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.

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.

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.

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.

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.

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.

Abstract Deep learning based novel intelligent neural network models are developed in this research study and employed for performing multi‐step wind speed and wind power forecasting for the data pertaining to certain wind farms. It has always been tedious to predict wind speed and wind power accurately due the existence of non‐linearity in the wind farm data and as well previous traditional and heuristic techniques has their own merits and demerits in performing the prediction process. This research study intends to handle the prevailing non‐linearity of the wind farm data and as well perform prediction of the parameters in a better manner with increased accuracy rate. The prediction study facilitates the renewable energy community to install the wind mills in the locations with higher accuracy rate and thereby power production gets increased extravagantly. The intelligent neural network developed in this article includes the ELMAN and spiking neuronal models with incorporated deep learning procedure and varying momentum factor criterion to achieve minimal error and better accuracy rate. Multi‐step forecasting is carried for 10‐min ahead and the numerical simulation executed with the proposed intelligent non‐linear forecasting techniques. The attained results confirm the superiority of the developed models over other techniques from previous works.

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.

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.

Battery swapping has attracted recent attention from transportation companies and government authorities alike as a solution to enable faster adoption of electric vehicles. While the attractiveness of battery swapping stems from its ability to alleviate the issues of cost, charging duration and operating range associated with electric vehicles, multiple challenges exist with regard to a large scale deployment of battery swapping. One of these is the optimal deployment of batteries and charging infrastructure across various swapping and charging stations. In this paper, we analyze the impact of battery distribution across swapping stations on the performance of their operations. We propose performance metrics to analyze the performance and show how mobility patterns impact performance of individual stations. Further, we show such performance can be improved through decentralized swapping decision making.

We study the problem of information dissemination in vehicular networks in this paper. Crucial information, such as traffic jamming status, is usually broadcasted inside the network. Each vehicle in the network has its own choice to either forward or drop packets for others. Although the energy consumption in general is not an issue in vehicular networks, forwarding every received packets is still not preferred as it may result in congestion in transmission. We model this problem via evolutionary game theory to investigate the cooperative behavior among vehicles. Simulation results show that, the cooperation ratio in the network is proportional to the setting of synergy factor for the evolutionary game. When nodes change their strategies according to the evolutionary game, information can be disseminated as fast as flooding scheme, while more bandwidth can be reserved.

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve wide network characteristics. Studies have shown that Small-World properties, primarily low average path length (APL) and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small-world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small-world properties show a degree of distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the APL and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using the Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on APL, clustering coefficient and connectivity.

We look at the problem of network wide broadcast using the multi-rate feature of a wireless ad hoc network. Existing research has primarily focused on achieving minimum latency by construction of minimum weight connected dominating sets (WCDS) based on neighbourhood information. In this paper, we are interested in stateless multi-rate broadcasting algorithms in which nodes determine their broadcasting behaviour based on neighbourhood transmissions. The primary contribution of this paper is that we show how broadcast effectiveness at different data rates are related and how this relationship can be used to optimize algorithm design. We propose three stateless broadcasting algorithms and demonstrate the performance improvements achievable. Our simulation results show that significant benefits can be obtained in terms of minimizing both the number of forwarding nodes as well as the broadcast latency.

Existing literature has looked to exploit wireless broadcast advantage (WBA) in order to optimize the performance of a wide variety of network operations. In this paper, we obtain a measure of WBA in a multihop scenario. We consider that all nodes in the network store and propagate implicitly received information from neighbourhood transmissions, resulting in the creation of a distributed cache, which we term broadcast cache. We obtain a lower bound on the growth of the broadcast cache in terms of the fewest set of transmissions in the network, which we define as the minimum set of non-altruistic transmissions. Subsequently, we use our results to obtain feasibility conditions that determine whether WBA can be effectively utilized depending on flow requirements.

Cooperative communication techniques have earlier been applied to design of the IEEE 802.11 medium access control (MAC) and shown to perform better. High rate stations can help relay packets from low-rate stations resulting in better throughput for the entire network. However, this also involves additional energy costs on the part of the relay which can result in reducing the network lifetime. We propose a cooperative MAC protocol NetCoop with the objective of maximizing the network lifetime and achieving high throughput. Based on this design, we also propose a flexible strategy which allows cooperation to be achieved using more than one relay. We show that this can achieve at least as good throughput as that of single relay cooperation while maintaining a high network lifetime.

Existing ad-hoc network routing strategies base their operations on flooding route requests throughout the network and choosing the shortest path thereafter. However, this typically results in a large number of unnecessary transmissions, which could be expensive for resource-constrained nodes such as those in a sensor network. In this paper, we propose a new mechanism HopAlert which optimizes route establishment and packet routing by limiting the number of nodes taking part in the route discovery process while achieving a low number of hops establishment. Using analysis and simulations, we show that this results in more routes with shorter hop counts than a reactive flooding protocol such as AODV while achieving higher savings.

The IEEE 802.11e standard has introduced specifications for service differentiation among different classes of data by specifying four service classes and a new contention resolution mechanism called EDCA. However, while the protocol shows a better performance for higher priority data such as voice and video, the performance is seen to drop drastically at high loads. In this paper, we explore the effectiveness of a multi-stage contention scheme for providing QoS differentiation among four different service classes, as specified by EDCA. From our analysis, we observe that the multi-stage with prioritization that we propose gives a much better performance than EDCA for higher priority data. Moreover, its good performance even at high network loads shows that this design is much more scalable.