Effective end-to-end service quality management is critical for successfully adopting edge computing. However, existing solutions lack the necessary integration of key characteristics to identify and provide actionable insights for resolving the root causes of service quality issues. To address this gap, this paper introduces a service quality manager (SeQaM) to improve service quality in edge computing. SeQaM includes distributed observability, adaptive data collection, real-time analytics, rapid feedback mechanisms, and the ability to create controlled events and experimental scenarios. These features are of utmost importance for infrastructure and service providers, application developers, and researchers to implement, test, validate, and benchmark solutions focused on service quality. To accomplish this, SeQaM is composed of distributed and central components. The distributed components are responsible for collecting service quality metrics and implementing feedback mechanisms in edge applications, user devices, network devices, and edge servers. The central components aggregate the collected metrics, perform holistic analysis, and plan corrective actions to enhance service quality. Moreover, SeQaM can be seamlessly deployed across diverse environments, including emulated testbeds, laboratory settings, and real-world infrastructures. Finally, the effectiveness of SeQaM is demonstrated through three use cases, highlighting its capability to provide detailed insights on the causes of service quality issues, generate data for model training, and support data-driven decision-making.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

We present SNAKE, an innovative method that harnesses heat transferred from human touch interactions to estimate product quality. SNAKE offers an accessible and cost-effective solution that seamlessly integrates with existing retail practices; for example, it can be integrated with scales and cashiers already present in shops. Rigorous and systematic experiments demonstrate that SNAKE achieves a high level of accuracy (83%) and outperforms optical sensing and WiFi sensing baselines. We also provide evidence that SNAKE can capture touch interactions of different durations and maintain consistency across diverse user profiles and operating environments. To assess the potential for practical impact, we also carry out an additional user study (N = 100) which suggests that SNAKE has potential to improve consumer purchasing decisions by at least 25% and reduce food waste (or increase promotional opportunities) by 10%–15%. In summary, our contribution offers a novel solution for leveraging smart IoT solutions to support retailing and foster sustainable retail practices.

We demonstrate SPATIAL, a proof-of-concept system that augments modern applications with capabilities to analyze trustworthy properties of AI models. The practical analysis of trustworthy properties is key to guaranteeing the safety of users and overall society when interacting with AI -driven applications. SPATIAL implements AI dashboards to introduce human-in-the-loop capabilities for the construction of AI models. SPATIAL allows different stakeholders to obtain quantifiable insights that characterize the decision making process of AI. This information can then be used by the stakeholders to comprehend possible issues that influence the performance of AI models, such that the issues can be resolved by human operators. Through rigorous benchmarks and experiments in a real-world industrial application, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness. However, this, in turn, increases the complexity of developing and maintaining the systems implementing AI. Our work paves the way towards augmenting modern applications with trustworthy AI mechanisms and human oversight approaches.

Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of- concept architecture that analyzes AI models in a human-in-the- loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in real- world industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight.

The inherent social characteristics of humans make them prone to adopting distributed and collaborative applications easily. Although fundamental methods and technologies have been defined and developed over the years to construct these applications, their adoption in practice is uncommon because end-users may be puzzled about how to use them without much hassle. Indeed, commonly, these applications require a certain level of technical expertise and awareness to use them correctly. Fortunately, AI-chatbot interventions are envisioned to assist and support various human tasks. In this paper, we contribute pervasive chatbots as a solution that fosters a more transparent and user-friendly interconnection of devices in distributed and collaborative environments. Through two rigorous user studies, firstly, we quantify the perception of users toward distributed and collaborative applications (N = 56 participants). Secondly, we analyze the benefits of adopting pervasive chatbots when compared with the chatbot reference model designed for assistance and recommendations (N = 24 participants). Our results suggest that pervasive chatbots can significantly enhance the practicability of distributed and collaborative applications, reducing the time and effort needed for collaboration with surrounding devices by 57%. With this information, we then provide design and development implications to integrate pervasive chatbot interventions in distributed and collaborative environments. Moreover, challenges and opportunities are also provided to highlight the remaining issues that need to be addressed to realize the full vision of pervasive chatbots for any multi-device application. Our work paves the way towards the proliferation of sophisticated and highly decentralized computing environments that are easily interconnected.

Hand-grip strength is a widely recognized indicator of muscle strength and overall health of individuals, particularly among older adults. Hand-grip strength measurements are typically obtained using dynamometers or specifically tailored devices, limiting the context in which measurements can be taken to health checks and clinical settings. In this demo, we showcase a new smart ring, namely HIPPO. The smart ring implements an innovative approach that offers a non-intrusive and opportunistic way to extract handgrip strength measurements from individuals. HIPPO re-purposes off-the-shelf light sensors available in existing wearable devices, e.g., smartwatches, and exploits the principle of light reflectivity, such that as an individual interacts with everyday objects, changes in their surfaces can be used to derive the hand-grip measurements.

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis of data. This in turn limits the scale and complexity of the applications that can operate based on these data. In this article, we develop underwater fog computing support using low-cost micro-clouds and demonstrate how they can be used to deliver cost-effective support for data-heavy underwater applications. We develop a proof-of-concept micro-cloud prototype and use it to perform extensive benchmarks that evaluate the suitability of underwater micro-clouds for diverse underwater data science scenarios. We conduct rigorous tests in both controlled and field deployments, using river and sea waters. We also address technical challenges in enabling underwater fogs, evaluating the performance of different communication interfaces and demonstrating how accelerometers can be used to detect the likelihood of communication failures and determine which communication interface to use. Our work offers a cost-effective way to increase the scale and complexity of underwater data science applications, and demonstrates how off-the-shelf devices can be adopted for this purpose.

We contribute MIDAS as a novel sensing solution for characterizing everyday objects using thermal dissipation. MIDAS takes advantage of the fact that anytime a person touches an object, it results in heat transfer. By capturing and modeling the dissipation of the transferred heat, e.g., through the decrease in the captured thermal radiation, MIDAS can characterize the object and determine its material. We validate MIDAS through extensive empirical benchmarks and demonstrate that MIDAS offers an innovative sensing modality that can recognize a wide range of materials – with up to 83% accuracy – and generalize to variations in the people interacting with objects.

Mobile Mesh Social Network (MMSN) represents an environment where the mobile device users are capable of performing various virtual social network activities such as sharing information, forming social groups, text messaging when they encounter each other in the physical vicinity within the wireless network range. Moreover, the characteristics of MMSN such as the Internetless activities and Wireless Mesh Network (WMN)-based connectivity provides various potentials including but not limited to business opportunities, scalable crowdsourcing or crowdsensing deployment, edge computing and so on. Although there exist a fair number of software platforms that help developers to implement MMSN, they still cannot fully overcome the limitation derived from the hardware resource constraint nature of the participative mobile devices. In order to enhance the MMSN in terms of cost efficiency, we introduce Fog Social Network (FSN) model, which utilises the computing and networking resources in users' close vicinity to improve the overall efficiency of MMSN. Further, the proposed FSN framework consists of an adaptive resource-aware cost-performance index (CPI) scheme, which performs dynamic approach selection autonomously at runtime to choose the most efficient route for the delivery of the messages for MMSN activities. With this intention, we have implemented and validated a proof-of-concept prototype.

Emerging modern smartphones are powerful and capable of exchanging larger files in a peer-to-peer manner. Such a connection can be used in opportunistic networks where the permanent end-to-end path does not exist. In this study, we present an energy-aware forwarding protocol for large data messages. We implemented a mobile peer-to-peer network application to forward messages over the Wi-Fi link. The preliminary experimental result shows that the proposed protocol can optimise the energy consumption across the network.

The distant data centre-centric Internet of Things systems face the latency issue especially in the real-time-based applications. Recently, Fog Computing models have been introduced to overcome the latency issue by utilising the proximitybased computational resources. However, the increasing users of Fog Computing servers will cause bottleneck issues and consequently the latency issue arises again. This paper introduces the utilisation of Mist Computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of IoT networks. The proposed service-oriented mobile-embedded Platform as a Service framework enables the edge IoT devices to provide a platform that allows requesters to deploy and execute their own program models. The framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. Additionally, the framework also supports task distribution among a group of Mist nodes. The prototype has been tested and performance evaluated on the real world devices.

Emerging Mobile Phone Sensing (M-Sense) systems enable a flexible large scale wireless sensing capability and also reduce the need of establishing the infrastructure of Wireless Sensor Network for collecting sensory information in the Internet of Things applications. M-Sense has been applied in numerous scenarios including mobile-health systems, environmental monitoring, vehicle ad hoc network, mobile social network, and so on. The drawback of existing M-Sense systems in terms of privacy, trust, less efficiency of participating in multiple sensing networks, has motivated the next generation sensing service provisioning approach. This paper introduces a generic service-oriented Mobile Host Sensing as a Service provisioning framework that allows a mobile device to provide sensing data to multiple parties based on mobile Web services. The proposed framework consists of the hybrid workflow-based control system, the dynamic Utility Cloud service, and the service provisioning scheduling model to enhance the quality of service provisioning. The prototype has been tested on real mobile devices and the details of the performance evaluation are presented.

Internet of Things (IoT) represents a cyber-physical world where physical things are interconnected on the Web. This paper presents an architecture designed for Energy-efficient Inter-organizational wireless sensor data collection Framework (EnIF). Environmental monitoring and urban sensing are two major application scenarios in IoT. Different from the traditional sensor environments, environmental sensing in IoT may require battery-powered nodes to perform the sensing tasks. Such a requirement raises a critical challenge to ensure that sensor data gathering can be collected in a timely and energy-efficient manner. Although numerous energy-efficient approaches for IoT scenarios have been proposed, previous works assumed the entire network was managed by a single organization in which the network establishment and communication have been pre-configured. This assumption is inconsistent with the fact that IoT is established in a federated network with heterogeneous devices controlled by different organizations. The aim of the framework is to enable a dynamic inter-organizational collaborative topology towards saving energy from data transmissions using a service-oriented architecture.