A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.

<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>

The Internet of Things (IoT) has recently received considerable interest due to the development of smart technologies in today’s interconnected world. With the rapid advancement in Internet technologies and the proliferation of IoT sensors, myriad systems and applications generate data of a massive volume, variety and velocity which traditional databases and systems are unable to manage effectively. Many organizations need to deal with these massive datasets that encounter different types of data (e.g., IoT streaming data, static data) in different formats (e.g., structured, semi-structured) coming from multiple sources. Several data integration mechanisms have been designed to process mostly static data. Unfortunately, these techniques are not able to deal with and integrate IoT streaming datasets from multiple sources. In this paper, we identify the challenges of IoT Streaming Data Integration (ISDI) and present a formal approach for the real-time integration of such IoT streaming datasets. We address one of the important issues of timing conflict/alignment among streaming data coming from multiple sources. A generic window-based ISDI approach is proposed to deal with IoT data in different formats and algorithms are developed to integrate IoT streaming data from multiple sources. In particular, we extend the basic windowing algorithm for real-time data integration and to deal with the timing alignment issue. We also introduce a de-duplication algorithm to deal with data redundancy and to demonstrate the useful fragments of the integrated data. We conduct several sets of experiments and quantify the performance of our proposed window-based approach. In particular, we compare our local experimental results with a real setup for streaming data, using Apache Spark. The results of the experiments, which are performed on several IoT datasets, show the efficiency of our proposed solution in terms of processing time. The results are also used to provide an integrated data view to the users.

<title>Abstract</title> The Internet of Things (IoT) services have seen rapid growth in recent years. However, existing research has yet to propose a robust method for quantitatively evaluating IoT services, nor has it adequately addressed the impact of cooperation and competition among IoT service providers on pricing strategies. This paper develops a duopolistic Cournot competition model involving data providers, service providers, and consumers. IoT service providers leverage machine learning to train models that generate user-centric IoT services, which they can then market and monetize for profit. We introduce a Logistic-curved service-quality function, with the amount of data used for machine learning as the primary metric for IoT service quality. The competitive behavior between IoT service providers in a Cournot duopoly market is examined, and we derive the optimal IoT service pricing and data procurement strategies that maximize profits for both parties. Next, we explore the possibility of bundling IoT services at discounted prices, categorizing four distinct scenarios based on the relationship between service quality and pricing. We calculate the pricing of IoT service bundles and determine the optimal data purchase quantities for both providers to maximize their profits. Finally, we incorporate a price-fluctuation factor to account for market uncertainty, refining the profit-function model to better reflect real-world market dynamics. The research can provide decision-making references for IoT service pricing, and the pricing mechanism based on machine learning can effectively mitigate the biases caused by price fluctuations.

The Internet of Things (IoT) has revolutionized data collection and analysis from diverse sources. IoT-based monitoring systems are now widespread in manufacturing, healthcare, and smart cities. These systems gather vast amounts of data from sensors and devices, enabling the detection of anomalies and patterns. The IoT has become an integral part of our lives, transforming various industries by enabling seamless connectivity between devices and increasing automation and efficiency. However, the reliability of IoT systems is often compromised due to the complexity and scale of these networks, making them vulnerable to failures and security breaches. To mitigate this problem, anomaly detection using efficient learning in IoT is a promising solution to identify unusual patterns that could indicate system faults or threats. This paper implements an efficient learning-driven approach for anomaly detection classification in IoT-based monitoring systems. The study aims to improve the accuracy and speed of identifying unusual patterns or behaviours in IoT data streams. By using advanced machine learning techniques, the proposed method can effectively detect anomalies in real-time, enhancing the overall security and reliability of IoT monitoring systems. The proposed framework enhances IoT system reliability by reducing downtime, improving security, and ensuring consistent performance of connected devices. Experimental results demonstrate the efficiency of machine learning techniques and their capabilities in anomaly detection. Empirical findings show that Decision Trees (DT) and Random Forests (RF) outperform other competitive models like Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Adaptive Boosting (AB) and Voting Classifier (VC) for network intrusion detection in the context of anomaly detection.

Pricing Models for the Internet of Things (IoT): Game Perspectives

Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

IoT data analytics in dynamic environments: From an automated machine learning perspective

SummaryRecently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required.In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.

The Internet of Things (IoT) enabled technologies to have proliferated due to their abilities to capture and exchange quality information. This empirical study aims to investigate the factors influencing the intention to use IoT services in healthcare by young physicians. An integrated model based on the theory of planned behaviour (TPB) and diffusion of innovations (DOI) theories are drawn upon and analyzed through a Structural Equation Modelling (SEM) with importance-performance map analysis (IPMA). Data were collected through a structured survey questionnaire. The results indicated that behavioural intention to use IoT in healthcare services vastly affected by the attitude and value co-creation. Attitude was found to be a principal determinant followed by the relative advantage towards behavioural intention to use IoT services. The novelty of this empirical study is that the proposed integrated model provides a meaningful application of the young physicians’ intention to use IoT in healthcare in developing countries.

In this work we discuss and propose a marketplace concept for the Internet of Things (IoT). We start from the vision that unlike the current IoT landscape where tech and consulting companies provide complete “solutions” for some vertical markets, in future we might witness an “uberization” of IoT, where every person or company will be able to offer or “consume” sensor data, actuator access and data analytics services to/from anybody else, leading to several marketplaces where these IoT services will be traded and exchanged. In this line, we assume that data and information about the places, things and persons will be the major asset in our future society, and that physical objects and cloud-based computing environments will be simply the resources that will allow greater interaction between the physical and digital world. In this paper, we present our initial analysis about the main principles and technologies enabling future trade of sensor data and actuation access, and how it might affect the economic organization of our society today.

Multi-source Internet of Things (IoT) data, archived in institutions’ repositories, are becoming more and more widely open-sourced to make them publicly accessed by scientists, developers, and decision makers via web services to promote researches on geohazards prevention. In this paper, we design and implement a big data-turbocharged system for effective IoT data management following the data lake architecture. We first propose a multi-threading parallel data ingestion method to ingest IoT data from institutions’ data repositories in parallel. Next, we design storage strategies for both ingested IoT data and processed IoT data to store them in a scalable, reliable storage environment. We also build a distributed cache layer to enable fast access to IoT data. Then, we provide users with a unified, SQL-based interactive environment to enable IoT data exploration by leveraging the processing ability of Apache Spark. In addition, we design a standard-based metadata model to describe ingested IoT data and thus support IoT dataset discovery. Finally, we implement a prototype system and conduct experiments on real IoT data repositories to evaluate the efficiency of the proposed system.

The Internet of Things (IoT) is becoming increasingly ubiquitous, acting as an important source of real-time data for various applications. By allowing data exchange between various parties along the IoT devices-Edge-Cloud computing continuum, the larger societal benefits of the IoT can be achieved. Assuring security and fostering confidence for IoT data sharing, however, is one of the biggest obstacles. Sharing real-time data originating from connected devices is crucial to real-world intelligent IoT applications, i.e., based on artificial intelligence/machine learning. Such IoT data sharing involves multiple parties for different purposes and is usually based on data contracts that might depend on the dynamic change of IoT data variety and velocity. We aim to support multiple parties (aka tenants) with dynamic contracts based on the data value for their specific contextual purposes. This work addresses these challenges by introducing a novel dynamic context-based policy enforcement framework to support IoT data sharing (on-Edge) based on dynamic contracts. Our enforcement framework allows IoT Data Hub owners to define extensible rules and metrics to govern the tenants accessing the shared data on the Edge based on policies defined with static and dynamic contexts. We have created an edge-centered architecture that enables multi-tenant use cases with tenant-specific application deployment and IoT-context-based data sharing on edge servers. Our proof-of-concept prototype for sharing sensitive data such as surveillance camera videos has illustrated our proposed framework. The experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time. We have addressed the need to enable multi-parties IoT (data) resources to be shared based on contracts, especially with dynamic IoT contexts, for tenant applications on the edge to allow their closer access to data.

Billions of devices in the Internet of Things (IoT) collect sensitive data about people, creating data privacy risks and breach vulnerabilities. Accordingly, data privacy preservation is vital for sustaining the proliferation of IoT services. In particular, privacy-preserving IoT connects devices embedded with sensors and maintains the data privacy of people. However, common misconceptions exist among IoT researchers, service providers, and users about privacy-preserving IoT. This article refutes five common misconceptions about privacy-preserving IoT concerning data sensing and innovation, regulations, and privacy safeguards. For example, IoT users have a common misconception that no data collection is permitted in data privacy regulations. On the other hand, IoT service providers often think data privacy impedes IoT sensing and innovation. Addressing these misconceptions is essential for making progress in privacy-preserving IoT. This article refutes such common misconceptions using real-world experiments and online survey research. First, the experiments indicate that data privacy should not be perceived as an impediment in IoT but as an opportunity to increase customer retention and trust. Second, privacy-preserving IoT is not exclusively a regulatory problem but also a functional necessity that must be incorporated in the early stages of any IoT design. Third, people do not trust services that lack sufficient privacy measures. Fourth, conventional data security principles do not guarantee data privacy protection, and data privacy can be exposed even if data is securely stored. Fifth, IoT decentralization does not attain absolute privacy preservation.

Providing Internet of Things (IoT) environments with service level guarantee is a challenging task for improving IoT application usage experience. We specify in this paper an IoT architecture enabling an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). In order to guarantee the IoT applications’ requirements, Quality of Service (QoS) mechanisms should be implemented within all the layers of the IoT architecture. Thus, we propose a specific mechanism for the lowest layer of our service level based IoT architecture (i.e., sensing layer). It is an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism enabling to take into consideration the requirements of real-time IoT services. Our access method called QBAIoT (QoS based Access for IoT) extends IEEE 802.15.4 systems by creating a new contention access period for each specified traffic class in the iSLA. Furthermore, due to the huge number of IoT connected devices, self-configuring capability provisioning is necessary for limiting human intervention and total cost of ownership (TCO). Thus, we integrate a self-configuring capability to the QBAIoT access method by implementing the MAPE-K closed control loop within the IoT High Level Gateway (HL-Gw) of our proposed QoS based IoT architecture.