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The emerging paradigm of the Social Internet of Things (SIoT) has transformed the traditional notion of the Internet of Things (IoT) into a social network of billions of interconnected smart objects by integrating social networking facets into the same. In SIoT, objects can establish social relationships in an autonomous manner and interact with the other objects in the network based on their social behaviour. A fundamental problem that needs attention is establishing of these relationships in a reliable and trusted way, i.e., establishing trustworthy relationships and building trust amongst objects. In addition, it is also indispensable to ascertain and predict an object's behaviour in the SIoT network over a period of time. Accordingly, in this paper, we have proposed an efficient time-aware machine learning-driven trust evaluation model to address this particular issue. The envisaged model deliberates social relationships in terms of friendship and community-interest, and further takes into consideration the working relationships and cooperativeness (object-object interactions) as trust parameters to quantify the trustworthiness of an object. Subsequently, in contrast to the traditional weighted sum heuristics, a machine learning-driven aggregation scheme is delineated to synthesize these trust parameters to ascertain a single trust score. The experimental results demonstrate that the proposed model can efficiently segregates the trustworthy and untrustworthy objects within a network, and further provides the insight on how the trust of an object varies with time along with depicting the effect of each trust parameter on a trust score.

The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects, when deployed in the real-world aggregates useful information from their surrounding environment. As-of-late, this notion of IoT has been extended to incorporate the social networking facets which have led to the promising paradigm of the `Social Internet of Things' (SIoT). In SIoT, the devices operate as an autonomous agent and provide an exchange of information and service discovery in an intelligent manner by establishing social relationships among them with respect to their owners. Trust plays an important role in establishing trustworthy relationships among the physical objects and reduces probable risks in the decision-making process. In this paper, a trust computational model is proposed to extract individual trust features in a SIoT environment. Furthermore, a machine learning-based heuristic is used to aggregate all the trust features in order to ascertain an aggregate trust score. Simulation results illustrate that the proposed trust-based model isolates the trustworthy and untrustworthy nodes within the network in an efficient manner., Comment: 6 pages

The recent advancement in home automation technologies and inter-connectivity of smart appliances has contributed immensely in developing intelligent home environment. The complex smart home environment consists of smart devices from different vendors having different configuration and communication standards that introduce the problem of a heterogeneous subsystem in a smart home. A need is emerging for a central management system to control all these various devices. In this paper, we propose an action-based framework that acts as a middleware between the user and smart appliances which allow the user to control these heterogeneous devices in a federated manner. The proposed framework is based on a controller, for accepting user input and sending instructions to the intended smart devices. Framework maintains the integrity of an instruction send to the device and employs the mechanism so that only authentic user can access them. We also incorporate Human System Interaction (HSI) in our framework, by developing an Android application which receives input in the form of motion gestures, to fulfill the human-centric interaction requirements of the user to communicate with the smart home appliances. We evaluate the performance of our framework by implementing the controller in a local home network and the cloud.

Mobile Ad-hoc NETworks (MANETs) comprise on wireless mobile nodes that are communicating with each other without any infrastructure. Vehicular Ad-hoc NETwork (VANET) is a special type of MANETs in which vehicles with high mobility need to communicate with each other. In this paper, we present a novel framework for link availability of paths for static as well as dynamic networks. Moreover, we evaluate our frame work for routing protocols performance with different number of nodes in MANETs and in VANETs. We select three routing protocols namely Ad-hoc On-demand Distance Vector (AODV), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). Furthermore, we have also modified default parameters of selected protocols to check their efficiencies. Performance of these protocols is analyzed using three performance metrics; Packet Delivery Ratio (PDR), Normalized Routing Overhead (NRO) and End-to-End Delay (E2ED) against varying scalabilities of nodes. We perform these simulations with NS-2 using TwoRayGround propagation model. The SUMO simulator is used to generate a random mobility pattern for VANETs. From the extensive simulations, we observe that AODV outperforms among all three protocols., IEEE Conference on Open Systems (ICOS2012), Kuala Lumpur, Malaysia, 2012

This paper evaluates and compares the performance of two routing protocols, one is reactive, Dynamic MANET On-Demand (DYMO) and other is proactive, Optimized Link State Routing (OLSR) in Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs). Performance of these protocols is analyzed using three performance metrics; Packet Delivery Ratio, Normalized Routing Overhead and End-to-End Delay against varying scalabilities of nodes. We perform these simulations with NS-2 using TwoRayGround propagation model. The SUMO simulator is used to generate a random mobility pattern for VANETs. From the extensive simulations, we observe that DYMO performs better than OLSR for both VANETs and MANETs at the cost of delay. Moreover, DYMO performs better in VANETs as compared to MANETs.