Calculating trust in sensor networks

More documents

Recommendations

Info

Name Direct- Indirect-trust Warning mechanism Monitored behaviour* Routing protocol Confidant X X X ? DSR CORE X X X (1,8) DSR SRP X X X 1 not specified TARP X X X 1 not specified T-GPSR X 1,2 GPSR TGRS X 1 not specified WSNodeRater X X X ? GESAR (GEAR) TLEACH X X ? LEACH Teihal X X 1,2,3,4,5,6,7 not specified * Monitored behaviour: 1: Packet forwarding 2: Network ACK 3: Packet integrity 4: Authentication 5: Cryptography 6: Corrent reputation sharing 7: Remaining energy 8: Routing functions 36 ? : Not exactly specified in the article Figure 6: Comparison of different trust-based routing schemes.
4 Analysis of trusted service discovery The previous sections have included comparisons of different routing protocols for service discovery, including trust-based ones. A major motivation for defining and monitoring trust in sensor networks is that an individual node may be subject to node capture. A malicious node could then try to damage the network by sending false sensing data or by disrupting the routing. But all of the proposed trust-based routing protocols only consider the neighbouring nodes. As such, a node will have no way of knowing anything about nodes far away from them. Another thing to consider is how the trust information could be leveraged for other appli- cations? If the trust management system could give an accurate guess about which nodes are malicious and which are not, would the user like to know about it? Consider a scenario where a user has used service discovery to locate two sensors from the network and now cannot decide which of them is more suitable? Discarding such information as location (maybe the nodes are right next to each other) or power levels, should we ask which of the nodes is more trustworthy? The following chapters detail a way with which the individual trust-values of other nodes can be used to describe the trustworthiness of a single node. Consider the previous scenario where a user has received a reply from one of the potential service providers: What if the trustworthiness of the provider could be defined as the product of the route through which a packet must traverse. If the service provider is not trusted by its neighbours, the overall trustworthiness should go down. If the packet traverses a route of highly trusted nodes, the trustworthiness should be high. If the packet goes through a couple of untrustworthy nodes, the trustworthiness should go down. We define the trustworthiness of a service or a node as the chain of individual reputations, or trusts from the service provider to the service seeker. As a node receives a packet for forwarding, it must alter the trustworthiness field of the packet according to how much it trusts the node from which it received the packet. A series of simulations were prepared to test how this type of a scheme would behave 37
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 and 24: called recommending or gossiping ab
Page 25 and 26: of trustworthy nodes. These are nod
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: positives, nodes that seem to be fi
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

Calculating trust in sensor networks

Create successful ePaper yourself

Delete template?

Save as template?