We propose an integrity-preserving framework for managing trust information in crowdsourced IoT environments. The integrity of trust information is paramount for ensuring accurate trust assessment. Traditional trust frameworks assume that distributed storing entities of trust information are trustworthy, making them vulnerable to internal attacks. In this respect, entities responsible for storing trust data could tamper with information for personal gain and competitive advantage. Trust assessment using such tampered data could lead to inaccurate evaluations and may mislead IoT users within the environment. We propose a novel Tampering Detection Approach (TDA) to identify the tampering in trust information. Furthermore, we propose a technique to discover the tampering sophistication level. A set of experiments is conducted to evaluate the effectiveness and efficiency of the proposed approaches. Results demonstrate that our TDA achieves a 40% accuracy improvement in detecting tampered data compared to state-of-the-art methods.

Fall detection is critical to support the growing elderly population, projected to reach 2.1 billion by 2050. However, existing methods often face data scarcity challenges or compromise privacy. We propose a novel IoT-based Fall Detection as a Service (FDaaS) framework to assist the elderly in living independently and safely by accurately detecting falls. We design a service-oriented architecture that leverages Ultra-wideband (UWB) radar sensors as an IoT health-sensing service, ensuring privacy and minimal intrusion. We address the challenges of data scarcity by utilizing a Fall Detection Generative Pre-trained Transformer (FD-GPT) that uses augmentation techniques. We developed a protocol to collect a comprehensive dataset of the elderly daily activities and fall events. This resulted in a real dataset that carefully mimics the elderly's routine. We rigorously evaluate and compare various models using this dataset. Exper-imental results show our approach achieves 90.72% accuracy and 89.33% precision in distinguishing between fall events and regular activities of daily living.

We propose a novel dynamic and immersive 3D framework designed to facilitate the setup and customization of drone scheduling algorithms for evaluating service-based drone delivery systems. This framework features a robust system architecture that supports user-defined behavior logic. It also incorporates real-time data communication protocols for relaying timely instructions to the drones. Additionally, it integrates a comprehensive drone energy consumption model that accurately simulates the physics of drone operations and accounts for both internal and external factors affecting energy usage. The framework includes a sophisticated 3D visualization component, depicting drone deliveries from source to destination through a realistic skyway network within an interactive virtual urban environment. It also enables automated data tracking, which is crucial for testing algorithms and collecting data to support data-driven decisions and optimizations. We evaluate the framework by conducting a comprehensive usability test to assess its user interface and overall user experience. Additionally, we test the framework using a drone swarm to execute delivery requests under both simple and complex energy consumption models. The results show that the framework has a user-friendly interface and effectively supports drone delivery simulations under the complex physics-based energy consumption model.

We propose a novelQuality of Experience (QoE)metric as a key criterion for optimizing the composition of energy services within a crowdsourced IoT environment. Two novel composition approaches, namely, Importance-based and Heuristic-based, are proposed to ensure the highest QoE for consumers. The Importance-based approach prioritizes time slots based on their significance. The Heuristic-based approach considers the importance of time slots and the availability of services to maximize QoE while minimizing service provisioning costs. We conduct extensive experiments using real-world datasets to evaluate the effectiveness and efficiency of the proposed approaches. The results demonstrate that both approaches enhance consumer satisfaction by optimizing energy allocation, with the Heuristic-based approach outperforming the Importance-based method in minimizing rewards.

We propose a novel service-based framework for drone service resilience. Our framework monitors inter-drone interference that may lead to drone service failure. We present a novel drone service interference taxonomy to formally identify different interference types in a skyway network. We then propose a heuristic-based approach that leverages spatio-temporal proximity analysis to detect the occurrence of inter-drone interference. In addition, we present an interference severity assessment to quantify their impact on drone services' efficiency. We conduct a set of experiments using real-world datasets to evaluate the effectiveness and efficiency of our proposed approach. The results indicate that the proposed heuristic-based approach detects the occurrence of inter-drone interferences with an accuracy of 95%. In addition, the proposed method is$\approx$70% more efficient than the baseline exhaustive approach and$\approx$48% faster than the K-means approach.

Abstract Social media platforms usually contain several modified versions of an image. This proliferation of versions questions the trust of social media images. We propose a novel framework to find modified versions of social media images using only their metadata. We consider several aspects to determine if an image is a modified version of another image. These aspects include topic of an image, spatio-temporal information, and semantic similarity. We first do topic modeling to find images linked to the same context. Secondly, we perform spatio-temporal clustering to group spatio-temporally close images. Finally, we perform hierarchical clustering to form more precise clusters of versions. Notably, the proposed framework also considers modifications introduced in an image’s metadata while determining versions of the image. Modifications in social media images pose a significant challenge to correctly cluster versions together as a version may exhibit significant deviations from its original image. We address this issue by exploring inconsistencies in the image metadata. These inconsistencies are reflective of the changes in an image. We validate our model on a fact-checked image verification corpus and the Multimodal C4 dataset. We achieve around 95% accuracy, validating the effectiveness of the proposed approach.

Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models is less than 25%. The low accuracy is mainly due to the fluid nature of human movement. Typically, humans do not adhere to rigid patterns in their daily activities, making it difficult to identify hidden regularities in their data. To address this issue, we proposed a web platform to visualize human mobility patterns by abstracting the locations into a set of places to detect more realistic patterns. However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale. Therefore, we extend the platform to visualize the mobility of multiple users from a city-scale perspective. Our platform allows users to visualize a graph of visited places based on their historical records using a modified PrefixSpan approach. Additionally, the platform synchronizes, aggregates, and displays crowd mobility patterns across various time intervals within a smart city. We showcase our platform using a real dataset.

We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods.

We propose a novel activity-based profiling framework to estimate IoT users’ harvested energy based on their daily activities. Energy is harvested from natural sources such as the kinetic movement of IoT users. The profiling framework captures the users’ physical activity data to define activity-based profiles. These profiles are utilized to estimate the harvested energy by IoT users. We train and evaluate our framework based on a real Fitbit dataset.

We propose a novel spatio-temporal service composition framework for crowdsourcingmultipleIoT energy services to cater tomultipleenergy requests. We define a new energy service model to leverage thewearable-basedenergy and wireless power transfer technologies. We reformulate the problem of spatio-temporal service composition to provision multiple energy requests asa matching problem. We leverage thefragmentednature of energy to offerpartialservices to maximize the utilization of energy services. We proposeEnergyFlowComp, a modified Maximum Flow matching algorithm that efficiently provisions IoT energy services to accommodate multiple energy requests. Moreover, we proposePartialFlowComp, an extension of theEnergyFlowCompapproach that considers thepartial-temporaloverlapbetween services and requests in provisioning. We conduct an extensive set of experiments to assess the effectiveness and efficiency of the proposed framework.

We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers variousintrinsic and extrinsicdelivery constraints. We propose to usesupport droneswhose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the next stations. We also use two settings to position the support drones in a flight formation for comparative purposes. Two novelenergy sharingmethods are proposed, namely, Priority-based and Fairness-based methods. A re-ordering method of the delivery drones is presented to facilitate the in-flight energy composition process. An enhanced A* algorithm is implemented to compose the optimal services in terms of delivery time. Experimental results prove the efficiency of our proposed approach.

We propose a novel Quality of Experience (QoE) metric as a key criterion to optimize the composition of energy services in a crowdsourced IoT environment. A novel importance-based composition algorithm is proposed to ensure the highest QoE for consumers. A set of experiments is conducted to evaluate the proposed approaches’ effectiveness and efficiency.

We present DIY-IPS - Do It Yourself - Indoor Positioning System, an open-source real-time indoor positioning mobile application. DIY-IPS detects users' indoor position by employing dual-band RSSI fingerprinting of available WiFi access points. The app can be used, without additional infrastructural costs, to detect users' indoor positions in real time. We published our app as an open source to save other researchers time recreating it. The app enables researchers/users to (1) collect indoor positioning datasets with a ground truth label, (2) customize the app for higher accuracy or other research purposes (3) test the accuracy of modified methods by live testing with ground truth. We ran preliminary experiments to demonstrate the effectiveness of the app.

Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25%. The low accuracy may be justified by the flexible nature of human movement. Indeed, humans are not rigid in their daily movement. In addition, the rigid mobility models may result in missing the hidden regularities in users' records. Thus, we propose a novel perspective to study and analyze human mobility patterns and capture their flexibility. Typically, the mobility patterns are represented by a sequence of locations. We propose to define the mobility patterns by abstracting these locations into a set of places. Labeling these locations will allow us to detect close-to-reality hidden patterns. We present IMAP, an Individual huMAn mobility Patterns visualizing platform. Our platform enables users to visualize a graph of the places they visited based on their history records. In addition, our platform displays the most frequent mobility patterns computed using a modified PrefixSpan approach.

We propose a novel reliability model for composing energy service requests. The proposed model is based on consumers’ behavior and history of energy requests. The reliability model ensures the maximum incentives to providers. Incentives are used as a green solution to increase IoT users’ participation in a crowdsourced energy sharing environment. Additionally, adaptive and priority scheduling compositions are proposed to compose the most reliable energy requests while maximizing providers’ incentives. A set of experiments is conducted to evaluate the proposed approaches. Experimental results prove the efficiency of the proposed approaches.