startup and speedup
configuration.
The speed-up
configuration
shows paths
with lower
delay (less
hops)
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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
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