With the proliferation of distributed data sources, Federated Learning (FL) has emerged as a key approach to enable collaborative intelligence through decentralized model training while preserving data privacy. However, conventional FL algorithms often suffer from performance disparities across clients caused by heterogeneous data distributions and unequal participation, which leads to unfair outcomes. Specifically, we focus on two core fairness challenges, i.e., representation bias, arising from misaligned client representations, and collaborative bias, stemming from inequitable contribution during aggregation, both of which degrade model performance and generalizability. To mitigate these disparities, we propose CoRe-Fed, a unified optimization framework that bridges collaborative and representation fairness via embedding-level regularization and fairness-aware aggregation. Initially, an alignment-driven mechanism promotes semantic consistency between local and global embeddings to reduce representational divergence. Subsequently, a dynamic reward-penalty-based aggregation strategy adjusts each client’s weight based on participation history and embedding alignment to ensure contribution-aware aggregation. Extensive experiments across diverse models and datasets demonstrate that CoRe-Fed improves both fairness and model performance over state-of-the-art baseline algorithms.

Unsupervised federated learning for cross-modal retrieval has received increasing attention in recent years as it can free the requirement for annotations and avoid uploading original clients’ data to servers. Most existing methods focus on how to learn better local models and their aggregation to overcome data distribution drift across clients. Unlike prior works, we propose to address the data distribution problem by generating synthetic data, which can benefit existing federated learning methods. Specifically, we train a WGAN generator with three newly designed loss constraints on each client to improve the quality of the generated data. We first compute cluster prototypes to address the problem of lack of labels. Then, a direct contrastive loss between generated image and text features, an indirect contrastive loss with reference to cluster prototypes, and a Jensen-Shannon Divergence (JSD) loss also with reference to cluster prototypes work together to constrain the WGAN. The locally trained generators and local prototypes are sent to the server to generate and filter synthetic data with consideration of data distribution across all clients. The filtered data are used to train the aggregated global retrieval model, which is later sent to clients. The final global model becomes robust to all clients after several rounds of client-server iteration. Extensive experiments using four baselines across three datasets demonstrate that our method performs favourably against state-of-the-art methods.

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.

Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients’ constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.

Significant growth of internet users signifies that more autonomous systems need to be designed. Software-Defined Network (SDN) replaces traditional networks due to their solubility and capability to integrate with artificial intelligence. It also makes networking future poof. A technological shift often comes with shortcomings, and SDN is no exception. Anomaly detection is a crucial task. Existing anomaly detection methods use traditional statistical and machine learning-based models that may perform poorly due to two key issues: (i) the unavailability of all training network-packets at the beginning since these packets are received over time and the training dataset gradually grows over time, and (ii) limited memory and computation capacity of the computing system for model building, preventing the storage of all network-packets that arrive over time for a prolonged period. To address the issues, this paper introduces a novel functionality in SDN called Adaptive anomaly Detection Method (ADM), which utilises an existing technique for facilitating incremental learning so that ADM can handle new anomaly types by keeping track of historical information. ADM is evaluated using four criteria by comparing its performances against four state-of-the-art methods on two publicly available datasets. Our experimental results demonstrate that ADM performs significantly better than existing techniques. The source code of this work has been made publicly available at https://github.com/eferdous/adm

In edge computing, end devices (EDs) containerize tasks with the necessary resources and offload subsets to a nearby high-capacity edge server (ES) to improve efficiency. Most existing research focuses on inseparable task offloading to minimize response times or resource allocation to reduce energy consumption. However, task execution can be speeded up with excessive computing and network resources, it will increase energy consumption and incur unnecessarily high costs. Besides, complex applications like autonomous driving often partition sequential tasks to improve performance, necessitating a joint optimization of sequential task offloading and multi-resource allocation. In this paper, we introduce a Stackelberg game-based framework to model the interplay between these elements.EDs, acting as leaders, determine the offloading breakpoints of sequential tasks and the locality for processing. TheES, as the follower, uses the Karush-Kuhn-Tucker (KKT) conditions and a Boundary-constrained quasi-Particle Swarm Optimization (Bc-qPSO) algorithm to refine computing and network resource allocation, aiming to reduce system costs effectively. Our simulations show that the proposed algorithms reduce cost by approximately 10%-20% compared to traditional methods, highlighting their potential for improving the efficiency of edge computing systems.

Software engineering, as a discipline, emerged in the wake of the software crisis since both software developers and the inadequacy of conventional development methods failed to tackle the complexity of large-scale software development projects. This not only resulted in spending of more time and finances than what was originally estimated for developing a particular software but also the end product, in case if got realized, turned out to be of much lower quality. Software engineering, therefore, came to the rescue with its initial boost from the NATO Software Engineering Conferences (of the NATO Science Committee) in 1968 and 1969. As a systematic and intelligent approach to software development, software engineering utilizes the engineering principles, methods, techniques, and tools for designing, developing, and maintaining state-of-the-art software possessing key characteristics, including but not limited to, being highly safe, secure, reliable, usable, and accessible. Being a skilled software engineer mandates possessing a diverse range of technical and practical skills, and which needs to be imparted in an ideal world via a software engineering-based educational program. In particular, the key aim of this paper is to discuss the prevalent trends of software engineering education in the Australia's higher education landscape.

Digital technology advances have converted traditional archives into digital formats, broadening data diversity and posing new management challenges. This paper introduces a novel framework for Multimodal Archival Data Ecosystems, designed to enhance the management, accessibility, and utilization of archival data across diverse modalities. The need for such a framework arises from critical challenges in archival resource management, including the absence of systematic organizational structures, inefficiencies in inter-regional communication, and insufficient strategies for preserving and enhancing archival material values. Traditional archival systems struggle with these issues, especially in the face of growing data diversity and volume due to advancements in digital technologies. Our method is grounded in information ecology theory, leveraging recent advancements in information science. We propose a multimodal approach that integrates various data types and sources, enhancing the analytical and knowledge services capabilities of archival resources. This framework promotes efficient knowledge exchange by implementing collaborative mechanisms among archival stakeholders and incorporating intelligent archiving services to improve user interaction and resource accessibility. We conducted a comprehensive analysis of the ecosystem’s structure and operational mechanisms, emphasizing the integration of collaborative and intelligent services to meet the dynamic needs of users and keep pace with ongoing technological advancements. This study supports innovative practices that aim to transform traditional archival systems into dynamic, multimodal ecosystems capable of handling the complexities of modern archival data.

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.

Summary As the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

Influence Maximization(IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named,ReconnectingTop-$l$lRelationships(RT$l$R) query, which aims to find$l$number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT$l$R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT$l$R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT$l$R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given question. However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis. The task of question rewriting has the potential to address the challenges of resolving dependencies amongst the contextual turns by transforming them into intent-explicit questions. Nonetheless, the solution of rewriting the implicit questions comes with some potential challenges such as resulting in verbose questions and taking conversational aspect out of the scenario by generating the self-contained questions. In this paper, we propose a novel framework, CONVSR (CONVQA using Structured Representations) for capturing and generating intermediate representations as conversational cues to enhance the capability of the QA model to better interpret the incomplete questions. We also deliberate how the strengths of this task could be leveraged in a bid to design more engaging and more eloquent conversational agents. We test our model on the QuAC and CANARD data sets and illustrate by experimental results that our proposed framework achieves a better Fl score than the standard question rewriting model.

The citation function generally signifies the purpose or reason underlying a citation within a scholarly paper or a research article. Automatic citation function classification is, therefore, a task in computational linguistics and information science that can facilitate further applications in reference research, citation recommendation, and evaluation of research activities. By taking into account the state of the art, we identify two major constraints pertinent to the data of the citation function classification task, i.e., data imbalance and data sparsity. On the one hand, the natural distribution of different types of citations in one scientific literature is uneven leading to data imbalance in the real scenario. On the other hand, the citation function data is generally labeled by an expert which takes huge human effort resulting in a limited data scale. To this end, in this paper, we propose HybridDA, a two-stage model based on GPT-2 data argumentation and data retrieval to synthesize more high-quality annotated citation function data in a bid to solve both data imbalance and data sparsity problems. We conduct experiments on imbalance setting and low resource setting with our proposed approach. The experimental results on both of these settings demonstrate that our proposed model can achieve competitive performance in contrast to the other baseline models.

Summarising from the Web could be formed as a problem of multi-document Summarisaiton (MDS) from multiple sources. In contrast to the current MDS problem that involves working on benchmark datasets which provide well clustered set of documents, we envisage to build a pipeline for content Summarisaiton from the Web, but narrow down to the Social Internet of Things (SIoT) paradigm, starting at data collection from the IoT objects, then applying natural language processing techniques for grouping and summarising the data, to distributing summaries back to the IoT objects. In this paper, we present our simulation tool, SimSumIoT, that simulates the process of data sharing, receiving, clustering, and Summarisaiton. A Web-based interface is developed for this purpose allowing users to visualize the process through a set of interactions. The Web interface is accessible via http://simsumlot.tk.

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.

Trust evaluation and management schemes have been extensively employed in a bid to alleviate diverse insider attacks. These trust values are often ascertained by taking into consideration trust parameters to determine the honesty and reliability of a vehicle and evaluating a weighted sum of the said parameters. To achieve precision and to reflect a reasonable impact of these parameters, rational weight values are imperative. Accordingly, this research primarily emphasizes on the quantification of weights associated with the contributing trust attributes by proposing a novel trust management mechanism that utilizes contextual information in addition to employing relevant impacting quantities as weights to formulate trust evaluations. Moreover, the envisaged trust management model incorporates 1) attack resilience while constituting certain parameters and 2) an adaptive and flexible threshold to mitigate malevolent behaviors. The simulation results depict that the devised parameters and the formulated trust aggregation cater to the dynamic nature of vehicular networks demonstrating the rationality of the weights’ quantification, and the introduction of the adaptive threshold for misbehavior detection aligns well with the requirements of the ever-changing vehicular networks.

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.

Abstract Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on conversational question answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user’s information needs. While the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers over the recent years. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.

The emerging yet promising notion of trust amalgamated with the paradigm of the Internet of Vehicles (IoV) has been of significant interest for researchers in both industry and academia. Trust management models are being widely employed in diverse domains seeking to mitigate multifarious insider attacks where dishonest entities counterfeit or alter the messages from legitimate users, subsequently, endangering the lives of drivers, passengers, and vulnerable pedestrians. While vehicles form opinions towards the fellow network participants, numerous influencing attributes in reference to the interactions among the vehicles are aggregated to develop a final belief on a target vehicle. The significance of these trust impacting attributes is often depicted by manually assigning a weight to each parameter. The aim of this research is to address the challenges of quantification of the influencing trust attributes and the weights associated with these parameters, subsequently, quantifying the aggregated trust. Furthermore, the time-based behavior of the aggregated trust along with the contributing parameters is also analyzed in this paper to study the behavior of each vehicle and to identify suitable trust-based patterns for safety-critical and non-safety vehicular applications. Performance analysis demonstrates that the trust values computed via the envisaged trust model are lower than those calculated via the benchmark scheme.

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

The growth in distributed energy resources (DER) has produced positive impacts on energy grid systems. However, there are still significant challenges for deployment of DER systems. In this paper, we bring out the latest advancements in the domains of the internet of things (IoT), artificial intelligence, and distributed ledger technology in tandem to create the next generation of a smart, distributed, and efficacious energy management and trading system in Australia. The system is comprised of cost-effective and easy-to-assimilate IoT devices, e.g., smart sockets and inverters connected to existing devices or renewable energy sources, to formulate a mobile-friendly platform that provides the energy consumers with intuitive analytics, programmable control, and real-time energy monitoring and trading so as to assist them in improving their energy-efficiency.

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 promising paradigm of the Internet of Things (IoT) has rapidly proliferated in a number of domains, e.g., energy monitoring, transportation, smart cities, healthcare, and the like. Leveraging on the same, in this paper, we proposed an Internet of Plants system to remotely monitor soil moisture level and automate watering control for the plants. The envisaged system not only facilitates in over watering or under watering of the plants but also provides data collection for the gardeners to understand the key features pertinent to healthy growth of plants. Such kind of system also encourages more people to grow their small-scale organic gardens in urban areas, thereby, increasing greenery in modern cities.

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.

The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the ε-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.

Over the past few decades, Vehicular Ad hoc Networks have attracted the attention of numerous researchers from both academia and industry. Today, this promising wireless communication technology plays an indispensable role as vehicles exchange low-latent safety critical messages with one another in a bid to make the road traffic more safer, efficient, and convenient. However, dissemination of malicious messages within the network not only significantly reduces the network performance but also becomes a source of threat for the passengers and vulnerable pedestrians. Accordingly, a number of trust models have been recently proposed in the literature to ensure the identification and elimination of malicious vehicles from the network. These trust models primarily rely on the aggregation of both direct and indirect observations and evict the malicious vehicles based on a particular threshold set on this composite trust value. Nevertheless, setting-up of this threshold poses a significant challenge especially owing to diverse influential factors in such a dynamic and distributed networking environment. To this end, in this manuscript, machine learning has been employed to compute the aggregate trust score for flagging and evicting of the malicious vehicles from a vehicular network. It is evident from the simulated results that the devised method is both accurate and scalable.

Considerable innovative advancements in vehicular communication have led to the emerging yet promising paradigm of Internet-of-Vehicles, wherein vehicles exchange safety-critical information with minimal delay for ensuring road safety as well as efficacious traffic flows. It is, therefore, indispensable that these safety messages are authentic and reliable, and have originated from a legitimate vehicle. This demands establishing trust among vehicles such that the malicious and dishonest vehicles (and their malicious content) could be flagged and subsequently eliminated from the network. The risk manifolds if a malicious vehicle gets elected as the cluster head of other vehicles thereby compromising the safety of vehicular passengers and pedestrians on the road. Hence, intelligent algorithms should be in place to opt for the trusted and resource efficient cluster heads which could enhance the overall security and efficiency of their respective clusters. To this end, in this paper, we have proposed a scalable hybrid trust model which takes into account a composite metric encompassing the weighted trust score and available resources of each vehicle for identification of multiple malicious vehicles in real-time, and for meeting the stringent performance requirements of vehicular safety applications. Moreover, an optimal role assignment scheme based on the Hungarian algorithm has been proposed for electing the optimal cluster head, proxy cluster head, and followers among the members of a vehicular cluster so as to maximize its overall efficiency. Preliminary simulations have been carried out and are also presented in this paper.

Managing network resources in highly dynamic vehicular environments is difficult. It is made more challenging when vehicular safety applications demand high bandwidth, reliability and extremely low latency. Standalone wireless access technologies cannot meet these requirements simultaneously and so heterogeneous wireless networking has been proposed as a solution. However heterogeneity introduces other problems, particularly network fragmentation and inefficient use of network resources. Software-defined Networking (SDN) offers logically centralized control and re-programmability of the control plane, with the potential to address these problems of heterogeneity. However, SDN has difficulty dealing with the high levels of mobility associated with highly dynamic vehicular networks. This paper presents ongoing work towards an SDN-based heterogeneous vehicular networking, employing edge-based caching to ensure extremely reliable and low-latency communication for safety-critical cooperative vehicular services.