Introduction
Trust terminologies
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I Trust you because of your good reputation.
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I Trust you despite your bad reputation.
Trust and reputation models in WSNs
Peer
Power
BTRM (Bio-inspired trust and reputation model)
LFTM
Related work
Preliminaries
System model
Non Data Aggregation | Data Aggregation | ||
---|---|---|---|
Leaf Nodes | 5 messages | Leaf Nodes | 5 messages |
Level 1 | 8 messages | Level 1 | 3 messages |
Level 2 | 9 messages | Level 2 | 1 message |
Total | 22 messages | Total | 9 messages |
mLFTM (Modified LFTM Trust Model)
Advantages of mLFTM over existing LFTM model
LDAT: LFTM based data aggregation and transmission protocol
Reliable data aggregation
Node ID (3) | Seq No.(4) | DEST(3) | SRC(2) | LEN(2) | DATA(0..56) |
Reliable path selection
Performance evaluation
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Percentage of client nodes: The percentage of nodes that want to send message to other nodes in the network and ask for services in a WSN.
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Percentage of relay nodes$$ =\left(\mathrm{No}\ \mathrm{of}\ \mathrm{relay}\ \mathrm{no}\mathrm{des}\ /\ \mathrm{Total}\ \mathrm{no}\ \mathrm{of}\ \mathrm{no}\mathrm{des}\right)*100. $$(4)
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Percentage of malicious nodes:$$ \left(\mathrm{No}\ \mathrm{of}\ \mathrm{adversaries}\ \mathrm{no}\mathrm{des}\ /\ \mathrm{Total}\ \mathrm{no}\ \mathrm{of}\ \mathrm{no}\mathrm{des}\right)*100. $$(5)
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Radio range: A distance within which the nodes are able to sense each other. Other sensors within the range of a node can be considered as its neighbours.
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Delay$$ {\mathrm{D}}_{\mathrm{T}} = \mathrm{N}\ /\ \mathrm{R} $$(6)where DT is the transmission delay, N is the number of bits, and R is the rate of transmission.
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Number of executions: The number of execution represents how many times the test runs.
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Number of networks: The number of WSNs simulated.
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Min./Max. Number of sensors: The minimum and maximum number of sensors in a random generated WSN.
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Trust and reputation model: Five trust and reputation models have been built into TRMSim-WSN 0.5: BTRM, Peer Trust Model, Eigen Trust Model, Power Trust, and LFTM.
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Accuracy: The selection percentage of trustworthy nodes :$$ \left(\mathrm{Number}\ \mathrm{of}\ \mathrm{successful}\ \mathrm{transmission}/\ \mathrm{total}\ \mathrm{number}\ \mathrm{of}\ \mathrm{message}\ \mathrm{transfers}\right)*100. $$(7)
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Path Length: The number of hops of the paths found by a trust and reputation system leading to the nearest trustworthy nodes.
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Energy Model: Consumption of the overall energy is sum of:1)Client nodes sending request messages;2)Server nodes sending response services;3)Energy consumed by malicious nodes, which provide bad services;4)Relay nodes which do not provide services; and5)The energy to run TRM executions.
NumExecution | 100 | % client | 20 % | ||||
NumNetworks | 100 | % Relay | 5 % | ||||
MinNumSensors | 100 | % malicious | {40 %, 60 %, 80 %, 95 %} | ||||
MaxNumSensors | 200 | ||||||
Radio range | 10 | phi | 0.01 | No. of ants | 0.35 | rho | 0.87 |
Niter | 0.59 | TraTh | 0.66 | PLF | 0.71 | alpha | 1.0 |
q0 | 0.45 | beta | 1.0 |
Evaluation of trust and reputation models
Accuracy with respect to selection percentage of trustworthy servers
20 % | 40 % | 60 % | 80 % | 95 % | |
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LFTM | 97.12 | 97.04 | 98.22 | 98.53 | 99.12 |
Peer | 99.75 | 99.35 | 97.74 | 81.79 | 57.23 |
Power | 99.74 | 99.24 | 98.44 | 97.4 | 96.39 |
BTRM | 99.7 | 99.2 | 98.57 | 96.89 | 88.92 |
20 % | 40 % | 60 % | 80 % | 95 % | |
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LFTM | 3.12 | 2.52 | 2.28 | 2.22 | 2.21 |
Peer | 6.46 | 6.84 | 6.68 | 6.66 | 6.61 |
Power | 6.7 | 6.87 | 6.73 | 6.8 | 6.85 |
BTRM | 2.24 | 2.33 | 2.42 | 3.03 | 6.29 |
Average path length leading to trustworthy servers
Energy consumption
20 % | 40 % | 60 % | 80 % | 95 % | |
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LFTM | 2.1*10^17.0 | 0.01*10^17.0 | 0.24*10^17.0 | 0.22*10^17.0 | 0.36*10^17.0 |
Peer | 0.15*10^17.0 | 0.10*10^17.0 | 0.17*10^17.0 | 0.28*10^17.0 | 0.26*10^17.0 |
Power | 16*10^17.0 | 24*10^17.0 | 29*10^17.0 | 24*10^17.0 | 22*10^17.0 |
BTRM | 0.21*10^17.0 | 0.58*10^17.0 | 0.22*10^17.0 | 12*10^17.0 | 21*10^17.0 |