MEJORAMIENTO DE LA CALIDAD DE LOS COMBUSTIBLES

Trust is one of the most crucial concepts driving decision making and establishing relationships. Trust is indispensible when considering interactions among individuals in artificial societies such as electronic commerce [YUB03]. As an important concept in network security, trust is interpreted as a set of relations among nodes participating in the network activities [RAMC04] [LIMC08]. Trusted relationships among nodes in a network are based on different sources of information such as direct interactions, witness information and previous behaviours of nodes.

Trust management in distributed and resource-constraint networks, such as DTNs and sensor networks, is much more difficult but more crucial than in traditional hierarchical architectures, such as the Internet and access point centred wireless LANs. Generally, this type of distributed network has neither pre-established infrastructure, nor centralized control servers or trusted third parties. The dynamically changing topology and intermittent connectivity of disconnected MANETs establish trust management more as a dynamic systems problem [BARA05]. Furthermore, resources (power, bandwidth, computation etc.) are limited because of the wireless and ad hoc environment, so the trust evaluation procedure should only rely on local information. In early stages of trust and security on MANETs several researchers relied on authentication, cryptographic encryption and decryption techniques. These schemes for security were shown to be effective; however these are based on centralized certification authorities. Significant communication overheads from both pre-processing and during processing periods, as well as energy consumption were major challenges thus rendering these approaches to be poor for DTNs. It has been shown recently that trust and reputation based techniques are more effective in

38 de-centralized mobile networks [SRIV06] [MERW07] [BALA07] [PIYA08] [LUOA09] [SALE09].

As reputation and trust have recently received considerable attention in many diverse domains several definitions exist.

Mui et.al in [MUIL02], define trust as ―a subjective expectation a node has about another’s future behaviour based on the history of their encounters‖.

Also in reference [BALA07] trust is defined as ―a firm belief in the competence of an entity to act as expected such that the belief is not a fixed value associated with the entity, rather it is subject to the behaviour of the entity and applies only to the given context within a defined time”.

While trust definitions focus more on the history of user‘s encounters, reputation is based on the aggregated information from other individuals. For instance, Sabater and Sierra [SABA05] declared that ―reputation is the opinion or view of someone about something‖.

Trust and reputation models have been developed to improve the success of interactions by minimizing uncertainty. Many of the models are based on Marsh‘s trust formalism [MARS94], in using trust to assess the likelihood that a user honours its promises. Trust and reputation models can be classified into centralized and decentralized models.

2.5.1 Centralized Trust and Reputation Models

Reputation mechanisms have been widely used in online electronic commerce systems e.g. eBay [EBAY], Amazon which typically manage the reputation of all its users in a centralized manner. The main building block of these models is information about a node‘s past behaviours. This information is used to deduce the trustworthiness of that node in terms of its competency and reliability. Online reputation mechanisms e.g. those on eBay [RESN02] and Amazon Auctions [AMAZ] are probably the most widely used such models. They are implemented as a centralized rating system so that their users can report about the behaviour of one another in past transactions via rating and leaving textual comments. In so doing, users in their communities can learn about the past behaviour of a given user to decide whether it is trustworthy.

39 Disconnected MANETs are essentially distributed in nature, therefore centralized trust and reputation models may not be suitable. Recently some decentralized models for trust management for distributed systems have been proposed, some of these are presented here.

2.5.2 Decentralized Trust and Reputation Models

As more and more computational systems of all kinds move toward large-scale, open and dynamic architectures, more and more trust models are designed such that each node can carry out trust evaluation itself without the need for a central trust authority.

Jurca and Falting introduce a reputation mechanism where nodes are incentivized to report truthfully about their interactions results [JURC03]. They define a set of broker nodes called R-nodes whose tasks are buying and aggregating reports from other nodes and selling back reputation information to them when they need it. All reports about a node are simply aggregated using the averaging method to produce the reputation value for that node. In order to incentivize nodes to share their reports truthfully, [JURC03] propose a payment scheme for reputation reports. This scheme guarantees that nodes who report incorrectly will gradually lose money (during the process of selling reports and buying reputation information), while honest nodes will not. Therefore, this mechanism makes it rational for a node to report its observations honestly and this is the main contribution of their work.

ReGreT [SABA01] is a completely de-centralized model of trust and reputation with three dimensions of information: individual, social and ontological. The social dimension includes information on the experiences of other members of the evaluator‘s group, or neighbourhood, which is assumed to be a group of nodes with some common knowledge. Employing Regret, each node is able to evaluate the reputation of others by itself. In order to do so, each node rates its partner‘s performance after every interaction and records its ratings in a local database. The relevant ratings will be queried from this database when trust evaluation is needed. The trust value derived from those ratings is termed direct trust and is calculated as the weighed means of all ratings. Each rating is weighed according to its recency. Intuitively, a more recent rating is deemed to be more current and is weighted more than those that are less recent. Besides direct trust and witness reputation, Regret also introduces the concepts of neighbourhood reputation and system reputation. The former is calculated from the reputation of the target‘s neighbour nodes based on fuzzy rules.

40 Reference [YUB08] developed an approach for social reputation management, in which they represented a node‘s ratings regarding another node as a scalar and combined them with testimonies using combination schemes similar to certainty factors. In this system, nodes cooperate by giving, pursuing, and evaluating referrals (a recommendation to contact another node). Each node in the system maintains a list of acquaintances (other nodes that it knows) and their expertise. Thus, when looking for a certain piece of information, a node can send the query to a number of its acquaintances who will try to answer the query if possible or, if they cannot, they will send back referrals pointing to other nodes that they believe are likely to have the desired information (based on that node‘s expertise).

Reference [HANG08] proposed an adaptive probabilistic trust model that combines probability and certainty and offers a trust update mechanism to estimate the trustworthiness of referrers. Some other trust-based network models include Trust-Net [SCHI00] and Histos [ZACH00]. [PAPA03], present an encryption based technique for secure message transmission in networks. A Robust reputation based approach to trust management in MANETs is presented in [BUCH04]. Authors in [ZOUR05] and a later paper [ZOUR06] define trust metrics and evaluate performance of proposed reputation based techniques with an emphasis on secure data delivery rates. An adaptive trust management scheme is proposed in distributed applications for MANETs in [LIH07], and [YUNF07].

A popular decentralized TRM is FIRE presented in [HUYN04] and [HUYN06]. FIRE presents a modular approach that integrates up to four types of trust and reputation from different information sources, according to availability: interaction trust, role-based trust, witness reputation, and certified reputation. FIRE model classifies users in a network as Agents, a set of users participating in trust interaction; Targets, users whose trust and reputation is being sought in an interaction and Evaluators, users requesting trust information about a target. Each time agent i gives a rating, it will be stored in the agent‗s local rating database. Ratings in this database will be retrieved when needed for trust evaluation or for sharing with other agents. However, an agent does not need to store all ratings it makes. Old ratings become out of date due to changes in the environment and may not be stored in limited amount of memory. In FIRE, trust rating is calculated based on recommendations from direct interaction, witness interaction or rule based interactions.

41 The evaluator node uses its previous experiences in interacting with the target agent to determine its trustworthiness. This type of trust is most frequently used [WANG08] [SRIV06] and is called Direct Interaction Trust (DIT). Assuming that nodes are willing to share their direct experiences, the evaluator node can collect experiences of other nodes that interacted with the target node. Such information will be used to derive the trustworthiness of the target node based on the views of its witnesses. Hence this type of trust is called Witness Interaction Trust (WIT).