Evaluating dynamics, security, and performance metrics for smart manufacturing

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.

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.

The Internet of things (IoT) facilitated various aspects of people’s daily lives. In simple terms, the IoT refers to the connection of sensors, actuators, and devices to a network by which they can interact with each other and their users. It is a practical model that works on connecting a group of sensors and actuators that form a network that works to carry out several tasks, such as collecting information from the surrounding environment and processing it and providing the beneficiary of the network with this critical information is used in many cases in making important decisions and missions. Over time, the importance of IoT will increase due to its use in many fields. This technology is expected to become an essential milestone in developing many sectors and sciences. Therefore, to benefit from IoT technology, security in the IoT must focus on raising its efficiency. The security of IoT is a big challenge because of its complexity, heterogeneity, and a large number of related resources. It is necessary to improve security protocols to guarantee IoT connection and ensure all devices in this network are trusted and prevent an attacker from accessing any device in the network because these attacks penetrate privacy and expose the personal property to damage. This research introduces a new framework to improve the confidentiality, integrity, authenticity, and availability of IoT devices in indoor environments, by forcing new devices (IoT-MD), middleware (Security Centre SC), and protocols (add a new authorized device and IoT Traffic Control Protocol IoT-TCP). Security in IoT will be guaranteed by using this framework which enforces isolation between the IoT devices and networks to prevent unauthorized IoT devices. Moreover, prevent attacks in the IoT indoor environment.

Guaranteeing quality of service (QoS) to transmit real time applications which are delay sensitive is of utmost importance in Internet of Things (IoT) environment. This paper presents a dynamic Markov Chain based scheduling scheme for ensuring QoS for the delay sensitive traffic in IoT. The traffic is classified into prioritized and non- prioritized traffic. By estimating previous bandwidth allocated to prioritized traffic and the amount of decrease/increase in probability of average queue length of prioritized packets in consecutive cycles, the scheduler dynamically adjusts the percentage of link bandwidth allocated to it in each cycle. This helps in providing guaranteed services to real time traffic in IoT applications and simultaneously provide better throughput for non real time traffic as well. The proposed QoS- alert scheduling mechanism is able to optimize the desired performance in IoT network. For different experiments the simulation is performed which verifies and satisfies the blocking probability requirements of prioritized traffic for IoT data.

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

The research aims to develop and evaluate a model for Internet of Things (IoT) attack identification utilizing IoT data from the Global Cyber Alliance’s (GCA) Automated IoT Defense Ecosystem (AIDE). In the growing landscape of IoT security, the need for enhanced predictive solutions is vital. Our research leverages an enormous dataset, overall historical data from various IoT devices and network interactions, to develop a model to identify potential security threats. The key to our methodology concerns exploratory data analysis, which is focused on understanding complex patterns and anomalies in IoT data. This step is vital for feature engineering, where we meticulously select and transform data attributes to advance the model’s predictive strength. The data pre-processing stage further improves the dataset, ensuring the model training and testing on high-quality, relevant data. Model development is a composite process in this research. We tried out a few machine-learning algorithms, finally selecting the one that exhibited outstanding performance in preliminary tests. The chosen model endured strict training, with a basis on balancing accuracy and validity to effectively predict IoT attacks in various scenarios. The evaluation of our model is as durable as its development. We utilized a range of metrics, including accuracy, precision, recall, and F1 score, to evaluate the model’s behavior overall. The results show that our model not only attains high accuracy but also maintains a notable level of precision in predicting IoT attacks, which is crucial in minimizing false positives. In conclusion, our research contributes to the enhancement of IoT security by providing a very effective predictive model for IoT attack detection.

최근 정보통신 기술과 다양한 스마트 디바이스들의 성능이 빠르게 발전하고 실생활에 적용됨에 따라, 사물인터넷(Internet of things, IoT)의 중요성이 강조되고 있다. 본 논문에서는 IoT 서비스를 프로세스 기반으로 명세하고 이를 실행하기 위한 프로세스 기반 사물인터넷 객체 협업 모델을 제안한다. 즉, 본 논문의 목적은 프로세스 개념을 적용하여 IoT 서비스를 일련의 실행 순서를 가지는 태스크들과 이의 실행을 담당하는 IoT 객체간의 협업으로 정형적으로 명세하기 위한 방법을 제안하는 것이다. 결론적으로 제안 모델을 통해 IoT 서비스는 프로세스 기반 사물인터넷 컴퓨팅 환경에서 자동적으로 실행되며, 이에 대한 분석, 모니터링, 재사용이 가능할 것으로 기대된다. In recent years, the importance of IoT (Internet of Things) is emphasized by information communication technologies and the performance of various smart devices are rapidly developed and applied in the real world. In this paper, we propose a process-driven IoT-object collaboration model to specify and execute a IoT service based on processes. That is, the purpose of this paper is to suggest a formal method in order to describe a IoT service into a group of tasks having execution order and collaboration between IoT-objects in charge of the enactment of a task. Conclusively, through the proposed model, we expect that IoT services will be automatically executed, analyzed, monitored and reused in the process-driven IoT computing environment.

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS,IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms’ linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration.

The Internet of Things (IoT) is a emerging revolution technology that mankind has ever seen. IoT represents the future vision of computing and communication. IoT introduce the world of information and communication technology (ICT) for connectivity of anything at anytime from anywhere. Current Internet is considered as “Internet of People”, connecting the users of all over the world with various ways. IoT semantically means a network of interconnected things (Objects) with unique addressability and connected using a standard communication protocol technology. IoT can connect physical world to the virtual world. Data in IoT should be gathered by radio frequency identification (RFID), wireless sensor network (WSN) or electronic product code (EPC), etc. Recently scientists, academies and industries have put efforts to find a way for standard communication between physical object and real world, but there are many challenges in this area. This paper will represent the future of ubiquitous computing and showing the specific definition for standard architecture. Also we introduced two models architecture (3layer and 5layer architecture) in IoT. We will discuss the several challenges in IoT which recently researcher and architecture trying to solve these challenges. Finally we will be showing the Database scenario in IoT.

ABSTRACTContextSmart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home profile”. To comprehend the complete IoT ecosystem dynamics of an intelligent environment, it is necessary to learn from each IoT device to predict its status in the future time. Nevertheless, dealing with real‐world IoT data structure requires considerable preprocessing tasks and the employment of classifiers that can learn multiple IoT inputs from a single IoT message.ObjectiveAware of these challenges, this paper proposes a novel methodology to process multi‐label IoT data and provide a comprehensive comparison of multi‐label classifiers for forecasting the status of smart devices, considering their efficiency and accuracy.MethodWe propose a data transformation method to preprocess the IoT data to be used by multi‐label classifiers. This method is based on real data structure.ResultsWe evaluate our proposal in two real‐world scenarios and various multi‐label classifiers. The promising findings indicate that efficient classifiers can generate many correct predictions for a comprehensive IoT ecosystem in a small fraction of a second.ConclusionsOur proposed data transformation can fit the context of prediction to smart homes and work with multi‐label classifiers to understand user behavior.

PurposeThis study aims to explore the perceived impact of Internet of Things (IoT) adoption in Malaysian public sector organizations, focusing on key areas such as public service delivery, operational efficiency and governance, among others. The research highlights how IoT drives broader organizational changes, contributing to digital transformation and supporting Malaysia’s socio-economic development goals.Design/methodology/approachThis study adopts a qualitative research design, employing semi-structured interviews with 17 key participants from various ministries, central agencies, state governments and local city councils in Malaysia. The participants were selected using purposive sampling to ensure diverse representation from different stages of IoT adoption. Thematic analysis was conducted to identify key themes representing the impacts of IoT adoption in the public sector. The analysis followed Braun and Clarke’s six-phase framework, with the support of Atlas.ti software to systematically code, categorize and interpret the data. The findings provide comprehensive insights into the perceived impacts of IoT on governance, operational efficiency, service delivery and organizational culture within Malaysia’s public sector.FindingsThe study reveals IoT’s transformative impact on Malaysian public sector organizations, with key themes emerging in operational excellence, service delivery transformation and data-driven governance. Specific findings include improved resource allocation through real-time data analytics, increased transparency in decision-making processes and enhanced public health monitoring systems. Additional themes, such as advancements in public health, environmental resilience and citizen engagement, further highlight IoT’s role in driving public sector innovation and enhancing Malaysia’s socio-economic development goals.Originality/valueThis study contributes original insights into the nascent field of IoT adoption in the public sector, with a specific focus on the Malaysian context. By identifying 21 key themes through qualitative data, categorized into 4 impact areas, it offers a detailed examination of how IoT affects public service delivery, operational efficiency and governance, providing new perspectives on digital transformation in government.

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

Internet of Things (IoT) system provides personalized services to users by analyzing the amount of data collected from billions of sensors. It is important to ensure the trustworthiness of the heterogeneous IoT data. To address this requirement, the data provenance needs to be used in the IoT system. In this article, we integrate the PROV-DM model into the IoT architecture and propose an IoT data provenance model. To avoid corrupting and forging the provenance information, we record the provenance data onto the blockchain. In addition, we use the IoT secure and computable data sharing system to illustrate that the proposed model can construct a provenance graph to detect which agent operates the IoT data abnormally. Finally, we implement and evaluate the proposed model on a public IoT database.