1. Multi-channel Real-time Communications in Wireless Sensor Networks
Xiaodong Wang
Good afternoon, my name is XXX. I am a 2nd year Ph.D. student at the University of Tennessee. This is joint work with another student, XXX, and my advisor, Dr. Xiaorui Wang. 1

2. Introduction to Wireless Sensor Networks
A wireless sensor network (WSN) consists of spatially distributed inexpensive miniature devices to cooperatively monitor physical or environmental conditions [Wikipedia]
Cooperatively: communication with each other over wireless channels
Monitor: capable of computation and sensing
Inexpensive: Ideally less than 1 cent
Have a wide range of potential applications
industry, science, transportation, civil infrastructure, security, etc.
In my presentation today, I will mainly cover the following sub topics.
First,….
Then
After that
Finally

3. Comparison with Traditional Wireless Networks
Integration of sensors, processors and radio
Sensors are networked
Very limited resource and very low cost
Deeply embedded and networked
Stationary locations
May allow limited mobility
Large Scale Deployment
Data-centric communication
Care about data instead of identity: mostly many to one communication
Different from Internet or wireless ad hoc networks

4. Some Issues with WSN Energy Consumption End-to-end Communication Delay
Energy resource is critical in WSN: provided by batteries.
Normally don’t expect recharging or battery replacement
Research goal: extend network life
End-to-end Communication Delay
Real-time is important for a lot of application
Coverage
The range of monitoring and the communication connectivity
Synchronization
Distributed system requires good timing knowledge. Clock drift on the local crystal causes trouble
Security
Wireless channel is insecure

5. Real-time Property Packet Reception Ratio (PRR)
The probability of a packet to be successfully received by the receiver
Real-time metric: , the total transmissions we need to successfully receive a packet.
What is impacting real time service quality?
Interference
Packet cannot meet the required deadline because of collision and contention
Long packet routing path
High workload
Application type decides traffic pattern:
Event driven: End-to-end communication
Data collection monitoring: May require data aggregation

6. Flow-Based Real-time Communication In Multi-Channel Wireless Sensor Networks
Xiaodong Wang, Xiaorui Wang, Xing Fu, *Guoliang Xing, Nitish Jha
Good afternoon, my name is XXX. I am a 2nd year Ph.D. student at the University of Tennessee. This is joint work with another student, XXX, and my advisor, Dr. Xiaorui Wang.
EECS Department University of Tennessee, Knoxville
* CSE Department Michigan State University

7. Empirical Study on Multi-Channel Communication
With single channel, increasing sending power has significant impact to others’ transmission
Almost 40% drop ratio when the communication power is low
Experimental Setup
Using multiple channels significantly mitigates the interference to other transmissions.
Single Channel
Multiple Channel
Thus we conduct some empirical study on Multi-channel Communications
The experimental setup is like this topology. We use three pairs of motes and synchronize their transmission. Calculate the packet drop ratio at the receiver of pair two and consider pair 1 and pair 3 as interfering pairs
The first: same channel, all three pairs,
Find, if using low power on the transmission of pair 2, drop ratio
Then: different channels,
We can see that even using low power at pair 2, the drop ratio is only about 3 percent
This perfectly justify our idea to use multi-channels

8. Related Works
Single channel real-time communication
Implicit EDF
Collision-free real-real time scheduling
SPEED
Enforcing uniform communication speed
None of them takes advantage of multiple channels
Multi-channel protocols and channel assignment
Node-based protocols
Requires channel switching
Interference free channel assignment
Requires synchronization
Transmission power control
Most do not deal with real-time requirement
Several existing works are related to our paper
There are some single channel real-time communication protocol
Such as Implicit EDF and SPEED, but None
Also several Multi-channel protocols and channel assignment has been proposed, but such as …….. But some of them require swtiching channels and others may have the synchronization requirement.
Moreover, some work deal with transmission power control to decrease the interference, but …….

9. Outline Multi-Channel Real-Time protocol (MCRT)
Network metric design
Flow based channel allocation
Power-efficient real-time routing (RPAR)
Disjoint path search algorithm
Experiment result
Conclusion
Based on these information, we propose our Multi-Channel Real-Time
I will introduce the 1. 2. 3.

10. Multi-Channel Real-Time Protocol
Multi-Channel Real-Time protocol (MCRT)
Especially designed for the real-time application in multi-channel WSNs
Designed to meet the end-to-end delay
Application traffic type: many to one communication
Major components:
Flow-based channel allocation
Power-efficient real-time routing
Contributions:
Formulate a constrained optimization problem
Propose a heuristic to solve the problem
Incorporate power efficient component
Our Multi-Channel Real-Time Protocol…..is especially
By proposing MCRT in the paper, we give the following contributions
First, ………………..for the multi-channel real-time service problem
Then, A heuristic algorithm to solve the problem is proposed
Moreover, we incorporate the power efficient component in our protocol
And finally we did massive simulations to illustrate the quality of our protocol

11. Network Metric Design Link weight calculation Worst-case one-hop delay
Link weight = Number of transmissions = 1/PRR
PRR : Packet Reception Ratio
3 Communication Relationships defined by PRR:
Communication link: PRR > 90%
Interference link: 90%>PRR>10%
Cannot communicate: PRR<10%
Worst-case one-hop delay
End-to-end delay:
Summation of the worst-case one-hop delay along a path
In order to formulate our problem, we first need to design the network metric
In our work, we use the 1/packet reception ratio as the link weight
Also based on the value of packet reception ratio, we can have 3 communication relationships.

12. Flow Based Channel Allocation
Channel assignment requirements:
Different channels for different data flows
Data flows are mutually disjoint
Disjoint Path with Bounded Delay problem (DPBD)
Directed graph G=(V,E) with weighted edges
K source vertices s1,…, sk and a destination vertex t
Goal:
Find k disjoint paths, one from each source si to t
All the path delay are bounded by a value W
DBPD is NP-complete
NP-completeness of DBPD can be proved by reducing it to MLBDP
MLBDP problem: Maximum Length Bounded Disjoint Path Problem
As we have the network metric finalized, we start to allocate channels for network. This is also the first main component of our protocol:
We have two requirement for our channel allocation.
First, we want to assign different channels for different data flows
Then it gives the second requirement
Different data flows are mutually disjoint

13. Power-Efficient Real-Time Routing
Real-time forwarding
Velocityrequired(s, d) =dist(s, d)/Tslack
Tslack: the time remaining until the packet deadline expires
Velocityprovided(n, p, c) =(dist(s, d) − dist(n, d))/delay(n, p, c)
s = current node, d = destination,
n = neighbor, p = power, c = n’s channel
Feasible next hop: Velocityprovided > velocityrequired
Power and neighborhood management
Power adaptation
If a set of neighbors are feasible, decrease power to transmit to the least power consumer
Increasing the power to transmit to the max velocity provider, if no neighbors are feasible
Neighborhood discovery
If the transmission to all neighbors are requiring max power, but still cannot meet deadline, start neighborhood discovery by using Routing Request (RR) packet d
Vrequired n
Vprovided s
The second component of our protocol is Power-Efficient Real-time Routing component, in short (RPAR).
In this component, we first deal with the real-time forwarding issue:
We calculate the required at the current node to the destination by the first formula, which is the distance between the current node to the destination devided by the slack time left in order to catch the deadline.
Then we calculate the provided velocity from a certain neighbor choice by using the second formula. It need to calculate the euclidean between the current node and the certain neighbor and then devided by the delay we estimated for that neighbor under a certain power
After we decided a neighbor choice, we will use a power adaptation scheme to manage the power consumption.

14. Outline Introduction Multi-channel real-time protocol
Disjoint Path Search Algorithm
Experiment result
Conclusion

15. Disjoint Path Search Algorithm
DBPD Problem: Directed graph with weighted edge, k sources and 1 destination
Find k disjoint paths, one from each source si to t
All the path delay are bounded by a value W
Disjoint path search algorithm includes two phases
Phase I: Initial solution set searching
To search an initial solution set with some disjoint paths
Dijkstra's shortest path algorithm is implemented
Phase II: Augmentation algorithm
Get as many disjoint paths as possible
Phase I can only provide an incomplete solution set by fast searching scheme
Depth first searching
Matching scheme to the existing solution
Phase II is iterative.
Every round of phase II will add one more new disjoint path to the solution set
Centralized algorithm

16. Phase II: Augmentation algorithmW X V S U C T B Y W X V S U C T B Y W X V S U C T B Y
DFS
Match
New Path W X V S U C T B Y W X V S U C T B Y W X V S U C T B Y W X V S U C T B Y
DFS/match
One more path

17. Disjoint Path Search Algorithm (cont’)
Analysis of the augmentation algorithm:
Time complexity: O(W’2|V||E|)
DFS: O(W’|E|)
Matching algorithm O(W’2|V||E|)
W’ is the edge number boundary
V – number of nodes
E – number of edges
Extended DBPD problem
Better fault tolerance
More energy efficient neighbors
to choose for real time forwarding
In our work, we also extend DPBD problem. We add some shadow sources to the real source. Then in the extended problem, we need to find out disjoint paths from all the sources including the shadow source. By doing this, we can get more than one paths for a certain data flow so that the network will have more fault tolerance and more energy efficient neighbor as the next hop forwarding choice.

18. Outline Introduction Multi-channel real-time protocol
Disjoint path search algorithm
Experiment result
Baseline design
Simulation result
Conclusion

19. Baseline Design SIMPLE Node-based scheme
A flow-based distributed heuristic to find disjoint path
Requires an initialization phase to establish path
Using explorer packet
Node-based scheme
Every node has a default listening channel
Node need to switch channel between receiving and transmitting
Real-time Power Aware Routing (RPAR)
Single channel real time protocol

20. Simulation Setup Simulation setup Major evaluation metrics
NS-2 simulation, based on the characteristic of Mica2 sensor mote
Probabilistic radio model from USC is implemented
130 nodes in a 150x150m2 square scenario, divided into 130 grids
Major evaluation metrics
Deadline miss ratio
The percentage of packet that is missing the required service deadline
Energy consumption per data packet
Energy required for each scheme to successfully finish a work load under a certain deadline requirement
Our experiment is conducted in NS-2 simulator, based on ……
The probabilistic radio model from University of South California is implemented, which was not included in NS2 originally.
The simulation uses 130 nodes in a 150 by 150 square meters scenario.
We design two evaluation metric for our experiment result. The first one is deadline miss ratio, which is the percentage of packet that is missing the required service deadline.
2nd

21. Simulation Result (cont’)
Performance with different deadlines
MCRT outperforms other baselines on all the different deadlines
Performance with different packet rate
MCRT shows low miss ratio and energy consumption

22. Simulation Result (cont’)
Performance with different number of flows
MCRT is not impacted significantly by number of flows
Performance for different network density
MCRT is not sensitive to density

23. Conclusion
The proposed MCRT protocol can effectively utilize the multiple channels for the many to one flow traffic pattern application
MCRT greatly reduces the deadline miss ratio compared with a single channel real-time protocol and two baselines
MCRT is the most energy efficient scheme among the four schemes
MCRT has good scalability compared with others

24. Critiques
No explicit end-to-end delay boundary is provided in the paper
No explicit analysis for the real-time property after the channel is assigned.
Worst case is a theoretical approach, but not realistic enough in real networks.

25. Department of EECS University of Tennessee, Knoxville
Exploiting Overlapping Channels for Minimum Power Configuration in Real-Time Sensor Networks
Good afternoon, my name is XXX. I am a 2nd year Ph.D. student at the University of Tennessee. This is joint work with another student, XXX, and my advisor, Dr. Xiaorui Wang.
Department of EECS University of Tennessee, Knoxville 25

26. Introduction Multi-channel application
Multiple channels supported by hardware
Limited orthogonal channels, adjacent channels are overlapping
Should we use overlapping channels?
More channels resources to use is a benefit.
The interference between overlapping channels is not negligible

27. Empirical Study for Overlapping Channel
Overlapping channel interference
Transmission pair uses Channel 16 and power level 15
Jammer pair uses Channel 15
Both pairs achieve good PRR when jammer use power level

28. Problem to Solve Problem: Goal:
A WSN with multiple data flows from different sources to the base station
Assign channels (including overlapping channels) to the data flows
Determine a transmission power level for every node
Goal:
To minimize overall (transmission) power consumption of the network
To guarantee average end-to-end delay of each data flow to stay within a deadline

29. Contributions Overlapping channel interference reality
Overlapping channels can be used for improving real-time performance
Empirical models
Received signal strength (RSS) vs. transmission power
Packet reception ratio (PRR) model
Power and channel configuration problem formulated
Constrained optimization problem formulated
Heuristic algorithm proposed
Testbed established
Experiment conducted on a 25-motes testbed.

30. Outline Introduction & contributions Related work review
Empirical modeling of overlapping channel
Overlapping Channel RSS Model
Packet Reception Model
Minimum transmission power configuration
Empirical Results
Conclusion

31. Interference Strength
The interference strength is decided by the received signal strength (RSS) from the interference transmission.
Higher interference signal strength -> more severe interference.
Signal strength is essentially decided by the transmission power at the sender
Overlapping Channel RSS Model
Sender uses Channel 16
Linear RSS vs. Power model

32. Packet Reception Model
Packet Reception Ratio (PRR)
Decided by Signal to Interference and Noise Ratio (SINR)
Relationship between SINR and RSS
Packet reception model
PRR-SINR-Channel relationship
(SINRv, cv, PRRv)

33. ? Problem Solving Flow Transmission Power RSS vs. Power
Real-time constrained power minimization problem
RSS
SINR vs. PRR
PRR

34. Outline Introduction & contributions Related work review
Empirical modeling of overlapping channel
Minimum transmission power configuration
Problem Formulation
Average PRR Estimation
Collision Probability Calculation
Algorithm Design
Empirical Results
Conclusion

35. Problem Formulation System assumption
Many-to-one traffic pattern
Each source generates a data flow independently
All flows are disjoint
Base station is assumed to be a super node, with multiple radio interfaces
Power optimization problem formulation
Subject to the constraints:
Average packet reception ratio
Soft real-time guarantees for Wireless Sensor Networks
Sources generate independent random flows

36. Average PRR Estimation
Average packet reception ratio estimation
Low probability for more than two nodes to transmit at the same time
Average packet reception ratio :
P(u,w) : probability that node w's transmission collide with node v's reception of its own sender u
PRR(u,v,w) : packet reception ratio of v's reception from its sender under w's interference
Example:
Case
Probability
PRR
1/PRR
No interference at v
25%
100% 1
Interference at v
75%
50% 2

37. Collision Probability Calculation
Probability of collision
Verification of the average PRR estimation

38. ? Problem Solving Flow Transmission Power RSS vs. Power RSS
SINR vs. PRR ?
Real-time constrained power minimization problem
Goal: most energy efficient power configuration
PRR

39. Algorithm Design The configuration search space is huge: jnkm
m nodes forming n flows in the network. The total available number of channels on the equipment is j. Each mote can use k different power levels to transmit.
Simulated Annealing (SA): probabilistic method for global optimization problems
Reason for using SA:
commonly used to find suboptimal solutions when the search space is huge and discrete

40. Algorithm Design (cont’)
Algorithm based on Simulated Annealing
Choose Tini, calculate
the Cini and Pini
Find neighbor state
ΔP > 0
Calculate ΔP
ΔP < 0
Accepted by Probability? No
Yes
Calculate Delayi
Meet Constraint? No
Add Penalty
Yes
Reduce Temperature T
Program Terminate? No
Yes
end

41. Problem Solving Flow + Transmission Power RSS vs. Power RSS
Simulated Annealing
SINR vs. PRR
Real-time constrained power minimization problem
Goal: Best power configuration
PRR +

42. Outline Introduction & contributions Related work review
Empirical modeling of overlapping channel
Minimum transmission power configuration
Empirical Results
Testbed and baselines
Four different set of experiments
Conclusion

43. Empirical Result Testbed setup and baselines
25 Tmote Invent motes are used
Two Baselines:
Orthogonal: orthogonal channels with simulated annealing power consumption algorithm
Random: Random channel assignment with simulated annealing power consumption algorithm

44. Different Delay Constraints
Topology I is used in this experiment
Three channels, 16, 17 and 18, are used.
Totally four flows are formed.

45. Different Delay Constraints (cont’)
Overlapping scheme achieves a smaller average end-to-end transmission count and power consumptions
Loose constraint gives larger search space leading to better performance

46. Different Packet Rates
Higher packet rate leads to increased retransmission count and power consumption for each packet.
higher degree of packet collision

47. Different Flow Numbers
Topology II is used in this experiment
Five channels, from 16 to 20, are used.
Flow number is increased by one for each time

48. Different Flow Numbers (cont’)
More data flows bring more interference into the network
More flows are sharing the same channels for data transmissions

49. Different Network Size
Topology III is used in this experiment
Five channels, from 16 to 20, are used.
Four flows are formed
Node number is increased by one in each flow for each time

50. Different Network Size (cont’)
More nodes result in larger end-to-end transmission count and power consumption
More inter-channel and intra-channel interference is incurred

51. Conclusion Interference between overlapping channels is not negligible
Overlapping channels can be utilized to reduce both end-to-end delay and power consumption with careful power configurations.

52. Critiques
The channel assignment algorithm is a static algorithm, which has a large overhead for gathering the measurement of signal strength and packet reception ratio.
The average packet reception ratio estimation only works under the proposed traffic pattern.
No complexity analysis for the simulated annealing algorithm

53. Comparison MCRT Overlapping Goal
Channel Assignment with Real-time Constraint
Power Minimization with Real-time Constraint
Channel Used
Orthogonal Channel Only
Overlapping Channels
Channel Assignment
Static
Evaluation
Simulation
Real Test-bed result