8fault tolerance in collaborative sensor networks for target
Post on 29-May-2018
219 Views
Preview:
TRANSCRIPT
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
1/37
1
Fault Tolerance in CollaborativeSensor Networks for Target Detection
IEEE TRANSACTIONS ON COMPUTERS, VOL.53, NO. 3, MARCH 2004
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
2/37
2
Scenario Senor nodes
Fault-free
Faulty
Target Modeled by the signal
it emits
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
3/37
3
Sensors for Target Detection Sensor nodes obtain a target energy measurement after
T seconds while a target was at a given position inside oroutside the region R
Obtaining that energy requires preprocessing of the timeseries measured during period T
The detection algorithm consists of exchanging and
fusing the energy values produced by the sensor nodesto obtain a detection query answer (Value fusion)
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
4/37
4
Sensors for Target Detection Note that a more accurate answer can be obtained in
general if the sensors exchange their time series ratherthan energies
However, that would require high communicationbandwidth that may not be available in a sensor network
The performance of fusion is partly defined by the
accuracy that measures how well sensor decisionsrepresent the environment or ground truth.
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
5/37
5
Sensors for Target Detection Finally, this work assumes that detection results need to
be available at each node
The reason for such a requirement is that the results canbe needed for triggering other actions such aslocalization of the target detected
This requirement can be fulfilled by having a central
node make a decision and disseminate that decision toall the nodes in the network
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
6/37
6
Sensors for Target Detection The correctness of such a scheme relies on the central
nodes correctness, therefore, central node-basedschemes have low robustness to sensor failure
Distributing the decision making over several or all thenodes improves reliability at the cost of communicationoverhead
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
7/37
7
Fault Model Faults include misbehaviors ranging from simple crash
faults, where a node becomes inactive, to Byzantinefaults, where the node behaves arbitrarily or maliciously
In this work, faulty nodes are assumed to sendinconsistent and arbitrary values to other nodes duringinformation sharing
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
8/37
8
Fault Model
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
9/37
9
Fault Model The algorithm for target detection needs to be robust to
such inconsistent behavior that can jeopardize thecollaboration in the sensor network
For example, if the detection results trigger subsequentactions at each node, then inconsistent detection resultscan lead each node to operate in a different mode,resulting in the sensor network going out of service
The performance of fusion is therefore also defined byprecision. Precision measures the closeness of decisionsfrom each other, the goal being that all nodes obtain thesame decision
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
10/37
10
Target Energy
This study assumes no such obstacles to be present inthe region considered
Energy measurements at a sensor are usually corrupted
by noise
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
11/37
11
Distributed Detection (Data fusion) All local sensors communicate their data to a central
processor performing optimal or near optimal detection
Decentralized processing: some preliminary processing
of data is performed at each sensor node so thatcompressed information is gathered at the fusion center
Loss of performance, reduced communicationbandwidth
The performance loss of decentralized schemes maybe reduced by optimally processing the informationat each sensor node
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
12/37
12
Distributed Detection (Data fusion)
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
13/37
13
Binary hypothesis testing problem The observations at all the sensors either correspond to
the presence of a target (hypothesis H1) or to theabsence of a target (hypothesis H0)
The performance of detection is measured by the falsealarm probability PF and the probability of miss PM
The false alarm probability is the probability ofconcluding that a target is present when the target isabsent
The miss probability is the probability of concluding thata target is absent when a target is actually present
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
14/37
14
Neyman-Pearson criterion[27] Minimize the global probability of miss PM assuming that
the global probability of false alarm PF is below a givenbound
The thresholds used at each sensor and at the fusioncenter need to be determined simultaneously tominimize the PM under the constraint PF <
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
15/37
15
Agreement Problem and Fault Tolerance As processors in such a system cooperate to achieve a
specified task, they often have to agree on a piece ofdata that is critical to subsequent computation
Although this can be easily achieved in the absence offaulty processors, for example, by simple messageexchange and voting, special protocols need to be usedto reach agreement in the presence of inconsistent faults
Three problems have drawn much attention in trying todevelop these protocols, namely, the consensus problem,the interactive consistency problem, and the generalsproblem [1], [11]
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
16/37
16
Agreement Problem and Fault Tolerance The consensus problem considers n processes with initial
values xi and these processes need to agree on a valuey=f(x1,,xn), with the goal that each non-faulty processterminates with a value yi=y
The interactive consistency problem is like the consensusproblem with the goal that the non-faulty processesagree on a vector y=(y1,,yn) with yi=xi if process i isnon-faulty
The generals problem considers one specific processor,named general, trying to broadcast its initial value x toother processors with the requirement that all non-faultyprocesses terminate with identical values y and y=x ifthe general is non-faulty
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
17/37
17
Agreement Problem and Fault Tolerance A protocol is said to be t-resilient if it runs correctly
when no more than t out of N processes fail before orduring operation
The following results were derived regarding thegenerals problem under different assumptions
Theorem 1. There is a t-Byzantine resilient authenticationsynchronous protocol which solves the generals problem[18].
Theorem 2. There is a t-Byzantine resilient synchronousprotocol without authentication which solves the generalsproblem if and only if t/N < 1/3 [21].
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
18/37
18
Agreement Problem and Fault Tolerance A commonly used protocol for the generals problem,
namely, the oral message algorithm (OM), wasdeveloped in [18]. Whenever t out of N nodes are faulty,the OM(t) algorithm is guaranteed to provide agreement
among the N nodes if and only if N >= 3t+1
After the OM algorithm is performed for each node actingas general, inconsistent values sent by node C arereplaced by a common value (i.e., 10.0)
Note that, in this example, the final decisions of the non-faulty sensors are incorrect since the target is outside theregion of interest; however, the decisions are consistent.
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
19/37
19
Agreement Problem and Fault Tolerance Below is an example where a protocol for the generals
problem is used to reach interactive consistency amongthe four nodes of Fig. 2
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
20/37
20
Agreement Problem and Fault Tolerance In applications where processors hold an estimate of
some global value, it may be sufficient to guarantee thatthe nodes agree on values that are not exactly identicalbut are relatively close to one another. This was
formulated as the approximate or inexact agreementproblem[7][20][4]
Fewer messages exchanged among nodes at the cost ofdegraded precision
Some agreement protocols attempt to diagnose theidentity of faulty nodes so that diagnosis results can beused in subsequent agreement procedures[26]
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
21/37
21
Algorithms Different fusion algorithms can be derived by varying the
size of the information shared between sensor nodes
Value fusion where the nodes exchange their rawenergy measurements
Decision fusion where the nodes exchange localdetection decisions based on their energymeasurement [6]
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
22/37
22
Algorithms Value Fusion (with faults) at each node:
1. obtain energy from every node
2. drop largest n and smallest n values
3. compute average of remaining values
4. compare average to threshold for final decision
Decision Fusion (with faults) at each node:
1. obtain local decision from every node
2. drop largest n and smallest n decisions
3. compute average of remaining local decisions
4. compare average to threshold for final decision
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
23/37
23
Evaluation Metrics Precision
Measure the closeness of the final decisions obtained by allsensors, the goal being that all non-faulty nodes obtain thesame decision
Accuracy The goal being that the decision of non-faulty nodes is
object detected if and only if a target is detected (faultalarm probability and the detection probability)
Communication overhead This is not the focus of this paper and only qualitative
evaluations are made Robustness
System failure probability (when the faulty nodes exceedthe bound of tolerable faulty nodes)
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
24/37
24
Comparison of Algorithms Due to the reduced amount of information shared in
decision fusion, the communication cost is lower indecision fusion than in value fusion.
The system failure probability is identical for value anddecision fusion since failures depend on the number offaulty nodes present and not on the algorithm used.
However, the performance measured in terms ofprecision and accuracy differs from one option to theother.
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
25/37
25
Performance Value Fusion (without faults)
False alarm probability
False alarms occur when the average of the N values
measured by the sensors is above the threshold v inthe absence of target
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
26/37
26
Performance Value Fusion (without faults)
Detection probability
Detections occur when the average of the N valuesmeasured by the sensors is above the threshold
vin
the presence of target
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
27/37
27
Performance Value Fusion (with faults)
False alarm probability
t faults are present
Worst-case scenario (t sensors report the maximumallowed value)
n lowest values and n highest values are dropped sothat the decision is based on the N-2n middle-rangevalues
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
28/37
28
Performance Value Fusion (with faults)
Detection probability
t faults are present
Worst-case scenario (t sensors report the minimumallowed value)
Since the energy measured is a function of theposition of the sensor, the detection probabilitydepends on which sensors are faulty and which non-faulty sensor values are dropped
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
29/37
29
Performance
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
30/37
30
Performance
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
31/37
31
Simulation Results Although equations to evaluate the performance of value
and decision fusion were derived and validated for asmall number of nodes, they were found computationallyimpractical when the number of nodes exceed 20.
Simulation was therefore used to compare theperformance of the algorithms
Without Faulty Nodes
The superiority of value fusion over decision fusion
decreases as the decay factor increases
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
32/37
32
Simulation Results
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
33/37
33
Simulation Results With Faulty Nodes
In the presence of a target, a faulty node reports thelowest permissible value in value fusion and a localno detection in decision fusion, and vice versa.
The maximum power was increased substantially inthe presence of faults to obtain comparableperformance than in the absence of faults
Overall, faults have more impact on value fusion thanon decision fusion
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
34/37
34
Simulation Results
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
35/37
35
Simulation Results
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
36/37
36
Simulation Results
-
8/9/2019 8Fault Tolerance in Collaborative Sensor Networks for Target
37/37
37
Conclusion
The performance of localization and tracking algorithms,as opposed to detection algorithms, and the replacementof exact agreement by other agreement algorithms suchthat inexact or approximate agreement need to be
investigated
Methods for determining how to deploy the sensors inthe region of interest need to be developed to optimize
the detection performance
top related