Calculating trust in sensor networks

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more detailed monitoring scheme will more likely detect different kinds of attacks. A trust-based routing-algorithm might only use first-hand observations (direct trust) about it neighbours or it might gather third-hand observations (indirect trust) from other nodes. Indirect trust adds another layer of complications as the observations can also be from untrusted nodes. Some protocols do not employ indirect trust at all, while some weigh recommendations against what is known about the recommender. Some protocols also use active notifications in situations where they discover an untrustworthy node. These notifications usually warn other nodes about a bad node. The following details different trust-based routing protocols, their components, how they define trust and how they monitor their surroundings along with details about how trust is used in routing decisions. 3.2 Confidant Confidant [BB02] is a distributed trust manager that cooperates with the routing layer. Confidant is based on the Dynamic Source Routing protocol (DSR). Confidant consists of 4 components: the monitor, the trust monitor, the reputation system and the path manager. The monitor is in charge of noticing unwanted behaviour in neighbouring nodes and it reports deviations to the reputation system. The reputation system maintains a list of nodes and their reputations. The system changes a nodes reputation only when the monitor notices significant bad behaviour that occurs for a long period of time. The reputation system then modifies the reputation for the node depending on the type of bad behaviour that was monitored. The trust manager component sends out and deals with ALARM-messages. These messages are used by nodes to notify about untrustworthy nodes. An ALARM-message defines the type of misbehaviour that was noticed, how long this has occured and which node is in question. A node will send out and ALARM when it observes or is alarmed by an untrustworthy node. To deal with false messages, Confidant uses a sort of friends list 20
of trustworthy nodes. These are nodes that a node trusts and is willing to share trust information. Confidant identifies friends on four levels from no information to complete trust and uses this information to weigh received ALARM-messages. The path manager keeps a list of known routes. These routes are established according to the DSR protocol by sending route requests and route replies. Normally DSR chooses the route that receives the fastest or the shortest replies. Confidant adds trust considerations to the decision, although the article doesn’t [BB02] define how. If the reputations system notices enough bad behaviour about a node to raise suspicion, the path manager deletes all the routes that have this node. If the path manager receives route requests from malicious nodes, it may drop the requests. The path manager also re-arranges routes according to the ALARM-messages the trust manager receives. 3.3 CORE CORE [MM02a] defines itself as a generic method that aims to enforce node co-operation in ad hoc networks. It incorporates a monitoring scheme to gather trust-information about neighbouring nodes. This trust-information can in turn be used to weigh packet forwarding, route discovery, network management or any other decisions in regards to node co-operation. The article defines a way in which the DSR routing protocol can use this trust-information in route discovery. CORE defines trust as a product of direct-, indirect- and what it calls functional reputation. It calculates direct- and indirect trusts according to different functions, or observable traits of nodes and then combines these values to a single trust-value with functional trust. Direct trust (or what the article calls subjective reputation) is defined as a product of past actions, where more relevance is given to past observations. This is so that sporadic misbehaviour will have less of an impact than long term behaviour. Direct trust is defined as: directreputation t si (sj | f) = � ρ(t, tk)σk Where directreputation t si (sj | f) is the direct reputation value calculated at time t by 21
Page 1 and 2: Calculating trust in sensor network
Page 3 and 4: Contents 1 Introduction 1 2 Sensor
Page 5 and 6: 1 Introduction Wireless sensor netw
Page 7 and 8: 2 Sensor networks Advances in integ
Page 9 and 10: low power. A good guideline for des
Page 11 and 12: Figure 2: The SPEC mote and the tip
Page 13 and 14: n neither use every drop of enthe b
Page 15 and 16: of transmitting directly to the sin
Page 17 and 18: for sensor networks. SensorML conta
Page 19 and 20: would be to eavesdrop on the wires
Page 21 and 22: Development of these sensors leans
Page 23: called recommending or gossiping ab
Page 27 and 28: node uses this table to determine i
Page 29 and 30: and the nodes willingness (or malic
Page 31 and 32: Trust-based geographical routing do
Page 33 and 34: The rating component also employs a
Page 35 and 36: trust = (1 − w) ∗ trustold + w
Page 37 and 38: that nodes around it send in their
Page 39 and 40: positives, nodes that seem to be fi
Page 41 and 42: 4 Analysis of trusted service disco
Page 43 and 44: To other sensors... Simulated Senso
Page 45 and 46: A simple way of defining this metri
Page 47 and 48: esults. The square root-method reac
Page 49 and 50: 5 Results The JSIM network simulato
Page 51 and 52: Figure 12: A graph describing chain
Page 53 and 54: (a) Average reputations with no mal
Page 55 and 56: (a) Chains of trust for node 0, wit
Page 57 and 58: (a) Average reputations with no mal
Page 59 and 60: • The trustworthiness of nodes de
Page 61 and 62: (a) Average reputations with blackh
Page 63 and 64: (a) Chains of trust for node 28 wit
Page 65 and 66: Figure 20: Identifying malicious no
Page 67 and 68: BB02 Buchegger, S. and Boudec, J.-Y
Page 69 and 70: MICAZ The MicaZ datasheet. http://w

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