1. Introduction to Sensor Networks
Rabie A. Ramadan, PhD
Cairo University 2

2. Deployment, Clustering , and and Routing in WSN

3. Deployment Constraints
Sensor characteristics
Monitored field characteristics
Monitored/probed object

4. Deployment Parameters

5. Deployment Parameters
Diffraction: passing the signal through small opining and spreading it after passing the opening
Scattering: scatter the coming signal
Reflection : send the signal back to towards the sender

6. Deployment Parameters

7. Deployment Parameters

8. Deployment Problems and Solutions
Random Deployment
Virtual force Algorithm
Deterministic Deployment
Circle Packing
Energy Mapping
Movement-Assisted Sensor Deployment
Sink Placement Problem
Single node
Multiple sink deployment
Relay Node Placement in WSN

9. Random Deployment Virtual Force Algorithm

10. Virtual Force Algorithm
Sensors are initially deployed randomly
Objective:
To maximize the Coverage
Assume no prior knowledge about the monitored field
All nodes are mobile
Energy and obstacles might present in the field

11. Virtual Force Algorithm (Cont.)
Attractive and Repulsive forces
Sensors do not physically move
A sequence of virtual motion paths is determined for the randomly placed sensors.
Once the effective sensor positions are identified, a one-time movement is carried out to redeploy the sensors at these positions.

12. Virtual Force Algorithm (Semi Distributed.)
Assumptions:
Clustered network
All clustered heads are able to communicate with the sink node
The cluster head is responsible for executing the VFA and managing the one-time movement of sensors to the desired locations.

13. Virtual Force Algorithm (Cont.)
Each sensor behaves as a “Source of force” for all other sensors.
This force can be either positive (Attractive) or negative (Repulsive).
The closeness and wide distance between two sensors are measured using a predefined threshold.

14. Virtual Force Algorithm (Cont.)
Sensor Binary Model
Consider an n by m sensor field grid and assume that there are k sensors deployed in the random deployment stage.
Each sensor has a detection range r. Assume sensor si is deployed at point (xi , yi ).
For any point P at (x, y), we denote the Euclidean distance between si and P as d(si , P),
The coverage of a Grid Point P can be expressed by:

15. Virtual Force Algorithm (Cont.)
Virtual Forces
Attraction force  F12
Repulsive force  F13
Zero Force  F14
Obstacle Force 
preferential coverage
Force 
Total Force on node i =

16. Virtual Force Algorithm (Cont.)
Energy Constraints
Using such forces , the cluster head runs the VFA
After stability occurs , Sensors are ordered to move to the new positions
Energy and Obstacles might be problems
Any sensor will not be able to move the required distance , the moving order is discarded
Obstacles need an obstacle avoidance algorithm

17. Think…..
If some sensors are stationary, does this affect the virtual force algorithm?

18. SENSOR REPLACEMENT BASED ENERGY MAPPING

19. The problem
A set of sensors S is deployed in a monitored field F(A)for a period of time T.
The field is divided into a grid of cells A.
Each cell is assigned a weight where represents the importance of the cell i.
The location of each sensor is assumed known;
More than one sensor could be deployed in one cell.
Sensors are assumed heterogeneous in terms of their energy and mobility.

20. Assumptions A sensor could be in different states;
it could have its sensing off or on based on the field monitoring requirements.
Sensing off, radio off – (sleep mode)
Sensing off, radio receiving – (Receiving mode)
Sensing off, radio transmitting – (Routing mode)
Sensing on, radio receiving – (Sensing and Receiving mode)
Sensing on, radio transmitting – (Sensing and Transmitting mode)
Sensing on, radio off (Sensing mode)

21. The main idea Knowing the energy map of the network :
May lead to early detection to the uncovered areas.
Redeploy new sensors
Turn off some of the sensors due to their coverage redundancy
Wake up some of the nodes when needed
Move one or mobile nodes to cover the required uncovered spots

22. Redeployment based Energy map
Step 1: Energy dissipation rate prediction
Each sensor predicts its own energy rate based on its history (e.g. Markov Chain ..)
Step 2: sensors send their initial energy and the location, predicted energy dissipation rate to the sink node through a cluster head.
Sensors update their energy dissipation rate based on a specific threshold (if the new dissipation rate increased more than the given threshold , the node sends the new dissipation rate)

23. Redeployment based Energy map
Step 3: the sink node constructs the energy map based on the received dissipated energy rate from the sensors.
The sink may move one of the mobile sensors to the uncovered spot or wake up one of the sleeping sensors

24. Think ……. What are the disadvantages of energy mapping algorithm ?
- Sensor network is an event based network . Therefore , events are not frequently or based on specific pattern. Thus, the amount of messages to be transmitted to report the energy mapping will not be expected and might play a role in sensors energy dissipation.
Centralized algorithm

25. Movement-Assisted Sensor Deployment

26. The problem of sensor deployment
Given the target area, how to maximize the sensor coverage with less time, movement distance and message complexity
The importance of the problem
Distributed instead of centralized

27. Voronoi Diagram Definition:
Every point in a given polygon is closer to the node in this polygon than to any other node.

28. Overview of the proposed algorithm
Sensors broadcast their locations and construct local Voronoi polygons
Find the coverage holes by examine Voronoi polygons
If holes exist, reduce coverage hole by moving
VOR : VORonoi-based
Pull sensors to the sparsely covered area

29. Part of Assignment 2
Implement both Virtual Force algorithm and Voronoi based algorithm ? Report your experience and algorithms efficiency?
Given a set of sensors with limited amount of energy. Some of these sensors are assumed mobile and others are assumed stationary. Assume similar sensing and communication ranges for all sensors. Sensors are allowed to move from one place to another iff they have enough energy to move to the required destination. In addition , the borders of the monitored area is assumed known in terms of 2D coordinates. Borders may be found in the monitored area. Advice a suitable deterministic deployment algorithm for efficient deployment to the sensors given that the deployed sensors have to be connected and important areas in the field are covered. In addition , your algorithm must guarantee the coverage of the monitored field for certain period of time.
You may look for an already given solution or come up with a convincing one .

30. Deterministic Deployment
Deployment Using Circle Packing

31. Deployment Using Circle Packing
Deployment of homogenous sensors
Full Coverage Deployment
Deployment of connected heterogeneous sensors 31

32. Deployment of homogenous sensors
Sensing range
Density 1 2 3 4 5 6 7 8 9 14 16 25 36 32

33. Full Coverage Deployments
Sensor’s sensing range (r) 1 16 2 17 3 18 4 19 5 20 6 21 7 22 8 23 9 24 10 25 11 26 12 27 13 28 14 29 15 30 33

34. Sequential Packing-based Deployment Algorithm (SPDA)
Given
Sensors Sensing Ranges
Sensors Communication Ranges
Bounded Monitored Field
Objective
Best Connected Deployment Scheme
Max. Coverage.
Min. Overlapped Areas
Benefit from the properties learned from the optimal deployment 34

35. Sequential Packing-based Deployment Algorithm

36. Sequential Packing-based Deployment Algorithm

37. Potential Points 37

38. How do you guarantee connectivity ?
Think …..
How do you guarantee connectivity ?

39. Correctness of the Algorithm39

40. Sink Placement Problem

41. Potential benefits of sink relocation
Increased network longevity: shortened data paths can safe the total energy consumed to data collection and extend the life of relaying nodes.
Improved timeliness: involves fewer relays leading to avoidance of large packet backlogs
Enhanced safety: moves the sink away from harmful events without damaging network performance

42. Energy-Based Relocation -- Motivation
Normal Operational Mode:
Sensors pursue multi-hop paths to communicate with the sink node
Issues:
When the sink is stationary, nearby sensors get involved in heavy packet forwarding and die quickly
Sink node
Inactive Sensor
Active Sensor
One hop Sensor
Dead Sensor
Can repositioning the sink node help?
To where ?
Nodes further away are picked as substitute relays
Consequence:
Increase in total transmission power  rapid energy depletion
Effect grows spirally outward

43. Moving the Sink Where to go
Towards the region, whose sensors generate the most number of packets
Centroid of the last-hop nodes that route the largest traffic (use a distance * traffic metric)

44. Think….
What about putting the sink node initially in the center of all nodes? Does this will be the best position for the sink node?
No , because sensor networks again are event based networks

45. Part of your assignment
Device an algorithm for Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks?
You may look at :
E. Ilker Oyman and Cem Ersoy, Multiple Sink Network Design Problem in Large Scale Wireless Sensor Networks,, IEEE International Conference on Communications, 2004

46. Relay Node Placement in WSN Clustering Algorithms

47. Clustering Facts
Clustering plays a dominant role in delaying the first node death, while aggregation plays a dominant role in delaying the last node death
In each cluster one node acts as a cluster head which is in charge of coordinating with other cluster heads

48. LEACH Algorithm LEACH is considered as clustering and routing protocol
The LEACH Network is made up of nodes, some of which are called cluster-heads
The job of the cluster-head is to collect data from their surrounding nodes and pass it on to the base station
LEACH is dynamic because the job of cluster-head rotates
LEACH is considered as clustering and routing protocol
LEACH (Low-Energy Adaptive Clustering Hierarchy)

49. The Amount of Energy Depletion
This is the formula for the amount of energy depletion by data transfer:

50. LEACH’s Two Phases
The LEACH network has two phases: the set-up phase and the steady-state
The Set-Up Phase
Where cluster-heads are chosen
The Steady-State
The cluster-head is maintained
When data is transmitted between nodes

51. Stochastic Threshold Algorithm
Cluster-heads can be chosen stochastically (randomly based) on this algorithm:
If n < T(n), then that node becomes a cluster-head
The algorithm is designed so that each node becomes a cluster-head at least once.
R  round number

52. Deterministic Threshold Algorithm
A modified version of this protocol is known as LEACH-C (or LEACH Centralized)
This version has a deterministic threshold algorithm, which takes into account the amount of energy in the node…

53. Think more …..
How to modify LEACH to include more parameters such as node degree?

54. HEED: Hybrid Energy Efficient Distributed Clustering
HEED was designed to select different cluster heads in a field according to the amount of energy that is distributed in relation to a neighboring node.
Four primary goals:
prolonging network life-time by distributing energy consumption
terminating the clustering process within a constant number of iterations/steps
minimizing control overhead
producing well-distributed cluster heads and compact clusters.

55. Heed Algorithm
Each node performs neighbor discovery, and broadcasts its cost to the detected neighbors.
Each node sets its probability of becoming a cluster head, Chprob , as follows:
Where, Cprob is the initial percentage of cluster heads among n nodes (it was set to 0.05),
Eresidual and Emax are the residual and the maximum energy of a node (corresponding to the fully charged battery), respectively.
The value of CHprob is not allowed to fall below the threshold pmin .

56. Disadvantage (LEACH and HEED) – think….
Nodes’ score is computed based on node identifiers, and each node holds its message transmission until all its neighbors with lower IDs have done so.
Each node stops its protocol execution if it knows that every node in its neighborhood has transmitted.
It is assumed that the network topology does not change during the algorithm execution, and it is thus valid for each node to wait until it overhears every higher-scored neighbor transmitting.

57. Think…
How to solve Heed’s problems?

58. HEED Assignment
Previous Algorithm is used with homogenous sensors (all have the same characteristics ).
Device another clustering algorithm for heterogeneous WSN (nodes with different capabilities) .
You may have a look at the following paper
Harneet Kour and Ajay K. Sharma, “Hybrid Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network, ” International Journal of Computer Applications (0975 – 8887) Volume 4 – No.6, July 2010
the score is computed based on node identifiers,
and each node holds its message transmission until all its
neighbors with lower IDs have done so. Each node stops
its protocol execution if it knows that every node in its
neighborhood has transmitted. It is assumed that the
network topology does not change during the algorithm
execution, and it is thus valid for each node to wait until
it overhears every higher-scored neighbor transmitting.

59. Mobility Resistant Clustering in Multi-Hop Wireless Networks --- Distributed Efficient Clustering Approach (DECA) ---

60. DECA
Each node periodically transmits a Hello message to identify itself, and based on such Hello messages, each node maintains a neighbor list.
Define for each node the score function as:
Where E stands for the node residual energy, C stands for the node connectivity, I stands for the node identifier, and the weights follow
The computed score is then used to compute the delay for this node to announce itself as the cluster head. The higher the score, the sooner the node will transmit.
The computed delay is normalized between 0 and a certain upper bound Dmax

61. Think…
How mobility can affect DECA algorithm?

62. Multimodal Limited Similarity Clustering (MFLC)

63. MFLC for single and multimodal sensor networks
A single feature sensor network is a network with each sensor node reports only one feature.
Multimodal sensor network is a network with nodes report more than one feature.
MFLC adapts LEACH clustering technique to support the multimodal sensor networks.
MFLC differs from the LEACH on the criteria used for a node to decide to be a cluster head or not.

64. MFLC single and multimodal sensor networks
Score Equation :

65. Data Similarity Clustering Based Fuzzy Logic (DSBF)

66. DSBF Phase One: Computing Node Degrees
Phase Two: Cluster Head Election
Phase Three: Data Reporting

67. Phase One: Computing Node Degrees
The node degree based similarity feature is computed
The node degree in this context means the number of similar sensors around

68. Phase Two: Cluster Head Election

69. Routing in WSN

71. Flat Routing Each node plays the same role Data-centric routing
Due to not feasible to assign a global id to each node
Save energy through data negotiation and elimination of redundant data
Protocols
Sensor Protocols for Information via Negotiation (SPIN)
Directed diffusion (DD)
Rumor routing
Minimum Cost Forwarding Algorithm (MCFA)
Gradient-based routing (GBR)
Information-driven sensor querying/Constrained anisotropic diffusion routing (IDSQ/CADR)
COUGAR
ACQUIRE
Energy-Aware Routing
Routing protocols with random walks

72. Sensor protocols for information via negotiation (SPIN)
Features
Negotiation
to operate efficiently and to conserve energy
using a meta-data
Resource adaptation
To extend the operating lifetime of the system
monitoring their own energy resources
SPIN Message
ADV – new data advertisement
REQ – request for ADV data
DATA – actual data message
ADV, REQ messages contain only meta-data

73. Sensor protocols for information via negotiation (SPIN)
Operation process
Step1
ADV
Step3
DATA
Step2
REQ
Step4
Step5
Step6

74. Sensor protocols for information via negotiation (SPIN)
Resource adaptive algorithm
When energy is plentiful
Communicate using the 3-stage handshake protocol
When energy is approaching a low-energy threshold
If a node receives ADV, it does not send out REQ
Energy is reserved to sensing the event
Advantage
Simplicity
Each node performs little decision making when it receives new data
Need not forwarding table
Robust to topology change
Drawback
Large overhead
Data broadcasting

75. Think…. In SPIN What about mobile nodes?
What about the multimodal Wireless nodes?

76. Directed Diffusion (DD)
Feature
Data-centric routing protocol
A path is established between sink node and source node
Localized interactions
The propagation and aggregation procedures are all based on local information
Four elements
Interest
A task description which is named by a list of attribute-value pairs that describe a task
Gradient
Path direction, data transmission rate
Data message
Reinforcement
To select a single path from multiple paths

77. Directed Diffusion (DD)
Basic scheme
Sink
Source
Step 2 : Initial gradients setup
Gradients
Event
Low rate
Sink
Source
Step 1 : Interest propagation
Interests
Event
Sink
Source
Step 3 : Data delivery along reinforced path
Event
High rate

78. Directed Diffusion (DD)
Advantage
Small delay
Always transmit the data through shortest path
Robust to failed path
Drawback
Imbalance of node lifetime
The energy of node on shortest path is drained faster than another
Time synchronization technique
To implement data aggregation
Not easy to realize in a sensor network
The overhead involved in recording information
Increasing the cost of a sensor node

79. Think…. In DD What about mobile nodes?
What about the multimodal Wireless nodes?

80. Comparison between SPIN, LEACH & Directed Diffusion
Optimal
Route No
Yes
Network
Lifetime
Good
Very good
Resource
Awareness
Use of meta-data

81. Rumor Routing Feature Combine query flooding and event flooding
Discovering arbitrary paths instead of the shortest path
Rumor routing is attractive only when
The number of queries is larger than a threshold
The number of events is smaller than another threshold
Assumption
The network is composed of densely distributed nodes
Only short distance transmissions
Immobile nodes

82. Rumor Routing Basic scheme Each node maintains A lists of neighbors
An event table
When a node detects an event
Generate an agent
Let it travel on a random path
The visited node form a gradient to the event
When a sink needs an event
Transmit a query
The query meets some node which lies on the gradient
Route establishment

83. Rumor Routing
The node sensing an event probabilistically generates an agent.
In order to propagate directions to the event as far as possible in the network, a straightening algorithm is used
The agent maintains a list of recently seen nodes.
When picking its next hop, it will first try nodes not in the list.

84. Think…. In Rumor Routing
What about mobile nodes?
What about the multimodal Wireless nodes?

85. Minimum Cost Forwarding Algorithm (MCFA)
Objective
Establish the cost field
Transmit the data through the minimum-cost path
Feature
Optimality
Minimum cost path criteria : hop count, energy consumption, delay etc.
Simplicity
Need not to maintain forwarding table
Need not to know an ID for a neighbor node

86. Minimum Cost Forwarding Algorithm (MCFA)
Operation process
Each node stores its cost to the sink
The sink broadcasts an ADV message
containing its own cost (0 initially)
Each node receiving the message transmits neighbor node
Add the cost in ADV message to its own cost
The cost field is set up
after the ADV message propagates through the network
The source transmits an information through cost field
Drawback
Limited network size
The time to set the cost field is directly proportional to the size of the network
Load is not balanced

87. Think…. In Rumor Routing
What about mobile nodes?
What about the multimodal Wireless nodes?

88. Geographic Adaptive Fidelity (GAF)
Forms a virtual grid of the covered area
Each node associates itself with a point in the grid based on its location
Nodes associated with same point in grid are considered equivalent
Some nodes in an area are kept sleeping to conserve energy
Nodes change state from sleeping to active for load balancing

89. Creating a Virtual Grid
Use location information (GPS) to create a virtual grid
All nodes in a grid are equivalent
Only one node from a grid point is active at a time
All nodes in a grid point is within the radio range of nodes in adjacent grids
Virtual grid results in hierarchical clusters of nodes

90. Think once more …. What are the problems of GAF?
What about mobile nodes?
What about the multimodal Wireless nodes?
Depends on location information in grid creation.
Scheduling and node synchronization are required