1. Presented By:
Pavithra R.
III M C A
Mangalore University
2. CONTENTS
• INTRODUCTION TO WSN
• FAULT MANAGEMENT MECHANISM FOR WSN
• FAULT DETECTION AND DIAGNOSIS
• FAULT RECOVERY
• NETWORK AND FAULT MODEL
• FAULTY SENSOR DETECTION
• CONCLUSION
• FUTURE SCOPE
3. INTRODUCTION
A wireless sensor network is a collection of sensor nodes
organized into a cooperative network
• WSN are used to collect data from the environment.
• A sensor network consists of multiple detection
stations called sensor nodes, each of which is small,
lightweight and portable.
• The nodes in the network are connected via Wireless
communication channels.
• Each node has capability to sense data, process the
data and send it to rest of the nodes or to Base
Station.
• These networks are limited by the node battery
lifetime.
4. Every sensor node is equipped with a transducer,
microcomputer, transceiver and power source.
The transducer generates electrical signals based on
sensed physical effects and phenomena. The
microcomputer processes and stores the sensor output.
The transceiver, which can be hard-wired or wireless,
receives commands from a central computer and
transmits data to that computer. The power for each
sensor node is derived from the electric utility or from a
battery.
Wireless sensor networks (WSN) usually have limited
energy and transmission capacity, which can't match the
transmission of a large number of data collected by
sensor nodes.
5. WSN ARCHITECTURE
Sensor Node
Gateway
Base Station
Wireless Sensor Network Architecture
6. Fault Management Mechanism for WSN
In this approach a new fault management mechanism
was proposed to deal with fault detection and recovery.
It proposes a hierarchical structure to properly distribute
fault management tasks among sensor nodes by heavily
introducing more self-managing functions.
The proposed fault management mechanism can be
divided into two phases:
Fault detection and diagnosis
Fault recovery
7. Fault Detection and Diagnosis
Detection of faulty sensor nodes can be achieved by two
mechanisms i.e. self-detection (or passive-detection) and
active-detection.
In self-detection, sensor nodes are required to
periodically monitor their residual energy, and identify the
potential failure.
In this scheme, we consider the battery depletion as a
main cause of node sudden death. A node is termed as
failing when its energy drops below the threshold value.
Self-detection is considered as a local computational
process of sensor nodes, and requires less in-network
communication to conserve the node energy.
To efficiently detect the node sudden death, fault
management system employed an active detection
mode.
8. In active detection, cell manager asks its cell members
on regular basis to send their updates. Such as the cell
manager sends “get” messages to the associated
common nodes on regular basis and in return nodes
send their updates. This is called in-cell update cycle.
The update_msg consists of node ID, energy and
location information.
The exchange of update messages takes place between
cell manager and its cell members. If the cell manager
does not receive an update from any node then it sends
an instant message to the node .
If cell manager does not receive the acknowledgement in
a given time, it then declares the node faulty and passes
this information to the remaining nodes in the cell.
9.
10. Fault Recovery
After nodes failure detection (as a result of self-detection
or active detection), sleeping nodes can be awaked to
cover the required cell density or mobile nodes can be
moved to fill the coverage hole.
A cell manager also appoints a secondary cell manager
within its cell to acts as a backup cell manager. Cell
manager and secondary cell manager are known to their
cell members.
If the cell manager energy drops below the threshold
value (i.e. less than or equal to 20% of battery life), it
then sends a message to its cell members including
secondary cell manager.
11. This is an indication for secondary cell manager to stand
up as a new cell manager and the existing cell manager
becomes common node and goes to a low computational
mode.
Common nodes will automatically start treating the
secondary cell manager as their new cell manager and
the new cell manager upon receiving updates from its cell
members; choose a new secondary cell manager.
The failure recovery mechanisms are performed locally
by each cell.
14. Network model and Fault model
Sensors are randomly deployed in the interested area
which is very dense and all the sensors have a common
transmission range.
Depending on majority voting among the sensors, we
assume that each sensor node has at least 3 neighboring
nodes.
Because a large amount of sensors are deployed into the
interested area to form a wireless network, this condition
can be easily obtained.
Each sensor node is able to locate its neighbors within its
transmission range via a broadcast/ acknowledge
protocol. Faults can occur at different levels of the sensor
network such as system software, hardware, physical
layer, and middleware.
15. In this mechanism, we focus on hardware level faults by
assuming all system software as well as the application
software is always fault tolerant.
We can categorize the hardware components of sensor
nodes into two groups.
The first group of hardware level components consists of
a storage subsystem, computation engine and power
supply infrastructure.
The second groups of components are sensors and
actuators.
Sensor nodes are still capable of receiving, sending, and
processing when they are faulty in the algorithm.
16. Faulty Sensor Detection
Definition:
n : total number of sensors;
p : probability of failure of a sensor;
k : number of neighbor sensors;
S : set of all the sensors;
N ( Si ) : set of the neighbors of Si;
xi : measurement of Si;
t
d ij : measurement difference between Si and Sj at time t ,
d ij = xit − x tj ;
t
17. Faulty Sensor Detection (cont.)
Δtl = tl + 1 − tl;
Δd ij tl : measurement difference between Si and Sj from
Δ
time tl to tl + 1, Δd ij tl = d ijl +1 − d ijl = ( xitl +1 − x tjl +1 ) − ( xitl − x tjl );
Δ t t
cij : test between Si and Sj , cij ∈{0, 1}, cij = cji;
θ1 and θ 2 : two predefined threshold values;
Ti : tendency value of a sensor, Ti ∈{LG, LF, GD, FT};
18. Faulty Sensor Detection (cont.)
Algorithm
Step 1:
Each sensor Si, set cij = 0 and compute d ij ;
t
IF | d ij | > θ 1 THEN
t
Calculate Δd ij tl ;
Δ
IF | Δd ij tl | > θ 2 THEN cji = 1;
Δ
i cij = 1 j
xit x tj
xit +1 x tj+1
19. Faulty Sensor Detection (cont.)
Step 2:
IF ∑Sj ∈ N ( Si ) cij ≤ | N ( Si ) | /2 , where | N ( Si ) | is
the number of the Si ' s neighboring nodes THEN
Ti = LG;
ELSE Ti = LF;
Communicate Ti to neighbors; 2
c42 = 1
LF
1
c41 = 1
c43 = 0
4 3
20. Faulty Sensor Detection (cont.)
Step 3:
IF ∑Sj ∈ N ( Si ) and Tj = LG (1 − 2 cij ) ≥ | N ( Si ) | / 2
THEN
Ti = GD;
Communicate Ti to neighbors; 5
LG
c65 = 0
1 c61 = 0 GD
LG c64 = 0
6 4
c62 = 0 c63 = 0 LG
LF 2
21. Faulty Sensor Detection (cont.)
Step 4:
FOR i = 1 to n FT 2
IF Ti = LG or Ti = LF THEN
c32 = 1
IF Tj = GD ∀Sj ∈ N ( Si ) THEN
IF cij = 0 THEN GD
Ti = GD;
c31 = 0
ELSE Ti = FT;
ELSE repeat GD 1
Communicate Ti to neighbors;
22. Faulty Sensor Detection (cont.)
Step 5:
FOR each Si, IF Tj = Th = GD
∀Sj , Sh ∈ N ( Si ), where j ≠ h,
and IF cji ≠ chi THEN
IF Ti = LG (or LF) THEN
Ti = GD (or FT)
GD
1 c31 = 1 GD
c21 = 0
2
3 LG or LF
23. Faulty Sensor Detection (cont.)
LG LG LG
LG
0 0 1 1
1
LG LF
0 1 0 LG
0 1
LF
LG LG LG
0 1 0 1 1
1 LG
1 LF 1 LG 0
0 0
LF 1 1
LG 0 1
LG 1 LG
1 0 0
LG
LG
2008/10/01 23
24. Faulty Sensor Detection (cont.)
LG LG LG
LG
0 0 1 1
1
GD FT
LF
0 1 0 LG
0 1
FT
LF
LG GD GD
0 1 0 1 1
1 LG
1 FT
LF 1 GD 0
0 0
FT
LF 1 1
LG 0 1
GD 1 LG
1 0 0
LG
LG
2008/10/01 24
25.
26. CONCLUSION
In a faulty sensor detection algorithm where each sensor
identifies its own status to be either ”good” or ”faulty” and
the claim is then supported or reverted by its neighbors
as they also evaluate the node behavior.
The probabilities of faulty sensors being diagnosed as
“good” and good sensors not being diagnosed as “good”
are very low.
27. Future Scope
In future we intend to calculate the detection accuracy for
the nodes in the Wireless Sensor Network where
detection accuracy depicts the ratio of the number of
faulty sensors detected to the total number of faulty
sensors in the network. The time consumed by approach
to find out the faulty node is relatively less. So we want to
verify it for larger number of nodes.
