Calculating trust in sensor networks

node si about node sj while monitoring the function f. σk is the success of observation
k, a negative observation would have a value −1 and a positive observation would have a
value of 1 with a scale of anything in between for partially successful actions. ρ(t, tk) is a
time dependent function that gives higher relevance to past values of σk.
Indirect trust in CORE works by neighbours sharing only their positive observations about
other nodes. This approach is chosen as a means to combat denial of service-attacks from
malicious nodes that could otherwise badmouth good nodes.
Functional reputation in CORE means a combined metric of both direct- and indirect
reputations according to function f. All of the functional reputations of a node can then
be combined into one global reputation-value that different weights to different functional
reputations. Each node must maintain a Reputation table where it stores the direct- and
indirect reputations of each observable function for every neighbouring node.
The final trust-value is defined as such:
reputation t si = � wk ∗ (directreputation t si (sj | fk) + indirectreputation t si (sj | fk)
Where wk is the weight associated to the functional reputation of function fk.
The article does not specifically define which sort of behaviour (or functions) nodes should
monitor about their neighbours. It does suggest however, that packet forwarding and
routing functions should be monitored to ensure efficient operation of the network, with
more weight given to packet forwarding.
Instead of a proprietary monitoring scheme, each node in CORE uses the Watchdog-
mechanism [MGLB00] to validate other nodes behaviour. Watchdog is summoned every
time the correct execution of a function by another node must be verified. It does this by
storing all expected behaviour to a buffer. It then compares the observered behaviour to
this buffer and removes the entry in case the expected and observed behaviours match.
This is also marked as positive behaviour and the reputation table is updated accordingly.
If an entry stays in the buffer for too long, it is marked as a negative observation and also
marked in the reputation table.
An entry is stored in the reputation table for each observed function of other nodes. A
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