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Communication and Coordination in Wireless and Sensor Actor Networks

Melodia, Pompili, Gungor and AkyildizPresented By: Adam Browning

6 NOV 2007

Outline

• Motivation

• Related Work

• DEPR

• Actor/Actor Coordination

• Conclusions

• Future Work

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Motivation

• As distributed control systems, WSANs need fast, reliable communications

• Permit real-time coordination and cooperation in WSANs

• Maximize energy efficiency and reliability in WSANs

• Define an event-driven partitioning scheme to achieve goals

Related Work

• I.F. Akyildiz and I.H. Kasimoglu [1] defines research challenges in WSANs

• R. Vedantham, Z. Zhuang, and R. Sivakumar [7] defines hazards and avoidance mechanisms in WSANs

• SPEED [8] provides real-time communications• MMSPEED [9] extends SPEED with the ability to

meet different delay & reliability requirements• [10-12] work to guarantee scalability & efficiency

with topology-dependent partitioning

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Definitions

• WSAN: Wireless network of sensors and actors

– Actors are not strictly actuators; they also send/receive network messages

• Sender/Actor Coordination: establishing data path between sensors & actors

• Actor/Actor Coordination: actors working together to decide how to handle an event

Definitions continued…

• B: Latency Bound

• Expired: Any packet that is not received within B (also called unreliable)

• r: Reliability ratio

• rth: Minimum acceptable reliability ratio

• Sv: The set of all nodes (sensors & actors)

• SA + SS = Sv; P = {(s,a) : s Є S, a Є A} (set of source-destination connections)

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Equations

Constant energy cost / unit distance

Path loss exponent (2 < α < 5)

d Constant energy cost / unit distance

1 iff link ij is associated with k

Cost of ij (E for ij)

Penalty for unreliable packetsQ Number of unreliable packets

Distributed Event-driven Partitioning and Routing (DEPR)

• Relies on local information

• Makes greedy routing decisions

• Group feedback to avoid overhead of providing reliability feedback to sources individually

• State-machine based

• Forms a data-aggregation tree (da-tree)

• Dynamically modifies transmit power of nodes to maintain reliability threshold

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DEPR Assumptions

• Each sensor knows where it is

• Each sensor knows the location of its neighbors and the actors

• Network is synchronized

• Nodes can dynamically alter their transmission power

DEPR FSM

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DEPR Start-Up

• Entered when packet received or event observed

• Selects next hop based on two-hop rule

– Find Minimum

– Eelec shows up 4x because we send from 2 nodes and receive from 2 nodes

– Guarantees no loops

DEPR Speed-Up

• Tries to improve reliability by minimizing # hops from source to actor with Greedy Routing Scheme

• Sets nextHop to be the node with the greatest absolute positive advance toward an actor

– Algorithm does so by selecting the node farthestfrom current node that is closer to the actor, but within transmission range

– Does the algorithm always work?

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DEPR Speed-Up Mess-Up?What about:

A SD1D2

d = 5

d = 4

d = 3

DSR Speed-Up Mess Up

A SD1

D2

?

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DSR Speed-Up Mess Up

A SD1

D2

X

DEPR Aggregation

• Basically the opposite of Speed-Up

• If reliability goes above threshold, then find a closer neighbor to save energy

– Leaf nodes start sending to nearest neighbor that is closer to its actor than they are

– Non-leaf nodes start sending to the nearest neighbor on the same tree that is closer to its actor than they are (otherwise could form cycles)

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DEPR State Transitions

• Driven by actor feedback

• Actors periodically calculate:

– Reliability at instant k:

– Short-term reliability at instant k:

– Anticipated reliability:

• Does long and short-term reliability calculations to spot trends

DEPR State Transitions cont.

• If predicted reliability is below threshold, tell nodes to move to Speed-Up state

• If predicted reliability is above threshold– And long and short-term reliability are above

threshold tell nodes to go to Aggregate state

– Otherwise keep status quo

• If reliability is below threshold, short-term reliability is below high-mark and predicted reliability is above low-mark, tell everyone to move to Speed-Up

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DEPR State Transitions cont.

• If short term reliability is below low-threshold, tell everyone to speed up

– This takes precedence over ALL of the other rules

• If none of the preceding requirements are met

– If expected reliability above threshold, tell nodes to move to Aggregate state

– If expected reliability below threshold, tell nodes to move to Speed-Up state

Recovery State

• Recovery state is entered when a node cannot get a message closer to its actor

• Nodes transmit at highest power, with a virtual proximity so that their neighbors can hear

• If a node hears a neighbor with a higher virtual proximity transmitting to the same destination, then it adds that neighbor to its detour path

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Actor/Actor Coordination

• Actors have an area of effect

– Areas of actors may overlap

• Sometimes an actor can’t act on an event

– e.g. camera can’t turn to record the event

• δ[s] , the action completion bound, is the max length of time from when an event is sensed to when it is acted upon

Actor Area Overlap

• Areas 1-8 are overlapping

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Actor/Actor Coordination Definitions

• SA = Set of actors

• SC = Set of collectors

• = h non-overlapping areas for which actor c is responsible

• = m overlapping areas for which actor c is responsible

• Hc = Number of non-overlapping areas

• Mc = Number of overlapping areas

Overlap display

• Yellow areas are

• Red areas are

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More Definitions

• is the set of actors that can act on the mthoverlapping area for which c is responsible

• Ra[m] is the action range of actor a

• is the maximum power that a can use to perform an action

– Selected from L discrete power levels

– Using more power gets the action done more quickly

• = Efficiency of actor a

• is the time for actor a, workingindependently to complete an action on the mth overlapping area using power level p

• is the power-time relationship for actor a

• K is a constant (in Joules/m2); is the pth

power level for the mth overlapping area

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ha = 1 iff actor a is involved in an action; = 1 iff m in a’s action range

Goal is to Maximize: Within Constraints

Energy must remain non-negative

Energy use comes from all areas on which an actor works

Only 1 power level/actor & area; Every area must be acted upon by someone

Multiple actors working together get the job done faster

ha is 1 if a is acting on an area

An actor can only act on events in its action area

Actor/Actor Coordination using Localized Auction Protocol

• Three possible roles– Seller: collector for an event area

– Auctioneer: conducts the “auction” for an overlapping area; selected by the Seller

– Buyer: Actors that can act on an overlapping area

• Buyers make a bid consisting of their power level, time to complete action and remaining power

• Multiple Auctions occur in parallel

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Localized Auction Protocol cont.

• Seller selects an auctioneer for each overlapping area in its area of effect

– Seller sends the auctioneer the time that it can be spent acting on the event and the max time to spend on the auction

• If there’s no overlap, then the actor for the area does the work

CONCLUSIONS!

• Sensor/Actor Optimization problem modeled in A Mathematical Programming Language (AMPL) and solved with CPLEX

• DEPR Convergence analysis simulated

– Assumed ideal feedback to focus on DEPR & avoid noise from communication mechanism

– LOTS of details, not critical to results

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• Optimum cost is negligible

• Aggregation from speed-up is a growing curve– Aggregation flattening the speed-up cost

• Start-Up grows quickly– Have to send messages further

• Speed-up spikes up– Trading energy for speed

• Why is aggregation from startup is a flat curve?– Data “fusion” consolidates messages to save

energy

Sensor/Actor Energy Cost as a Measure of Event Ranges

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Startup Cost as a function of event distance and # of nodes

• Longer distance causes greater cost

– Obvious reason, having to send messages further costs more energy

• More nodes cause greater cost

– Total cost, more sensors bring more messages

• The Speed-Up state tells the same story as Startup, and for the same reasons

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DEPR Convergence Analysis

• If reliability is really low, set chances of moving to speed-up to 50%

• If reliability is a little below the low threshold, use a 10% chance to moving to speed-up

• If probability of moving to aggregate state is over 10%, instabilities result and observed reliability plummets

DEPR Observed Reliability

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DEPR Convergence

Distribution of Delays

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Delays at simulation times

Actor/Actor Coordination

• Tested with three solution approaches

– Optimal: Actors chosen by solving Residual Energy Maximization problem

– One-Actor: Action is taken in overlapping areas by the one actor who will have highest remaining residual energy

– Localized Auction: Approach described earlier

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Actor Types

• Tested with two types of actors

– Homogeneous Actors: All actors have efficiency of 0.8

– Heterogeneous Actors: 50% of actors have 0.6 efficiency, rest have 0.9 efficiency

Average Residual Energy (Homogeneous Actors)

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Average Residual Energy(Heterogeneous Actors)

Future Work

• Create an accurate, non-restrictive analytical model for the delay of large-scan WSANs under different conditions

• Run simulations longer to see how performance holds up as system runs out of energy