Introduction to Sensor Networks

Rabie A. Ramadan, PhDCairo University

http://rabieramadan.org rabie@rabieramadan.org

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Deployment, Clustering , and and Routing in WSN

Deployment Constraints Sensor characteristics

Monitored field characteristics

Monitored/probed object

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Deployment Parameters

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Deployment Parameters

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Deployment Parameters

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Deployment Parameters

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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

Random Deployment

Virtual Force Algorithm

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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

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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.

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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.

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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.

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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:

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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 =

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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

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Think….. If some sensors are stationary, does this

affect the virtual force algorithm?

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SENSOR REPLACEMENT BASED ENERGY MAPPING

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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.

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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)

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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

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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)

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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

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Think ……. What are the disadvantages of energy

mapping algorithm ?

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Movement-Assisted Sensor

Deployment

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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

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Voronoi Diagram Definition:

• Every point in a given polygon is closer to the node in this polygon than to any other node.

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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

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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 .

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Deterministic Deployment

Deployment Using Circle Packing

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Deployment Using Circle Packing Deployment of homogenous sensors

Full Coverage Deployment

Deployment of connected heterogeneous sensors

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Deployment of homogenous sensors

s Sensing range Density1 0.500000000000 0.785398163397 2 0.292893218813 0.539012084453 3 0.254333095030 0.609644808741 4 0.250000000000 0.785398163397 5 0.207106781187 0.673765105566 6 0.187680601147 0.663956909464 7 0.174457630187 0.669310826841 8 0.170540688701 0.730963825254 9 0.166666666667 0.785398163397 14 0.129331793710 0.73567925554316 0.125000000000 0.78539816339725 0.100000000000 0.78539816339736 0.083333333333 0.785398163397

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Full Coverage Deployment s Sensor’s sensing range (r) s Sensor’s sensing range (r)1 0.70710678118654752440 16 0.169427051598116023952 0.55901699437494742410 17 0.165680929570774725383 0.55901699437494742410 18 0.160639663597154535234 0.35355339059327376220 19 0.157841981746673756755 0.32616058400398728086 20 0.152246811233380310056 0.29872706223691915876 21 0.148953789551099321887 0.27429188517743176508 22 0.143693177121688000498 0.26030010588652494367 23 0.141244822387931359519 0.23063692781954790734 24 0.13830288328269767697

10 0.21823351279308384300 25 0.1335487065607704969311 0.21251601649318384587 26 0.1317648756148259646312 0.20227588920818008037 27 0.1286335345030996680713 0.19431237143171902878 28 0.1273175534656137214714 0.18551054726041864107 29 0.1255535079641135331715 0.17966175993333219846 30 0.12203686881944873607

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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

Sequential Packing-based Deployment Algorithm

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Sequential Packing-based Deployment Algorithm

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Potential Points

Think …..

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How do you guarantee connectivity ?

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Correctness of the Algorithm

Sink Placement Problem

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

Energy-Based Relocation -- MotivationSink node

Inactive Sensor

Active Sensor

One hop Sensor

Dead Sensor

Can repositioning the sink node help?

Normal Operational Mode: Sensors pursue multi-hop paths to

communicate with the sink nodeIssues: When the sink is stationary, nearby sensors

get involved in heavy packet forwarding and die quickly

• Nodes further away are picked as substitute relays

Consequence:• Increase in total transmission power rapid

energy depletion• Effect grows spirally outward

To where ?

Moving the Sink

Where to goTowards 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)

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AThe Sink nod directionis set to balance nodes

A and B's interestSink is placed onthe dotted arrow

C

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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?

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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

Relay Node Placement in WSNClustering Algorithms

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

LEACH Algorithm 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

The Amount of Energy Depletion This is the formula for the amount of energy

depletion by data transfer:

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

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.

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…

Think more ….. How to modify LEACH to include more

parameters such as node degree?

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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.

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 .

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.

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Think… How to solve Heed’s problems?

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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

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

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

Think… How mobility can affect DECA algorithm?

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Multimodal Limited Similarity Clustering (MFLC)

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.

MFLC single and multimodal sensor networks

Score Equation :

Data Similarity Clustering Based Fuzzy Logic (DSBF)

DSBF Phase One: Computing Node Degrees

Phase Two: Cluster Head Election

Phase Three: Data Reporting

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 Ss

Phase Two: Cluster Head Election

Routing in WSN

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

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

Sensor protocols for information via negotiation (SPIN)

Sensor protocols for information via negotiation (SPIN)• Operation process

Step1

ADV

Step3

DATA

Step2

REQ

Step4

ADV

Step5

REQ

Step6

DATA

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

Think…. In SPIN What about mobile nodes?

What about the multimodal Wireless nodes?

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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

Directed Diffusion (DD) Basic scheme

SinkSource

Step 1 : Interest propagation

Interests

Event

SinkSource

Step 2 : Initial gradients setup

Gradients

EventLow rate

SinkSource

Step 3 : Data delivery along reinforced path

Event

High rate

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

Think…. In DD What about mobile nodes?

What about the multimodal Wireless nodes?

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Comparison between SPIN, LEACH & Directed Diffusion

SPIN LEACH DirectedDiffusion

OptimalRoute

No No Yes

NetworkLifetime

Good Very good Good

ResourceAwareness

Yes Yes Yes

Use of meta-data

Yes No Yes

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

Rumor Routing

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

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.

Think…. In Rumor Routing What about mobile nodes?

What about the multimodal Wireless nodes?

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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

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

Think…. In Rumor Routing What about mobile nodes?

What about the multimodal Wireless nodes?

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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

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

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Think once more …. What are the problems of GAF?

What about mobile nodes?

What about the multimodal Wireless nodes?

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