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.

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.

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.

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.

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.

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.

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

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 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.

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.