Enhancing Food Outbound Logistics With Event-Driven and Service-Oriented IoT Middleware (EDSOA-OLP-IoT)

The Internet of Things (IoT) provides a large amount of data, which can be shared or consumed by thousands of individuals and organizations around the world. These organizations can be connected using Service-Oriented Architectures (SOAs), which have emerged as an efficient solution for modular system implementation allowing easy communications among third-party applications; however, SOAs do not provide an efficient solution to consume IoT data for those systems requiring on-demand detection of significant or exceptional situations. In this regard, Complex Event Processing (CEP) technology continuously processes and correlates huge amounts of events to detect and respond to changing business processes. In this chapter, the authors propose the use of CEP to facilitate the demand-driven detection of relevant situations. This is achieved by aggregating simple events generated by an IoT platform in an event-driven SOA, which makes use of an enterprise service bus for the integration of IoT, CEP, and SOA. The authors illustrate this approach through the implementation of a case study. Results confirm that CEP provides a suitable solution for the case study problem statement.

The rise of the Internet of Things (IoT) has transformed manufacturing and supply chain management. This article examines how IoT integration in lean manufacturing significantly enhances real-time supply chain optimization. Lean manufacturing aims to reduce waste and increase efficiency, making it a perfect match for IoT technology, which connects devices and systems for real-time data collection and analysis. The paper explores various applications of IoT in lean manufacturing, such as predictive maintenance, real-time inventory management, quality control, energy efficiency, sensor technology, data analytics, and cloud computing, it further elucidates how manufacturers can achieve enhanced supply chain visibility, reduced waste, improved quality, and increased agility. Predictive maintenance uses IoT sensors to monitor equipment, allowing maintenance to be performed only when necessary, reducing downtime and extending equipment life. Real-time inventory management tracks stock levels and movements with IoT sensors and RFID tags, ensuring optimal inventory levels and reducing excess stock. IoT also enhances quality control by detecting defects in real-time and enabling immediate corrective actions. Energy efficiency is improved through IoT technology that monitors and optimizes energy usage, reducing costs and environmental impact. Additionally, IoT provides end-to-end visibility and transparency across the supply chain, facilitating better decision-making and coordination. The article includes detailed analysis and case studies demonstrating how IoT-driven innovations lead to improved operational efficiency, cost reduction, and better overall supply chain performance. It also addresses the challenges and ethical considerations of IoT implementation, such as data security, privacy concerns, and the potential impact on the workforce. Finally, the article offers insights into future trends and advancements in IoT technology, highlighting its potential to further revolutionize lean manufacturing and supply chain management. By embracing IoT, companies can not only streamline their operations but also gain a competitive edge in a rapidly evolving marketplace. The integration of IoT with lean principles allows for more adaptive and resilient supply chains, which are crucial in today’s dynamic business environment. As technology continues to advance, the opportunities for IoT to drive innovation and efficiency in manufacturing and supply chains will only grow, making it a key area of focus for future research and development.

Automated Test Systems (ATSs) can join the Internet of Things (IoT) at multiple layers and benefit both the overall IoT concept as well as advance the goals of ATSs. Logistics and sustainment are traditionally brought into the IoT conversation through asset tracking, transportation, and Health and Usage Monitoring Systems (HUMS). However the IoT has additional aspects that can be related to logistics information systems and automated test. In relation to Automated Test Systems, the IoT can be a valuable component while an ATS may also use IoT concepts and capabilities within its own architecture. ATSs are applicable in all layers of the IoT: sensing, networking / communications, and applications. An ATS can be a sensor in the IoT serving to inform sustaining engineering, product support management, maintenance planning, and supply chain decisions through its test and diagnostic data. In addition, many ATSs consist of a network of instrumentation and measurement devices that can serve as part of the IoT network / communication layer. And finally an automated test and diagnostic application can be used in the IoT to expand the scale of testing beyond standard automated test capability. This paper describes and analyzes the implementation of Automated Test Systems in all three IoT layers. The described ATS / IoT interactions elicit multiple benefits and challenges. The ubiquity and diversity of sensors in the Internet of Things allows for a new scope of testing across an enterprise, correlation of asset usage and repair, supply chain prognostics, and opportunities for deep learning. Challenges inevitably include cybersecurity and the analysis of overwhelming data. The paper also outlines how these concepts extend to related topics such as Cyber-Physical Systems (CPS) and the Internet of Everything (IoE). In many ways Automated Test Systems presage the Internet of Things and certainly earn a high degree of thingosity.

The Internet of Things (IoT) has emerged as one of the most transformative technologies of the 21st century, revolutionizing how industries operate and how devices interact within interconnected ecosystems. IoT enables billions of smart devices to collect, process, and share data, fostering unprecedented innovation across sectors like healthcare, manufacturing, smart cities, and transportation. However, the rapid expansion of IoT ecosystems has given rise to significant challenges in managing the vast volume, velocity, and variety of data generated by these devices. Traditional approaches to data management and processing often fall short, particularly in environments requiring real-time responsiveness, seamless scalability, and reliable decision-making.Integrating Artificial Intelligence (AI) with advanced data engineering techniques offers a powerful solution to these challenges. AI brings capabilities such as machine learning, predictive analytics, and intelligent decision-making, which, when combined with robust data engineering practices, enable efficient streaming data management. This integration supports real-time data processing, anomaly detection, predictive maintenance, and dynamic resource optimization, which are essential for creating intelligent IoT systems. By leveraging tools like real-time data pipelines, edge computing, and distributed architectures, AI-driven data engineering frameworks address critical issues, including data latency, resource constraints, and system scalability.This article delves into the intricate relationship between AI and data engineering within IoT ecosystems, focusing on streaming data management for smart devices. It explores the technical and theoretical underpinnings of integrating these fields, providing a comprehensive framework for optimizing IoT data streams. Key methodologies include employing machine learning algorithms to analyze real-time data, using edge computing to preprocess data closer to its source, and implementing scalable data pipelines for continuous processing.The findings of this study underscore the transformative potential of combining AI and data engineering in IoT ecosystems. Through experimental simulations and case studies, the research demonstrates how this integration enhances data flow efficiency, reduces latency, and improves the overall performance of IoT systems. For instance, in healthcare, AI-powered IoT devices enable real-time patient monitoring and predictive analytics, leading to improved medical outcomes. Similarly, in smart cities, integrated systems streamline traffic management, reduce energy consumption, and enhance public safety.This integration represents a paradigm shift in IoT ecosystems, laying the groundwork for intelligent, adaptive systems capable of meeting the demands of rapidly evolving industries. The study not only highlights the technological advancements enabled by this synergy but also identifies challenges such as integration complexity, resource limitations on edge devices, and the need for enhanced data privacy measures. Ultimately, this article serves as a blueprint for researchers, practitioners, and industry stakeholders aiming to unlock the full potential of IoT by bridging the gap between AI and data engineering.

It is our great pleasure to welcome you to the 7th ACM International Conference on Distributed Event-Based Systems (DEBS 2013) here at The University of Texas at Arlington, Arlington, Texas, USA. DEBS is the flagship conference for the dissemination of original research, demonstration of prototypes, the discussion of new practical insights, and the reporting of relevant experience relating to event-based computing. Event-based systems have gained in importance in many application domains, ranging from real-time data processing in web environments, nontraditional applications, such as railroad safety and track monitoring, logistics and networking, to complex event processing in finance and security. The event-based paradigm strengthened by continuous stream data processing has gathered momentum as witnessed by current efforts in areas such as event-driven architectures, big data systems, the internet of things, complex event processing, publish/subscribe systems, business process management, cloud computing, web services, information dissemination, and message-oriented middleware. The DEBS conference brings researchers, students, and practitioners from these various communities together in an international setting to exchange ideas and knowledge about current research work and open challenges. The conference also provides a forum for facilitating the exchange of ideas between academics, vendors, and application developers. The call for scientific papers attracted 58 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 16 papers that cover a variety of topics, including distributed stream processing, publish/subscribe systems, complex event processing models and languages, and mobility and query optimization. The technical program is complemented by three keynotes talks provided by Roger Barga (Microsoft Research), David Wollman (National Institute of Standards and Technology), and Shailendra Mishra (Paypal). Roger Barga addresses the need for batch-oriented analytics engines that are supported by storage and data processing engines such as Hadoop to also provide real-time analytics capabilities. David Wollman outlines the role of stream and event processing for the Smart Grid and other cyber-physical systems applications. Finally, Shailendra Mishra describes the challenges of complex event processing as a supporting technology for the data cloud and cloud services framework. In addition, Jennifer Maxwell (BNSF Railway) presents an invited experience report on the use of event-based technology for the development of an advanced railroad application. To place emphasis on the practical use of event-based technologies in distributed environments, the Grand Challenge competition provides a showcase of event-based solutions to problems that are relevant to industry at large using real-life data and queries. This year's challenge involved demonstrating the applicability of event-based systems for real-time analytics over high velocity sensor data collected from a soccer game. In addition to the Grand Challenge competition, demonstration and poster sessions provide an opportunity for groups of students, researchers, and practitioners to showcase their prototypes and research for an international audience. The Doctoral Workshop also acts as a meeting place for students to discuss their research and obtain meaningful feedback as well as interaction with experts in the field.

PERCEPTUS: Predictive complex event processing and reasoning for IoT-enabled supply chain

This research explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in supply chain management, emphasizing their role in enabling intelligent and autonomous business operations. It examines how these technologies enhance efficiency, decision-making, and adaptability in modern supply chains. A systematic review of existing literature, case studies, and industry reports is conducted to analyze the impact of AI and IoT in supply chain processes. The research highlights that AI-driven predictive analytics and IoT-enabled real-time monitoring significantly enhance supply chain visibility, efficiency, and responsiveness. Automation through AI-powered decision-making and IoT-based smart tracking systems reduces operational risks, minimizes costs, and optimizes resource allocation. However, challenges related to cybersecurity, data privacy, and integration complexities remain key concerns. This study contributes to both academic and industry discussions by offering insights into the evolving role of AI and IoT in supply chains. Practically, it provides guidance for businesses seeking to implement intelligent supply chain solutions. Socially, the findings emphasize the need for ethical AI deployment, data security, and workforce upskilling to navigate the shift toward autonomous operations. This research presents a unique synthesis of AI and IoT advancements in supply chain management, highlighting their synergistic impact on creating more adaptive, resilient, and intelligent operations. By addressing both opportunities and challenges, it serves as a valuable resource for researchers, practitioners, and policymakers in the field.

With the broader dissemination of digital technologies, visionary concepts like the Internet of Things also affect an increasing number of use cases with interfaces to humans, e.g. manufacturing environments with technical operators monitoring the processes. This leads to additional challenges, as besides the technical issues also human aspects have to be considered for a successful implementation of strategic initiatives like Industrie 4.0. From a technical perspective, complex event processing has proven itself in practice to be capable of integrating and analyzing huge amounts of heterogeneous data and establishing a basic level of situation awareness by detecting situations of interests. Whereas this reactive nature of complex event processing systems may be sufficient for machine-to-machine use cases, the new characteristic of application fields with humans remaining in the control loop leads to an increasing action distance and delayed reactions. Taking human aspects into consideration leads to new requirements, with transparency and comprehensibility of the processing of events being the most important ones. Improving the comprehensibility of complex event processing and extending its capabilities towards an effective support of human operators allows tackling technical and non-technical challenges at the same time. The main contribution of this thesis answers the question of how to evolve state-of-the-art complex event processing from its reactive nature towards a transparent and holistic situation management system. The goal is to improve the interaction among systems and humans in use cases with interfaces between both worlds. Realizing a holistic situation management requires three missing capabilities to be introduced by the contributions of this thesis: First, based on the achieved transparency, the retrospective analysis of situations is enabled by collecting information related to a situation's occurrence and development. Therefore, CEP engine-specific situation descriptions are transformed into a common model, allowing the automatic decomposition of the underlying patterns to derive partial patterns describing the intermediate states of processing. Second, by introducing the psychological model of situation awareness into complex event processing, human aspects of information processing are taken into consideration and introduced into the complex event processing paradigm. Based on this model, an extended situation life-cycle and transition method are derived. The introduced concepts and methods allow the implementation of the controlling function of situation management and enable the effective acquisition and maintenance of situation awareness for human operators to purposefully direct their attention towards upcoming situations. Finally, completing the set of capabilities for situation management, an approach is presented to support the generation and integration of prediction models for predictive situation management. Therefore, methods are introduced to automatically label and extract relevant data for the generation of prediction models and to enable the embedding of the resulting models for an automatic evaluation and execution. The contributions are introduced, applied and evaluated along a scenario from the manufacturing domain.

Supply chain automation represents an essential approach for attaining operational excellence while strengthening resilience and sustainability across global logistics. The paper integrates research from over 100 peer-reviewed studies and industry reports published through 2020 to explore how automation technologies are applied across supply chain functions along with their benefits and challenges. Automation includes multiple technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and the Internet of Things (IoT). The implementation of these technologies extends to warehouse management along with inventory control, demand forecasting, logistics optimization and additional essential sectors as identified by Atzori et al., 2017 and Choi et al., 2018 and Fosso Wamba et al., 2018. Studies on automated systems demonstrate enhancements in operations through productivity increases of 20% to 30% and substantial cost savings (Frank et al., 2019; Lacity & Willcocks, 2016). The implementation of AI-driven demand forecasting methods can cut forecast errors down by 50%, which leads to better inventory management and lower carrying costs (Tiwari et al., 2019). The integration of automated warehouse systems with autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) results in faster and more precise order fulfillment while cutting labor costs according to Zhong et al. (2017). Automation adoption faces obstacles such as significant upfront costs to implement systems and challenges integrating new solutions with existing legacy infrastructures while requiring specialized technical expertise (Strandhagen et al., 2017). Ethical issues surrounding AI decision-making in supply chains and job displacement potential continue to gain recognition as important concerns (Ghobakhloo, 2018). The research explores the role of automation in developing resilient supply chains which have gained significance after global disruptions like COVID-19 (Ivanov & Dolgui, 2020). Through their findings researchers, practitioners and policymakers can gain important information about supply chain automation's benefits and challenges which forms a basis for well- informed technology adoption strategies. Keywords Supply Chain Automation, Artificial Intelligence, Machine Learning, Robotic Process Automation, Internet of Things, Warehouse Automation, Demand Forecasting, Logistics Optimization, Supply Chain Resilience

Connecting food supply chains

The Internet of Things (IoT) was initially adopted to enhance efficiency and inform decision-making in supply chains (SC), but it is now increasingly utilised as a strategic tool to transform them. No prior study has systematically reviewed the past, present, and future of IoT in the SC context, especially amid the rise of advanced technologies like Generative Artificial Intelligence (GenAI). This study conducts a bibliometric analysis of 572 articles and a systematic literature review of 196 IoT studies published in the SC context between 2009 and 2025 (as of March). It applies descriptive, topic, and thematic analyses. This study develops the Theories, Context, Methods – Antecedents, Implementation, Outcomes – Emerging themes (TCM-AIO-E) framework. It tracks the ‘past’ and evolution of IoT applications on SC with several methods, like topic evolution analysis. The TCM-AIO-E framework guides scholars and practitioners with ‘present’ adoption and implementation mechanisms for transportation, warehouse, procurement, and resource management, focusing on decision-making, visibility, traceability, and agility. It reveals ‘future’ opportunities, significant barriers, and research gaps in the transformative role of IoT. The study proposes research questions to guide future research and help companies transition from IoT automation to autonomous and autodidact supply chains and production systems.

Impact of RFID technology on supply chain decisions with inventory inaccuracies

Complex event processing (CEP) is attracting much attention as a method for analyzing streaming data in the IoT environment. Since a CEP system selects and executes a rule from rules that match identified events, i.e., multiple rules are sequentially executed. This, however, causes a problem in the Internet of Things (IoT) environment since rule execution is slow and the remaining rules must wait to be executed. Simply executing rules in parallel may trigger interference between rules, and thus unexpected and undesirable results. This paper extends the traditional CEP system by developing an execution model for parallel rule firing in the IoT environment so as to be able to execute multiple rules in parallel without any interference. We start with an extended parallel firing condition by adding the definition of dependency between sensors and actuators to the condition. Next, we extend synchronization control of parallel firing so as to avoid the interference among rules that can occur when actions take a lot of time to execute. This paper reveals that the extended CEP system (i) realizes triple parallelism (i.e., data parallelism, task parallelism, and pipeline parallelism) in the IoT environment and (ii) avoids the case where rule execution triggers unexpected results.

Complex Event Processing (CEP) has become the key part of Internet of Things (IoT). Proactive CEP can predict future system states and execute some actions to avoid unwanted states which brings new hope to transportation IoT. In this paper, we propose a proactive CEP architecture and method for transportation IoT. Based on basic CEP technology, this method uses structure varying Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision model. Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well when used to control congestion in transportation IoT.

The global supply chain landscape is undergoing a transformative shift, driven by the rapid integration of mobility and cutting-edge digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), blockchain, cloud computing, and advanced data analytics. These technologies collectively form the backbone of what is now referred to as the digital supply chain. This transformation is particularly significant in the textile industry—a sector traditionally plagued by inefficiencies, demand unpredictability, and supply chain opacity. This research delves into how the adoption of digital and mobile technologies is redefining supply chain dynamics in the textile sector, with a specific focus on improving inbound and outbound logistics. The study investigates how these technologies contribute to enhanced visibility across the supply chain, improved responsiveness to market fluctuations, reduced operational costs, and alignment with sustainability goals. Digital mobility, in particular, enables supply chain actors to access real-time information, collaborate across functions, and make informed, data-driven decisions swiftly—an imperative in today's fast-paced, demand-sensitive marketplace. The methodology adopted is qualitative and exploratory in nature, relying on semi-structured interviews conducted with five senior supply chain managers from textile firms operating in India—a country representative of developing economies where digital transformation is often met with systemic resistance. Thematic analysis of the interview transcripts reveals a clear consensus: while digital mobility holds transformative potential, its implementation is hampered by infrastructural constraints, high upfront costs, organizational inertia, and a shortage of skilled digital professionals.