Calculating trust in sensor networks

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for the desired speed, the packet is dropped according to random chance. This random punishement is used to force the routing protocol to try and find a faster or alternative route. A routing algorithm called Maximum residual energy path [CT04] offers an energy-conserving approach to routing packets in sensor networks. It considers the cost of a path to be the amount of energy a node is left with after transmitting a packet. This favors nodes that have high energy or can forward a packet with the least strain on their battery. The Bellman-Ford shortest path-algorithm is used to select the cheapest path, which would be the path whose residual energy is the largest of possible paths. Simulations made by the authors suggest that maximizing residual energy prolongs the lifetime of the network more than minimizing energy-consumption when transmitting. Research into sensor networks has spawned numerous new routing protocols [AY05]. These protocols try to best match the demands of a sensor network by 2.3 Service discovery Service discovery allows a more dynamic way of leveraging different networked entities. Service discovery in a sensor network may include the discovery of a specific service in a network from either inside or outside the sensor network. A user may want to query a deployed sensor network from a mobile terminal (for instance a PDA) while strolling around the area of the network. Another scenario could be a user, that uses a remote computer to monitor and maintain several different sensor networks remotely. A user may be interested in only the aggregated data from the network, such as the latest readings gathered straight from the sink-node, or a user may want to find the nearest sensor that will provide it with the sensing capability that the user wants. Different service discovery protocols already exist such as the SLP [SLP], UPnP [UPNP] or the Bluetooth Service Discovery Protocol (SDP) [BT]. None of these protocols are specifically designed for sensor networks. A protocol called SensorML [SML] exists that aims to provide service discovery specifically 12
for sensor networks. SensorML contains an information model and XML encodings for discovering, querying and controlling sensors that may be connected to the rest of the Internet. SensorML contains intricate ways for describing the type and properties of a sensor in XML, such as what kind of data it can provide, how long the data will be valid, what the sensor is attached to or who operates the sensor. SensorML does not itself define how services should be found; for instance should the service provider actively advertise itself to the network and how and who keeps track of the different services in the network. An open question is whether the same service discovery protocol should be used inside and outside the sensor network. Could a sink-node perhaps translate service requests from one protocol to another when querying the sensors? Or should the sink even query the sensors? A sink could work as a gateway for the sensors and exclude direct communications with the sensors from the outside. 2.4 Security considerations in sensor networks Sensor networks can pose unique security challenges. Traditional security techniques, that are used in more common networks, may not be directly applied to sensor networks. For one, sensor nodes have very limited communication and computational capacities and must operate on very low power levels. This limits both transmission range and intervals and may also rule out public-key cryptography. By their design sensor networks may require that end-to-end security be discarded. [KW03] In traditional networks the intermediary routers do not need to have access to the messages contents, but a sensor network may some form employ data aggregation or in-network processing to promote the lifespan of the network. This requires that intermediary nodes have access to the data they are forwarding. Nodes may refuse to forward repeated or redundant data to the data collector. They may also choose to aggregate and bundle larger amounts of sensing data that will be sent at a later time. Setting up cryptographic keys in sensor networks may be harder than in other networks 13
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: of transmitting directly to the sin
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 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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