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 versus 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.

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.

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 SOA-based 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 SOA-based IoT systems.

Delay tolerant networks (DTNs) are characterized by high end-to-end latency, frequent disconnection, and opportunistic communication over unreliable wireless links. In this paper, we design and validate a dynamic trust management protocol for secure routing optimization in DTN environments in the presence of well-behaved, selfish and malicious nodes. We develop a novel model-based methodology for the analysis of our trust protocol and validate it via extensive simulation. Moreover, we address dynamic trust management, i.e., determining and applying the best operational settings at runtime in response to dynamically changing network conditions to minimize trust bias and to maximize the routing application performance. We perform a comparative analysis of our proposed routing protocol against Bayesian trust-based and non-trust based (PROPHET and epidemic) routing protocols. The results demonstrate that our protocol is able to deal with selfish behaviors and is resilient against trust-related attacks. Furthermore, our trust-based routing protocol can effectively trade off message overhead and message delay for a significant gain in delivery ratio. Our trust-based routing protocol operating under identified best settings outperforms Bayesian trust-based routing and PROPHET, and approaches the ideal performance of epidemic routing in delivery ratio and message delay without incurring high message or protocol maintenance overhead.

Nowadays, with the rapid increase of WLAN-enabled mobile devices and the more widespread use of WLAN, it is increasingly important to have a more efficient initial link setup mechanism, and there is a demand for a faster access authentication method faster than the current IEEE 802.11i. In this paper, through experiments we observe that the authentication delay of 802.11i is intolerable under some scenarios, and we point that the main reason resulting in such inefficiency is due to its design from the framework perspective which introduces too many messages. To overcome this drawback, we propose an efficient initial access authentication protocol, FLAP, which realizes the authentications and key distribution through two roundtrip messages. We formally prove that our scheme is more secure than the four-way handshake protocol. Our practical measurement result indicates that FLAP can improve the efficiency of EAP-TLS by 94.7 percent. Extensive simulations are conducted in different scenarios, and the results demonstrate that when a WLAN gets crowded the advantage of FLAP becomes more salient. Furthermore, a simple and practical method is presented to make FLAP compatible with 802.11i.

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.

The Internet of Things (IoT) integrates a large amount of everyday life devices from heterogeneous network environments, bringing a great challenge into security and reliability management. Recognizing that the smart objects in IoT are most likely human-carried or human-operated devices, we propose a scalable trust management protocol for IoT, with the emphasis on social relationships. We consider multiple trust properties including honesty, cooperativeness, and community-interest to account for social interaction. Each node performs trust evaluation towards a limited set of devices of its interest only. The trust management protocol is event-driven upon the occurrence of a social encounter or interaction event, and trust is aggregated using both direct observations and indirect recommendations. We analyze the effect of trust parameters on trust assessment accuracy and trust convergence time. Our results show that there exists a trade-off between trust assessment accuracy vs. trust convergence time in the presence of false recommendations attacks performed by malicious nodes. We demonstrate the effectiveness of the proposed trust management protocol with a trust-based service composition application. Our results indicate that trust-based service composition significantly outperforms non-trust-based (random) service composition and its performance approaches the maximum achievable performance with global knowledge.

We propose a highly scalable cluster-based hierarchical trust management protocol for wireless sensor networks (WSNs) to effectively deal with selfish or malicious nodes. Unlike prior work, we consider multidimensional trust attributes derived from communication and social networks to evaluate the overall trust of a sensor node. By means of a novel probability model, we describe a heterogeneous WSN comprising a large number of sensor nodes with vastly different social and quality of service (QoS) behaviors with the objective to yield "ground truth" node status. This serves as a basis for validating our protocol design by comparing subjective trust generated as a result of protocol execution at runtime against objective trust obtained from actual node status. To demonstrate the utility of our hierarchical trust management protocol, we apply it to trust-based geographic routing and trust-based intrusion detection. For each application, we identify the best trust composition and formation to maximize application performance. Our results indicate that trust-based geographic routing approaches the ideal performance level achievable by flooding-based routing in message delivery ratio and message delay without incurring substantial message overhead. For trust-based intrusion detection, we discover that there exists an optimal trust threshold for minimizing false positives and false negatives. Furthermore, trust-based intrusion detection outperforms traditional anomaly-based intrusion detection approaches in both the detection probability and the false positive probability.

Crowd sourcing is based on a simple but powerful concept: Virtually anyone has the potential to plug in valuable information. The concept revolves around large groups of people or community handling tasks that have traditionally been associated with a specialist or small group of experts. With the advent of the smart devices, many mobile applications are already tapping into crowd sourcing to report community issues and traffic problems, but more can be done. While most of these applications work well for the average user, it neglects the information needs of particular user communities. We present CROWDSAFE, a novel convergence of Internet crowd sourcing and portable smart devices to enable real time, location based crime incident searching and reporting. It is targeted to users who are interested in crime information. The system leverages crowd sourced data to provide novel features such as a Safety Router and value added crime analytics. We demonstrate the system by using crime data in the metropolitan Washington DC area to show the effectiveness of our approach. Also highlighted is its ability to facilitate greater collaboration between citizens and civic authorities. Such collaboration shall foster greater innovation to turn crime data analysis into smarter and safe decisions for the public.

We propose a trust-based intrusion detection scheme utilizing a highly scalable hierarchical trust management protocol for clustered wireless sensor networks. Unlike existing work, we consider a trust metric considering both quality of service (QoS) trust and social trust for detecting malicious nodes. By statistically analyzing peer-to-peer trust evaluation results collected from sensor nodes, each cluster head applies trust-based intrusion detection to assess the trustworthiness and maliciousness of sensor nodes in its cluster. Cluster heads themselves are evaluated by the base station. We develop an analytical model based on stochastic Petri nets for performance evaluation of the proposed trust-based intrusion detection scheme, as well as a statistical method for calculating the false alarm probability. We analyze the sensitivity of false alarms with respect to the minimum trust threshold below which a node is considered malicious. Our results show that there exists an optimal trust threshold for minimizing false positives and false negatives. Further, the optimal trust threshold differs depending on the anticipated wireless sensor network lifetime.

We propose and analyze a class of trust management protocols for encounter-based routing in delay tolerant networks (DTNs). The underlying idea is to incorporate trust evaluation in the routing protocol, considering not only quality-of-service (QoS) trust properties (connectivity) but also social trust properties (honesty and unselfishness) to evaluate other nodes encountered. Two versions of trust management protocols are considered: an equal-weight QoS and social trust management protocol (called trust-based routing) and a QoS only trust management protocol (called connectivity-based routing). By utilizing a stochastic Petri net model describing a DTN behavior, we analyze the performance characteristics of these two routing protocols in terms of message delivery ratio, latency, and message overhead. We also perform a comparative performance analysis with epidemic routing for a DTN consisting of heterogeneous mobile nodes with vastly different social and networking behaviors. The results indicate that trust-based routing approaches the ideal performance of epidemic routing in delivery ratio, while connectivity-based routing approaches the ideal performance in message delay of epidemic routing, especially as the percentage of selfish and malicious nodes present in the DTN system increases. By properly selecting weights associated with QoS and social trust metrics for trust evaluation, our trust management protocols can approximate the ideal performance obtainable by epidemic routing in delivery ratio and message delay without incurring high message overhead.