Abstract-A future Internet of Things (IoT) system will connect the physical world into cyberspace everywhere and everything via billions of smart objects. On the one hand, IoT devices are physically connected via communication networks. The service oriented architecture (SOA) can provide interoperability among heterogeneous IoT devices in physical networks. On the other hand, IoT devices are virtually connected via social networks. In this paper we propose adaptive and scalable trust management to support service composition applications in SOA-based IoT systems. We develop a technique based on distributed collaborative filtering to select feedback using similarity rating of friendship, social contact, and community of interest relationships as the filter. Further we develop a novel adaptive filtering technique to determine the best way to combine direct trust and indirect trust dynamically to minimize convergence time and trust estimation bias in the presence of malicious nodes performing opportunistic service and collusion attacks. For scalability, we consider a design by which a capacity-limited node only keeps trust information of a subset of nodes of interest and performs minimum computation to update trust. We demonstrate the effectiveness of our proposed trust management through service composition application scenarios with a comparative performance analysis against EigenTrust and PeerTrust.

A social Internet of Things (IoT) system can be viewed as a mix of traditional peer-to-peer networks and social networks, where "things" autonomously establish social relationships according to the owners' social networks, and seek trusted "things" that can provide services needed when they come into contact with each other opportunistically. We propose and analyze the design notion of adaptive trust management for social IoT systems in which social relationships evolve dynamically among the owners of IoT devices. We reveal the design tradeoff between trust convergence vs. trust fluctuation in our adaptive trust management protocol design. With our adaptive trust management protocol, a social IoT application can adaptively choose the best trust parameter settings in response to changing IoT social conditions such that not only trust assessment is accurate but also the application performance is maximized. We propose a table-lookup method to apply the analysis results dynamically and demonstrate the feasibility of our proposed adaptive trust management scheme with two real-world social IoT service composition applications.

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Abstract-An Internet of Things (IoT) system connects a large amount of tags, sensors, and mobile devices to facilitate information sharing, enabling a variety of attractive applications. It challenges the design and evaluation of IoT systems to meet the scalability, compatibility, extendibility, dynamic adaptability and resiliency requirements. In this paper, we design and evaluate a scalable, adaptive and survivable trust management protocol in dynamic IoT environments. Recognizing that entities in an IoT system are connected through social networks of entity owners, we consider a community of interest (CoI) based social IoT where nodes form into communities of interest. Given inter-CoI vs. intra-CoI social connections among entity owners as input, we identify best trust protocol settings for achieving convergence, accuracy, dynamic adaptability and resiliency properties in the presence of dynamically changing conditions and malicious nodes performing trust-related attacks. For scalability, we consider a design by which a node only keeps trust information of a subset of nodes meeting its interest and performs minimum computation to update trust. We validate our design by extensive simulation considering both limited and ideal (unlimited) storage space. The results demonstrate that our trust management protocol using limited storage space achieves a similar performance level compared with the one under ideal storage space, and a newly joining node can quickly build up trust towards other nodes with desirable accuracy and convergence behavior.

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Keywords:Trust management Mobile ad hoc networks Trust bias minimization Model-based analysis Application-level trust optimization Reliability assessment a b s t r a c t Trust management for mobile ad hoc networks (MANETs) has emerged as an active research area as evidenced by the proliferation of trust/reputation protocols to support mobile group based applications in recent years. In this paper we address the performance issue of trust management protocol design for MANETs in two important areas: trust bias minimization and application performance maximization. By means of a novel modelbased approach to model the ground truth status of mobile nodes in MANETs as the basis for design validation, we identify and validate the best trust protocol settings under which trust bias is minimized and application performance is maximized. We demonstrate the effectiveness of our approach with an integrated social and quality-of-service (QoS) trust protocol (called SQTrust) with which we identify the best trust aggregation setting under which trust bias is minimized despite the presence of malicious nodes performing slandering attacks. Furthermore, using a mission-oriented mobile group utilizing SQTrust, we identity the best trust formation protocol setting under which the application performance in terms of the system reliability of the mission-oriented mobile group is maximized.

-An Internet of Things (IoT) system connects a large amount of tags, sensors, and smart devices often with mobility to facilitate information sharing, enabling a variety of attractive applications. On the one hand, the service oriented architecture (SOA) can provide connectivity and interoperability among heterogeneous IoT devices in the physical network. On the other hand, IoT devices are virtually connected via social networks. In this paper, we analyze the notion of adaptive trust management to support reliable service composition applications in SOAbased IoT systems. Each device records user satisfaction experiences toward devices with which it has interacted, and collects trust feedbacks from other devices sharing social interests. We consider friendship, social contact, and community of interest social relationships to select trust feedbacks. Further we develop a novel adaptive filtering technique to determine the best way to combine direct trust and indirect trust feedbacks dynamically to minimize both convergence time and trust bias. We demonstrate the effectiveness of the proposed trust management through a service composition application in SOAbased IoT systems.