The mounting demand for data processing in mobile networks, together with the inherent limitations of mobile devices, has as of late prompted the use of the Federated Learning (FL). FL is a distributed machine learning paradigm for distributing and aggregating a model's parameters while maintaining the data privacy. However, corrupt mobile devices, i.e., also referred to as participants or clients in the FL, are capable of interfering with the FL training process primarily owing to its lack of control over the local model training and which may eventually jeopardize the integrity of the global aggregation. This research therefore adopts a reputation-aware approach for quantifying the credibility of the participants for the FL tasks. The envisaged approach considers adaptive and dynamic parameters, i.e., historical accuracy, data quality, willingness, and behavioral consistency, in a bid to quantify a participant's reputation. In this work-in-progress paper, we present the foundational concept and framework of our envisaged model together with the preliminary experimental evaluations. It also include details on the datasets used, evaluation metrics, and a comparative analysis with a state-of-the-art method. A more comprehensive investigation of the FL training process together with an in-depth performance evaluation would be addressed in our future work.

The next generation of the Internet of Things (IoT) facilitates the integration of the notion of social networking into smart objects, i.e., things, to establish a social network of interconnected objects. This integration has led to the evolution of a promising and emerging paradigm of the Social Internet of Things (SIoT), wherein smart objects act as social objects and intelligently impersonate social behaviour similar to humans. These social objects are capable of establishing social relationships with the other objects in a network and can utilize these relationships for service discovery. Trust plays an indispensable role in establishing and maintaining such social relationships to achieve the common goal of trustworthy collaboration and cooperation among the objects and guarantee systems’ credibility and reliability. In SIoT, an untrustworthy object can disrupt the basic functionality of a service by delivering malicious messages and adversely affecting the quality and reliability of the service. In this survey, we present a holistic review of trustworthiness management in SIoT. The essence of trust in various disciplines and the trust in SIoT have been discussed, followed by a detailed study on trust management components in SIoT. Furthermore, we analyse and compare the trust management schemes by primarily categorizing them into four groups in terms of their strengths, limitations, trust management components (employed in each of the referred trust management schemes), and the performance of these schemes vis-à-vis a number of trust evaluation dimensions. Finally, we discuss the future research directions of the emerging paradigm of SIoT, in particular, in the context of trustworthiness management in SIoT.

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as “things”) in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework,Trust–SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover,Trust–SIoThas been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance ofTrust–SIoTvis-á-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate thatTrust–SIoTachieves a higher F1-score and lower MAE and MSE scores.

The recent integration of social networking concepts with the emerging yet promising paradigm of Internet of Things (IoT) has facilitated the emergence of a new domain named Social Internet of Things (SIoT) to socialize smart objects by emulating the human behavior. The interaction between these smart objects depend on social relations amongst them, wherein each object establishes these relationships autonomously via rules set out by its owners for creating virtual communities and working environments with shared interests. Trust is a concept that quantifies the reliability of these autonomous relationships and hence enriches cooperation between the SIoT objects. This paper explores the concept of time-aware similarity-based trust to address the issue of trust while establishing a social relationship concerning virtual communities, work environment, and friendship similarity. The proposed trust model segregates the trust in two types, i.e., direct trust and indirect trust, wherein direct trust signifies the direct perception of a trustor towards a trustee, whereas, indirect trust depends on recommendations from the neighboring objects of a trustor. Moreover, a dynamic weighted sum method is employed to aggregate all the similarities so as to obtain a single trust score at any given time. We conducted a comprehensive experimental study based on a real-world data set and the simulation results demonstrate that our method can efficiently classify trustworthy and untrustworthy objects.

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 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 services 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.

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.

Recently, the convergence of the paradigm of the Internet of Things (IoT) with social networking has been widely recognized as an emerging interdisciplinary area, lately, referred to as the Social Internet of Things (SIoT). SIoT enables the object-to-object interaction and provides the platform for these objects to autonomously socialize with the other objects in the network in a bid to overcome the key challenges of IoT, i.e., data discovery and composition, network navigability, trust management, etc. Trust plays a significant role while establishing these inter-object social relationships and it is also essential to observe the trustworthiness of an object before relying on information provided by them. A number of trust evaluation models for SIoT have been proposed in the literature, nevertheless, most of these models suffer in validating and testing their respective models due to lack of appropriate datasets. To address this issue, this paper proposes a scalable plug and play trust platform referred to as SCalable and Robust Trust platform for SIoT (SCaRT-SIoT) to provide the dataset to test and analyze various SIoT-based trust models of the research community.

Inadequate sleep has very bad impacts on human daily life activities. Sleep disorders are one of the main reasons for the inadequate sleep problem. Diagnosis of these disorders is a very challenging task because they occur at night when the patients are asleep and require a full night monitoring to diagnose. Conventional sleep monitoring techniques are not feasible due to various reasons such as they require the person to wear multiple invasive sensors, the cost is high and also need special environment for monitoring. In this work, we propose a non-invasive and cost-effective solution based on passive RFID technology for sleep monitoring. We attach passive RFID tags to the shirt of the person and collect low-level data which can capture the information about the vital signs of the person. The proposed solution can detect the on-bed movements which provide useful information about the sleep of the person. We propose an adaptive threshold-based technique which can effectively identify the stable breathing and apnea regions from the collected breathing signal. The stable region is used for estimating the breathing rate. Our solution can monitor vital signs such as respiration rate during the overnight sleep and can detect sleep apnea. We implement the proposed solution using COTS RFID tags in the real world scenarios and the results show that our system achieves 100% accuracy for apnea detection and above 95% for breath rate estimation.

In this paper, a framework for experimental parameters in which Packet Delivery Ratio (PDR), effect of link duration over End-to-End Delay (E2ED) and Normalized Routing Overhead (NRO) in terms of control packets is analyzed and modeled for Mobile Ad-Hoc NETworks (MANETs) and Vehicular Ad-Hoc NETworks (VANETs) with the assumption that nodes (vehicles) are sparsely moving in two different road. Moreover, this paper contributes the performance comparison of one Proactive Routing Protocol; Destination Sequenced Distance vector (DSDV) and two reactive protocols; DYnamic Source Routing (DSR) and DYnamic MANET On-Demand (DYMO). A novel contribution of this work is enhancements in default versions of selected routing protocols. Three performance parameters; PDR, E2ED and NRO with varying scalabilities are measured to analyze the performance of selected routing protocols with their original and enhanced versions. From extensive simulations, it is observed that DSR outperforms among all three protocols at the cost of delay. NS-2 simulator is used for simulation with TwoRayGround propagation model to evaluate analytical results.