Context-aware IoT applications demand fresh, relevant context under strict latency constraints to make real-time decisions. Context Management Platforms (CMPs) serve as middleware to deliver context information efficiently. To provide real-time responses, caching context becomes essential. However, due to the dynamic characteristics of context, it updates and changes far more frequently than traditional IoT data, leaving conventional IoT data caching techniques insufficient. In this paper, we propose VACF (Volatility-Aware Adaptive Context Caching Framework), approach that monitors how rapidly each context changes and adapts caching policies accordingly. Our framework introduces the Context Volatility Index (CVI) to measure change rates for different context attributes(CAs), uses priority analysis through AHP to determine attribute importance, weights and combines these with other metrics like context freshness(CF) and probability of access(PoA) to make intelligent caching decisions. VACF employs two decision methods. By continuously monitoring context volatility and adjusting cache refresh schedules accordingly, VACF keeps frequently changing context fresh. Through experimental evaluation on a real-world Melbourne city testbed handling up to 85,692 queries per hour during roadwork and incident scenarios, we demonstrate that VACF achieves a cache hit rate of 97% during stable conditions and maintains a hit rate of 85% during volatile incidents, while reducing data fetches by 2-3× and reducing query delays by more than 50% compared to existing approaches.

Context management platforms (CMPs) address the need for provisioning context to support Context-Aware Mobile IoT applications. Such applications often require the context to be delivered under strict constraints including latency, freshness etc. With the explosion of IoT deployment and increased adoption of IoT applications, caching context at the edge becomes essential to meet these strict constraints. However, existing edge caching approaches, designed for static content or IoT data, do not account for the dynamic nature of context. In this paper, we propose Edge Context Caching Framework (ECCF), an adaptive and distributed edge-based context caching architecture. ECCF introduces three components: (i) a Context Variability Assessment Engine (CVAE) that quantifies how rapidly context changes and maps this variability to adaptive refresh intervals, (ii) an edge-based Local Caching Value Function (LCVF), which integrates multiple metrics including variability, freshness, access probability, network costs and provider SLAs to compute a utility value for improved caching decisions, and (iii) a gossip-based coordination protocol that enables edge nodes to exchange cache summaries for distributed replication and consistency among edge nodes. We implement ECCF on a Raspberry Pi 5-based real-world edge testbed and evaluate it using real-world smart city workloads with up to 85,692 queries per hour. Compared to seven state-of-the-art baselines, ECCF achieves 97% hit ratios under stable conditions and 85% during high volatility scenarios, reduces provider fetch rates by 2-3 ×, and maintains gossip overhead below 2 % of bandwidth.

In industrial environments, most production-related activities performed by human operators are often complex. Accurate detections of these activities are pivotal as it can greatly help to assess productivity that can lead to improvement in worker training, as well as in other scenarios ensure a safe work environment and reducing injuries. Existing datasets on wearable Internet of Things (IoT) for human activity recognition primarily focuses on general activities, such as walking, running, etc., and therefore, related machine learning models and datasets are not suitable for application to industrial environments. In this paper, we present a novel dataset for classifying human operator activities in a meat processing plant where production line operators use knives to cut, process and produce meat products. Our dataset contains human operator activity data captured using wearable IoT sensors collected from a meat processing production facility. Through extensive experiments using machine and deep learning, we demonstrate that our dataset is effective and useful for detecting different activities of a human operator working in an industrial environment. To the best of our knowledge, this is the only real-world IoT dataset that will be made publicly available to support further research into industrial activities recognition. Our dataset and related experiments are available at https://digitalinnovationlab.github.io/mppdataset.

Internet of Things (IoT) sensor data, which capture time series physical measurements such as temperature and humidity, often lack proper classification. This limits their effective understanding, integration, and reuse. While sensor metadata—textual descriptions of the measurements—is sometimes available, it is frequently incomplete or ambiguous. As a result, classification often depends solely on the time series data. Leveraging both time series sensor readings and textual metadata for automated and accurate classification remains a challenge due to the heterogeneity and inconsistency of these data sources. In this paper, we propose DeepMetaIoT, a multimodal deep learning framework that integrates time series and textual data for classification. DeepMetaIoT employs a cross-residual architecture comprising a time series encoder and a text encoder based on a pre-trained large language model, enabling effective fusion of both modalities. Experimental results on real-world IoT sensor datasets show that DeepMetaIoT consistently outperforms state-of-the-art machine learning and deep learning baselines.

Producing high-quality product consistently is crucial in manufacturing, as discarding or reprocessing low-quality products increases waste and energy consumption and reduces overall production efficiency. Ensuring high-quality manufactured products is challenging due to relying on human activities for product quality and related consistency assessment, which is often performed postproduction instead of assessing these during each production run and making real-time production adjustments that can mitigate product quality and related consistency issues. In this article, we proposes a novel machine-state learner algorithm that captures the dependencies between product quality and related consistency and machine data (specifically the machine settings and corresponding sensor data). In addition, this article shows how this novel machine-state learner algorithm can be used to predict product quality during the production runs and how such prediction are used to make machine setting recommendations that mitigate product quality and related consistency issues before or during the production runs. These advances in machine state-based data modeling, predictive data analysis and recommendation are incorporated into an inline prediction and decision support system that achieves significant improvement in producing high-quality products consistently by guiding decision-making via recommendations during production in a digital manufacturing environment. In this article, we present an evaluation of the above contributions in a real-world manufacturing plant and yield double digit first pass and nearly perfect second pass product improvements in terms of product quality and related consistency and production efficiency.

The Internet of Things (loT) ecosystem is rapidly evolving, offering unprecedented opportunities to address complex challenges in various sectors, including mobile services through intelligent decision-making and actuation. Central to the functionality of context-aware IoT applications in mobile environments is the ability to access and utilize context in-formation in a timely manner, a task facilitated by Context Management Platforms (CMPs) a cloud based system. This paper introduces “CAPME” (Context Access Probability Monitoring Engine), a novel hybrid strategy designed to optimize the caching of context information in CMPs. We introduce Probability of Access (PoA) as a key metric to ensure that context information critical for real-time decisions and actuations by mobile IoT applications are readily available. CAPME integrates Multi-Attribute Utility Theory (MAUT) with a Deep Q-Network (DQN) reinforcement learning algorithm, offering a dynamic system that precisely assesses the utility of various context attributes. This hybrid algorithm considers factors such as usage frequency, cost, timeliness, context freshness, and quality of context, enabling adaptive prioritization that aligns with the ever-changing de-mands of mobile IoT environments and applications. Through a comprehensive experimental evaluation utilizing data obtained from a real-world mobile IoT applications we showcase the capability of CAPME to significantly improve the performance of context caching. Results indicate marked enhancements in cache hit rates, reduced response times/latency, and lower operational costs, demonstrating CAPME's effectiveness over conventional caching methods used to support context-aware IoT applications in mobile environments.

Demand for orbital image data is increasing at a pace much faster than down-link capacity to move this data to ground stations for processing. Space Edge Computing for DL-based analysis of orbital image data (categorization/change detection) offers a promising solution. Dynamic deployment of DL models to space edge computing devices is desirable but significantly constrained by uplink bottlenecks, hardware limitations and power budgets. This paper proposes a selection methodology for dynamic deployment of DL models for an on-orbit context. Making use of the Once-for-all (OFA) framework, our proposed solution considers required ML accuracy performance, upload availability and hardware limitations for time-critical, earth observation scenarios with the goal of reducing the number of orbital periods required. Groundstation aware orbital simulations are performed to determine maximum transmission size for a given time window to determines the maximum network size. This combined with space edge computing hardware limitations are used as input for a suitable OFA sub-network. Extensive experimental evaluation using space, ground scenarios that consider orbit position are used to evaluate the efficacy of the proposed selection methodology.

In Smart City applications, urban mobility involves complex interactions between traffic infrastructure, diverse road users, and physical environment. This paper addresses the limitations of conventional scene modeling methods that often fail to capture the varied and volatile nature of urban road scenes, particularly in representing the dynamic situations that unfold within them. Inaccurate situation representation hinders precise depiction of scene evolution, limiting our ability to understand and respond effectively to complex urban situations. This paper addresses these challenges by presenting the novel concept of the Context-Aware Scene Graph (CSG) for representing situations used in reasoning applications for enhancing safety and efficiency in urban environments, particularly for bicycle riders. CSG integrates multi-modal data, including ontological knowledge, sensor data, and images, to provide a comprehensive representation of urban road situations, enabling informed decision-making. This paper also validates the effectiveness of the proposed approach using real- world IoT datasets and camera images, with a focus on the bicycle dooring use case. The results outperform existing scene modeling methods by accurately representing situations, including those previously overlooked. The approach also ensures consistent representation, completeness, and captures transitions between situations, including causal relations. These findings highlight our approach's effectiveness in improving road safety, efficiency, and urban life quality through enhanced scene understanding.

Abstract The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide infrastructure, including roadside infrastructure such as road signs and the cleaning of illegally dumped rubbish. Currently, these maintenance tasks rely predominantly on citizen reports or on-site checks by council staff. However, this approach has been shown to be time-consuming and highly costly, resulting in significant delays that negatively impact communities. This paper presents AIoT-CitySense, an AI and IoT-driven city-scale sensing framework, developed and piloted in collaboration with a local government in Australia. AIoT-CitySense has been designed to address the unique requirements of roadside infrastructure maintenance within the local government municipality. A tailored solution of AIoT-CitySense has been deployed on existing waste service trucks that cover a road network of approximately 100 kms in the municipality. Our analysis shows that proactive detection for roadside infrastructure maintenance using our solution reached an impressive 85%, surpassing the timeframes associated with manual reporting processes. AIoT-CitySense can potentially transform various domains, such as efficient detection of potholes and precise line marking for pedestrians. This paper exemplifies the power of leveraging city-wide data using AI and IoT technologies to drive tangible changes and improve the quality of city life.

Geo-distributed web-applications (GWA) can be deployed across multiple geographically separated datacenters to reduce the latency of access for users. Finding a suitable deployment for a GWA is challenging due to the requirement to consider a number of different parameters, such as host configurations across a federated infrastructure. The ability to evaluate multiple deployment configurations enables an efficient outcome to be determined, balancing resource usage while satisfying user requirements. We proposeGeoDeploy, a framework designed for finding a deployment solution for GWA. We evaluateGeoDeployusing both a formal algorithmic model and a practical cloud-based deployment. We also compare our approach with other existing techniques.

The Sensor Sharing Marketplace (SenShaMart) enables IoT applications to find IoT sensors, which are owned and managed by other parties, integrate them, and pay for using their data. To provide corresponding services that implement that FAIR (Findable, Accessible, Interoperable, Reusable) principles of IoT, SenShaMart incorporates a specialized blockchain that manages all the information its services need to allow different parties in IoT to describe, query, integrate, pay for, and use IoT sensors and their data. The paper presents the SenShaMart's architecture, implementation, evaluation, and demonstration.

With increased deployment of IoT application on cloud platforms, assessing the performance of such application is an open problem. Currently, approaches are limited to legacy database-based applications and does not cater for the needs of IoT applications. This paper proposes, implements and validates a framework namely, IoTQGen, that can generate situation-based queries to conduct performance evaluation of IoT application hosted by cloud IoT middleware platform. The framework comprises: (i) a model to capture the query requirements of IoT applications; (ii) a data generator to generate IoT data based on specified configuration; and (iii) a set of queries designed to represent data analytic IoT applications. The framework supports different query types that can be typically used to represent and address IoT application scenarios. An important functionality of the framework is its ability to issue queries based on dynamic changes in the state of IoT entities (situations). The framework is evaluated based on two smart city use cases to highlight how the framework can be used to generate complex and dynamic queries tailored for IoT application scenarios.

Context-awareness (CA) has become an evolving trend, especially in the domain of Internet of Things (IoT). With the progress of IoT, the necessity for accessing real-time contextual information has become a critical factor for the advancement of IoT applications. Context management platforms (CMPs) have been proposed in the literature to support the needs of such Context-aware IoT applications. However, there are still significant gaps in terms of supporting the increasing needs of Context-aware applications, including the performance analysis of CMPs. In this paper, we propose a scene-graph based approach to generate context queries which primarily intends to support the performance analysis of CMPs and its ability to support plethora of Context-aware IoT application needs. Given the situation driven nature of IoT applications, the ability to generate relevant queries needs to be very realistic. Hence, we propose a novel Situation State Machine based approach to capture and model real-world situations. To demonstrate the potential to generate relevant context queries based on dynamic situations, a bicycle dooring use case is considered. We then present a template-based query generation approach to create realistic queries that represent real-world IoT application environment. The dooring use case is considered to validate the ability to represent complex queries, and the ability to generate complex queries in linear time.

The unprecedented growth of Internet of Things (IoT) is leading to its increased usage in various domains, such as manufacturing, health, and smart cities. A majority of IoT applications are autonomic, i.e., they operate under minimal/no human intervention, and make decisions/actuations based on machine-to-machine communication and data analytics. A key challenge in the development of such applications is the ability to measure their quality while they are working in a diverse and heterogeneous IoT ecosystem. In this article, we propose an agent-based IoT-Quality Watch (IoT-QWatch) framework that provides the ability to measure IoT quality metrics at each stage of the autonomic IoT application life cycle running in the IoT ecosystem. We envision that IoT-QWatch will enable the development of a new generation of quality-aware autonomic IoT applications that are able to be resilient to the heterogeneous and uncertain nature of IoT ecosystems. We present architectural details and implementation of IoT-QWatch, and corresponding models used to measure IoT quality metrics at different stages. We conduct extensive experiments using a real-world IoT test bed from the domain of manufacturing to validate the efficacy of IoT-QWatch. Experimental outcomes provide promising results in realizing IoT-QWatch in real-world deployment, while the framework itself offers significant extensibility to include new models for measuring IoT quality metrics.

Public Internet of Things (IoT) platforms, such as Thingspeak, significantly increased the availability of open IoT data and enabled faster and cheaper development of novel IoT applications by reducing or even eliminating the need for deploying their own IoT sensors and platforms. However, open IoT data is often heterogeneous, sparse, fuzzy, and lacks accurate description (which we refer to as IoT metadata). These limitations make open IoT data challenging to integrate and use, and prevent the efficient development of IoT applications. In fact, while several sensor data description models have been proposed and standardized, open IoT data currently lack or include only partial metadata description. Therefore, novel techniques for automatically annotating open IoT data are needed to fully unleash the power of open IoT. This article proposes a novel metadata-assisted cascading ensemble classification framework (MACE) for the automatic annotation of IoT data. MACE is capable of sequentially combining standalone classifiers, enabling it to cope with heterogeneous IoT data and different domains of information (e.g., numerical and textual), which have not been considered previously. MACE incorporates a novel ensemble approach for automatically selecting, sorting, filtering, and assembling classifiers in a way that improves annotation performance. This article presents extensive experimental evaluations of MACE using public IoT data sets. Results demonstrate that the MACE framework significantly outperforms existing solutions for open IoT data by as much as 10% in classification accuracy.

The unprecedented growth of Internet of Things (IoT) underpinned by machine to machine communication, analytics and actuation is spearheading the development of autonomic IoT applications in areas such as Smart Cities. Such autonomic IoT applications have minimal human involvement in the decision making and actuation process. A key challenge in developing such autonomic IoT applications is uncertainty in the data produced by the IoT devices with data freshness being a critical aspect. In this paper, we address this challenge by introducing Age of Data (AoD), a metric to quantify the freshness of the data produced by IoT devices. We analyse the impact of AoD on IoT applications and propose a model for computing AoD that can be used by IoT applications in the decision making process. We validate the proposed model via experimental evaluations using real-world data obtained from parking sensors. Our analysis found that in real-world scenarios, 21.4% of sensors provide data that is outdated by several hours. We show that incorporating AoD in the application logic leads to improved application decision making.

We observe significant interest in reinforcement learning methods for real-world sensing-control scenarios driven by the sensor data streams. However, the delay introduced to the data by the communication channels may degrade the system's performance. It is especially crucial in the internet of things (IoT), where devices with constraint resources and low throughput networks are used.

Timely detection of roadside assets that require maintenance is essential for improving citizen satisfaction. Currently, the process of identifying such maintenance issues is typically performed manually, which is time consuming, expensive, and slow to respond. In this paper, we present Mobile IoT-RoadBot, a mobile 5G-based Internet of Things (IoT) solution, powered by Artificial Intelligence (AI) techniques to enable opportunistic real-time identification and detection of maintenance issues with roadside assets. The Mobile IoT-RoadBot solution has been deployed on 11 bin service (waste collection) trucks in the western suburbs of Melbourne, Australia, performing real-time assessments of road-side assets as they service areas within the local government. We present the architecture of Mobile IoT-RoadBot and demonstrate its capability via an online 'points of maintenance' (PoMs) map.

The significant advances in the Internet of Things (IoT) have led to IoT applications being widely used in various scenarios ranging from smart city, smart farming, to Industrial IoT (IIoT) solutions. With the explosion of IoT application development, IoT middleware platforms are increasingly being used for hosting such IoT applications. This has given rise to the need for developing benchmarking solutions to analyze and test the performance of different middleware platforms that host these IoT applications. To develop such benchmarks, there are a number of key components that are needed. One of these components is an IoT dataset. To generate such datasets, representing IoT application requirements in a general and formal way is important. In this paper, we propose a framework to model the IoT Applications Requirements and enable Data Generation(ARDG-IoT). The framework supports a formal way to capture IoT application requirements and use these requirements to generate IoT data that can be used to create benchmarks for different IoT middleware platforms. ARDG-IoT consists of our proposed model, IoTSySML, which captures the application requirements, and an IoT data simulator tool, which is used to generate IoT data. We present an evaluation of the framework using a real world Industrial IoT application case study.

The traditional brick-and-mortar retail stores seek options to expand when they reach a certain point of growth. Such expansions can be in the form of a bigger retail store or opening additional retail stores. Most businesses leverage heuristics-based decisions to expand (often driven by financial performance). Existing literature on retail store expansion decisions fails to provide a complete view of factors that can influence the decision-making process. To address this gap in literature, this paper aims to identify the key factors that influence retail store expansion decisions. Our case study-based methodology is developed around a decade of data collected from 500 service-based brick-and-mortar retail stores operating in Australia and New-Zealand. Through an in-depth analysis of the literature and insights drawn from 10 years of operational data, we establish a list of factors that need to be considered to support retail store expansion decisions and drill down on the key factors that influence the decision-making process. Lessons learnt from the analysis of the data concludes the paper.

Summary Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.

A chatbot is a software that interacts with humans by conducting conversations via textual or auditory methods. Chatbots have recently been used for plethora of applications including travel, medical, education, retail etc. Several cloud-based platforms (e.g. IBM, Amazon, Google, Microsoft) are available for developing and deploying chatbots. However, there is a lack of an evaluation methodology and a tool for evaluating chatbots comprehensively. Current approaches for comparing cloud-based chatbots are manual and rely on expert's judgement. In this short paper, we propose, devise, implement and demonstrate a tool namely ECHO for empirical evaluation of cloud-based chatbots. ECHO is capable of automatically evaluating multiple cloud-based chatbots and report the outcomes of the comparative evaluation. We validate the efficacy of ECHO by conducting comparative evaluation of 3 popular cloud-based chatbots in 2 different question-answering application scenarios with 3 levels of complexities.

Smart buildings equipped with various building management systems and digital control systems produce enormous amounts of sensor data that can be used to investigate and diagnose operational issues such as unsatisfactory thermal comfort outcomes, excessive energy consumption and/or predicting failures before they occur. However, current building management systems often face the issues with incomplete or unstructured metadata associated with sensor data which prevent such pro-active, predictive and prescriptive analysis. Currently, building service engineers manually map the sensor data streams to aid their diagnostic process. This process is expensive, ineffective and is also prone to human errors. This paper proposes a novel semi-automated approach that annotates incoming sensor data streams. We also propose extensions to Project Haystack, a well-known ontology used for naming conventions and taxonomies for building equipment and operational data. We have developed a tool that is currently used by our industry partner and incorporates the proposed automatic annotation approach and maps the data streams to our Haystack-extended ontology. The tool includes an easy to use interface for engineers to easily diagnose issues in mechanical building services. The proposed approach has been validated via both usability and technical evaluation.

Smart mobile devices coupled with the Internet of Things (IoT) and Artificial Intelligence (AI) have emerged as a key enabler of modern digital health applications. While cloud computing is now a well established paradigm for analysing IoT captured data in mobile health applications, on-board analysis of data using AI approaches such as Deep Learning (DL) is gaining significant momentum. This is driven primarily by advances in on-board resources enabling modern mobile devices to execute complex DL models, while also offering improved response time and accuracy for rapid decision-making, and enhanced user privacy. While the number of mobile digital health applications that use IoT and DL is increasing, progress is currently impeded by a lack of framework for profiling and evaluating the performance of DL models on mobile devices. To this end, we propose MobDL, a framework for profiling and evaluating DL models running on smart mobile devices. We present the architecture of this framework and devise a novel evaluation methodology for conducting quantitative comparisons of various DL models running on mobile devices. Three diverse digital health applications using heterogeneous data (e.g. image, time series) are introduced. We conduct extensive experimental evaluations using several DL models that have been developed using the data sets obtained for the three digital health applications to validate the effectiveness of the proposed MobDL framework.

Industry 4.0 involves enhancing industrial processes with high-fidelity and high-value information from machines, workers, and products. Industry 4.0 applications improve production efficiency, product quality, etc., by using Internet of Things (IoT) and Artificial Intelligence (AI). Existing industry 4.0 application development approaches are centered on commercial IoT platforms that provide siloed development and runtime environments (leading to vendor lockdown) and only support individual sensors and actuators instead of entire machines. Therefore, Industry 4.0 applications need to construct representations of complex machines from such basic elements, which is a costly, error-prone, inefficient hindering portability across machines and plants. This paper proposes Cyber Twins, a comprehensive solution for efficient Industry 4.0 application development, testing, and portability. The Cyber Twins solution includes a model for machine representation and services that facilitate Industry 4.0 application development. Finally, a prototype Cyber Twin implementation is presented, with its functionality described using a sample Industry 4.0 application.

The Internet of Things (IoT) is growing fast and is gaining significant adoption in areas such as smart cities and manufacturing. The variety and low-cost of IoT devices with excellent audio/visual sensors is fueling the growth of multimedia IoT applications, many of which are bandwidth-hungry and time-sensitive (i.e., must produce their results within an application specific time-sensitive requirement). While a large body of related work has studied distribution of such Time Sensitive Multimedia IoT (TS-MIoT) applications in simulated environments, there is lack of a platform that can be used to experiment with techniques for meeting their time-sensitive and computing resource requirements on real-world IoT infrastructure, i.e., a combination of IoT devices, close by computers and a cloud data centre connected by a variety of networks. This paper proposes APOLLO, a platform for experimental analysis of TS-MIoT applications. APOLLO provides mechanisms to load TS-MIoT application execution plans and execute the plans on available IoT infrastructure. We describe a proof-of-concept implementation using Orleans and present experimental evaluations to validate APOLLO's ability to support the experimental analysis of TS-MIoT applications.

Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.

The Industrial Internet of Things (IIoT) is a key pillar of the Fourth Industrial Evolution or Industry 4.0. It aims to achieve direct information exchange between industrial machines, people, and processes. By tapping and analysing such data, IIoT can more importantly provide for significant improvements in productivity, product quality, and safety via proactive detection of problems in the performance and reliability of production machines, workers, and industrial processes. While the majority of existing IIoT research is currently focusing on the predictive maintenance of industrial machines (unplanned production stoppages lead to significant increases in costs and lost plant productivity), this paper focuses on monitoring and assessing worker productivity. This IIoT research is particularly important for large manufacturing plants where most production activities are performed by workers using tools and operating machines. With this aim, this paper introduces a novel industrial IoT solution for monitoring, evaluating, and improving worker and related plant productivity based on workers activity recognition using a distributed platform and wearable sensors. More specifically, this IIoT solution captures acceleration and gyroscopic data from wearable sensors in edge computers and analyses them in powerful processing servers in the cloud to provide a timely evaluation of the performance and productivity of each individual worker in the production line. These are achieved by classifying worker production activities and computing Key Performance Indicators (KPIs) from the captured sensor data. We present a real-world case study that utilises our IIoT solution in a large meat processing plant (MPP). We illustrate the design of the IIoT solution, describe the in-plant data collection during normal operation, and present the sensor data analysis and related KPI computation, as well as the outcomes and lessons learnt.

Emotion has been demonstrated to play an important role in the learning process. The capturing of these emotions is increasingly being digitally automated as researchers explore how technology can enhance student learning as well as teaching methodologies. More specifically, what emotions should be captured and how information about the captured emotions should be visualised has not been well researched in the context of teaching and learning. The study presented in this paper aims to produce a proof of concept prototype, called Sens-e-motion, that integrates multiple types of digital emotional monitoring and visualises the results in a meaningful way to the users. The monitoring includes facial expression recognition, blink-rate, text sentiment analysis, and keyword analysis. The prototype was designed and implemented as a web chat application with a RESTful web service backend and a NoSQL database. In this paper, we present the design of our system and describe technical details of it. Possible future research and development is also discussed.

Air quality is an important factor in planning activities in our everyday life. The information presented though captured data using Internet of Things (IoT) in smart cities is mostly single-dimensional where citizens do not have much opportunities to directly interact with the system to get personalised insights. Recent years have seen dire reports of extreme air pollution in mega cities around the world, which has led to government authorities grappling with solutions. Taking into account the existing IoT sensor setup in smart cities, it is now very convenient to visually explore the level of pollution of any places in real-time. In this context, this paper presents AqVision, a flexible visualisation tool for future citizens in smart cities that combines personalised awareness with generalised needs and leverages to envisage air pollution hotspots using more interactive manners considering individualised health and safety concerns.

Open multidimensional data from existing sources and social media often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, users can more easily interact with and pick out meaningful information from a large dataset. In this paper, we present VisCrime, a system that uses visual analytics to maps out crimes that have occurred in a region/neighbourhood. VisCrime is underpinned by a novel trajectory algorithm that is used to create trajectories from open data sources that reports incidents of crime and data gathered from social media. Our system can be accessed at http://viscrime.ml/deckmap

Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source . These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC’s are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time . The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection . Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC’s with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.

Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS-Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

This special issue asked for high quality original research papers (including smart city experience papers) that made significant contributions to the state-of-the-art in "method and techniques to build sustainable smart city solutions" research area. Rapid urbanization is a global megatrend with 66 percent of the world’s population expected to live in urban areas by 2050. The staggering exponential increase in urbanization is leading to more people migrating to major cities in the search of better opportunities and quality of life. Cities need to increase the efficiency in which they operate and use their resources sustainability in order to meet the demands imposed by rapid urbanisation. The challenge is to continue providing basic resources such as sufficient fresh water; cleaner energy; transportation alternatives to commute efficiently from one place to another; adaption to changing climatic conditions; safety and security; while also ensuring economical, social, and environment sustainability.

The Internet of Things (IoT) is a new internet evolution that involves connecting billions of internet-connected devices that we refer to as IoT things. These devices can communicate directly and intelligently over the Internet, and generate a massive amount of data that needs to be consumed by a variety of IoT applications. This paper focuses on the automatic contextualisation of IoT data, which also involves distilling information and knowledge from the IoT aiming to simplify answering the following fundamental questions that often arises in IoT applications: Which data collected by IoT are relevant to myself and the IoT Things I care for? Related work around context management and contextualisation ranges from database techniques that involve query re-writing, to semantic web and rule-based context management approaches, to machine learning and data science-based solutions in mobile and ambient computing. All such existing approaches have two main aspects in common: They are highly incompatible and horribly inefficient from a scalability and performance perspective. In this paper, we discuss a new RISC Contextualisation Framework (RCF) we have developed, implemented key aspects of, and assess its scalability. RCF provides fundamental contextualisation concepts that can be mapped to all existing contextualisation approaches for IoT data (and in this sense, it provides a common denominator that unifies the contextualisation space). RCF can be easily implemented as a cloud-based service, and provides better scalability and performance that any of the existing content management and contextualisation approaches in the IoT space.

The Internet of Things (IoT) is the latest Internet evolution that interconnects billions of devices, such as cameras, sensors, RFIDs, smart phones, wearable devices, ODBII dongles, etc. Federations of such IoT devices (or things) provides the information needed to solve many important problems that have been too difficult to harness before. Despite these great benefits, privacy in IoT remains a great concern, in particular when the number of things increases. This presses the need for the development of highly scalable and computationally efficient mechanisms to prevent unauthorised access and disclosure of sensitive information generated by things. In this paper, we address this need by proposing a lightweight, yet highly scalable, data obfuscation technique. For this purpose, a digital watermarking technique is used to control perturbation of sensitive data that enables legitimate users to de-obfuscate perturbed data. To enhance the scalability of our solution, we also introduce a contextualisation service that achieve real-time aggregation and filtering of IoT data for large number of designated users. We, then, assess the effectiveness of the proposed technique by considering a health-care scenario that involves data streamed from various wearable and stationary sensors capturing health data, such as heart-rate and blood pressure. An analysis of the experimental results that illustrate the unconstrained scalability of our technique concludes the paper.

Summary A large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright © 2016 John Wiley & Sons, Ltd.

Special issue on Big Data and Cloud of Things (CoT)Cloud computing and Internet of Things (IoT) are two technologies that are already becoming part of our daily lives and are attracting significant interest from both industry and academia.The Cloud of Things (CoT) is a vision inspired from the IoT paradigm where everyday devices, namely, 'smart objects', are fully connected to the internet and are integrated with the cloud.It is expected the IoT will grow to 35 billion units by 2020, making it one of the main sources of 'Big Data' with characteristics such as volume, heterogeneity, complexity, velocity, and value.In recent years, IoT has given rise to a number of new CoT paradigms (but not limited to) including: Sensing-as-a-Service, Sensing-and Actuation-as-a-Service, Video-Surveillance-as-a-Service, Big Data Analytics-asa-Service, Data-as-a-Service, Sensor-as-a-Service, and Sensor-Event-as-a-Service. Cloud computing is a more mature technology compared to IoT.It can offer virtually unrestricted capabilities (e.g., storage and computation) to support IoT services and application that can exploit the data produced from IoT devices.The cloud essentially acts as a transparent layer between the IoT and applications providing flexibility, scalability, and hiding the complexities between the two layers (IoT and applications).However, the integration of cloud and IoT into Cloud of Things is not straightforward and imposes several challenges.These challenges include IoT device and service discovery, IoT device integration, big data management and analytics, cloud monitoring and orchestration for distributed IoT applications, mobility issues in cloud access, privacy and security, and SLA management for both cloud and IoT.Specific attention must be paid to address a range of issues from IoT data collection, storage, processing, analytics on demand to automatic provision and management of cloud resources to support the growing population of things.Hence, this special issue solicits paper related to topics including CoT architectures and models for smart provision of CoT applications, data management challenges facing CoT applications, software and tools to monitor, manage, deploy and deliver CoT applications, quality of service and related SLA management and policies for CoT applications, and security and privacy challenges facing CoT applications.The call for special issues received a number of submissions.After a two-phase peer review process, we have accepted 10 high-quality papers related to the aforementioned areas of interest.The first paper titled Using adaptive resource allocation to implement an elastic MapReduce framework by Jiaqi Zhao, Changlong Xue, Xinlin Tao, Shugong Zhang, and Jie Tao addresses the runtime resource demand challenge faced by application running on MapReduce frameworks.The proposed approach is capable of making the map reduce application, aware of overloading or under-loading situations with the resources allocated.They have extended the existing Hadoop MapReduce resource manager to implement the proposed strategy and validated the concept on an high-performance computing cluster with standard benchmark applications.Experimental results show a significant performance gain, for example, an up to 45% improvement in execution time for running multiple applications.The second paper titled A traffic hotline discovery method over cloud of things using big taxi GPS data by Xiaolong Xu, Wanchun Dou, Xuyun Zhang, Chunhua Hu, and Jinjun Chen addresses the challenge of discovering traffic hotline in CoT environments.Traffic hotlines are identified as the traffic lines with intensive traffic flows among traffic spots.They propose a hotline discovery method over CoT by establishing a hotline discovery principle.They have implemented their approach on SAP HANA cloud and tested it using big taxi global positioning system data under two application scenarios.

Rapid urbanization is a global megatrend with 66 percent of the world’s population expected to live in urban areas by 2050. The staggering exponential increase in urbanization is leading to more people migrating to major cities in the search of better opportunities and quality of life. Cities need to increase the efficiency in which they operate and use their resources sustainability in order to meet the demands imposed by rapid urbanization. The challenge is to continue providing basic resources such as sufficient fresh water; cleaner energy; transportation alternatives to commute efficiently from one place to another; adaption to changing climatic conditions; safety and security; while also ensuring economical, social, and environment sustainability. These challenges represent a huge opportunity for a paradigm shift that will require the need for data processing, analysis, and security close to the connected "things" i.e., towards the edge of the network in-order to support the growing smart city ecosystem. This paradigm shift will lead to an explosive growth of independent, owned and operated things and services including gateways, repeaters, smart infrastructure, and systems. Such a paradigm needs to be architected in a way that is easy to operate and dramatically simplifies the management of service offerings through scalable orchestration and proper automation. It must allow management, integration, and deployment of different tenants (such as services and things independently owned) within the smart city ecosystem in a uniform way. It should also have a suitable policy framework, letting specific stakeholders have access to data produced by other tenants, and analyze and extract values from the data. In order to address these challenges, this special issue solicits high quality original research papers (including smart city experience papers) that made significant contributions to the state-of-the-art in "method and techniques to build sustainable smart city solutions" research area. The call for papers received a number of submissions. After a two-phase peer review process, we have accepted five high-quality papers related to the aforementioned areas of interest which will be published in the October-December 2017 as Part 1. The papers in this issue are briefly summarized.

In this paper, we present Ubiquitous data Exploration (UTE), a mobile sensor data collection, annotation and exploration platform. Our platform facilitates rapid prototyping of data mining experiments by using a flexible and do-it-yourself approach. The platform allows researchers to quickly design and deploy applications on mobile devices in order to record sensor data and the corresponding ground-truth information. The platform is supported by a web interface for designing data collection experiments, synchronizing and storing the sensor data with the corresponding labels, and sharing data.

The dynamic mobility and limitations in computational power, battery resources, and memory availability are main bottlenecks in fully harnessing mobile devices as data mining platforms. Therefore, the mobile devices are augmented with cloud resources in mobile edge cloud computing (MECC) environments to seamlessly execute data mining tasks. The MECC infrastructures provide compute, network, and storage services in one-hop wireless distance from mobile devices to minimize the latency in communication as well as provide localized computations to reduce the burden on federated cloud systems. However, when and how to offload the computation is a hard problem. In this paper, we present an opportunistic computation offloading scheme to efficiently execute data mining tasks in MECC environments. The scheme provides the suitable execution mode after analyzing the amount of unprocessed data, privacy configurations, contextual information, and available on-board local resources (memory, CPU, and battery power). We develop a mobile application for online activity recognition and evaluate the proposed scheme using the event data stream of 5 million activities collected from 12 users for 15 days. The experiments show significant improvement in execution time and battery power consumption resulting in 98% data reduction.

Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

The ubiquity of mobile devices coupled with the advances in Internet of Things (IoT) technologies has led to the development of large-scale applications that can collect information about people and their environments in real-time. Such applications are referred to as Mobile Crowdsensing (MCS). In MCS, tasks are allocated to participants (mobile devices) by a remote server according to the application requirements. The key challenge is reducing the energy consumption of the participating mobile devices. One of the effective approaches to reduce energy consumption of MCS applications is to improve efficiency of task allocation. An efficient task allocation approach can optimize several aspects of MCS applications such as task coverage (minimum number of participants required for a MCS task), data quality, and sensing costs. In this paper, we propose a novel Context-Aware Task Allocation (CATA) approach that aims to allocate sensing tasks to the best participant set while improving energy efficiency in MCS applications. Another important feature of the proposed CATA approach is that it preserves the privacy of participants' by only disclosing the less sensitive data to the server. The proposed approach employs local and global task allocation methods to enable two levels of data sharing and privacy. We describe the series of experiments that were conducted to validate our proposed approach in terms of coverage and efficiency.

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project-OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. (www.github.com/OpenIotOrg). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described.

We are witnessing a new revolution in computing and communication involving symbiotic networks of people (social networks), intelligent devices, smart mobile computing, and communication devices that will form cyber-physical social systems. The emergence of intelligent devices with monitoring, sensing, and actuation capabilities referred to as Internet of Things and social networks have increased the popularity of novel social applications such as crowdsourcing and crowdsensing. The upsurge of such applications has fostered the need for scalable cost-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a scalable, energy-efficient, generic and extensible component-based distributed data analytics platform for mobile crowdsensing (MCS) applications. CARDAP incorporates on-the-move activity recognition and a number of energy efficient data delivery strategies using real-time mobile data stream mining. We propose and develop theoretical cost models for typical crowdsensing application scenarios. Experimental evaluations of CARDAP using a proof-of-concept MCS scenario validate the theoretical cost model estimates and demonstrate the platform's ability to deliver significant benefits in energy, resource, and query processing efficiency.

The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.

Mobile devices are rapidly becoming the primary computing device in people’s lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of

In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing.

Sensor networks represent an important component of distributed infrastructure supplying raw data to various applications from military to healthcare. A key challenge is cost-efficient collection of distributed data streaming from those sensor networks. In this paper we propose the use of mobile data collectors that employ K-NN queries as a cost-efficient approach to collect data within the sensor network. We investigate a 3D sensor network and propose a cost-efficient 3D-KNN algorithm that uses minimal energy and communication overheads to compute k-nearest neighbors. The 3D-KNN algorithm uses a 3 dimensional plane rotation algorithm that maps sensor nodes on a 3D plane to a reference plane identified by the mobile data collector We propose a cost-efficient KNN boundary estimation algorithm that computes KNN boundary based on network density. We also propose a neighbor prediction algorithm that uses distance, signal to noise ratio and mobile data collector' strajectory information to identify sensor nodes along the mobile data collector's path. We simulate the proposed 3D-KNN algorithm using GlomoSim and validate its cost efficiency by evaluating its energy efficiency and query latency. Lessons and results of extensive simulation conclude the paper.

UMS can be described as a system that allows for communication between users via a number of communication technologies over heterogeneous network infrastructures[1]. It integrates networking technologies such as Ethernet, ATM, IEEE 802.11, GSM and UMTS alongwith various devices such as laptops, tab