This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, that is, when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs, a popular attack modelling method. However, traditional attack-graph approaches have been applied in static environments that do not continuously change, such as enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator’s control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. This article introduces an example scenario based on healthcare systems to motivate the work and illustrate the proposed approach. The proposed approach is implemented using a graph database management tool (GDBM), Neo4j, which is a popular tool for mapping, visualising, and querying the graphs of highly connected data. It is efficient in providing a rapid threat modelling mechanism, making it suitable for capturing security changes in the dynamic IoT environment. Our results show that our developed threat modelling approach copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths, whilst allowing for system dynamics. The developed dynamic topology and attack graphs can cope with the changes in the IoT environment efficiently and rapidly by maintaining their associated graphs.

The licensed cellular Internet of Things (IoT) market is expected to almost double from 2023 to 2030. As of 2023, the number of licensed cellular IoT connections was 3.5 billion, which is expected to reach six billion by 2030.

The revenue in the 'Healthcare IoT' segment of the internet of things market worldwide was modeled to be 83.81 billion U.S. dollars in 2024. Following a continuous upward trend, the revenue has risen by 48.02 billion U.S. dollars since 2018. Between 2024 and 2029, the revenue will rise by 50.62 billion U.S. dollars, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Healthcare IoT.

By 2025, forecasts suggest that there will be more than ** billion Internet of Things (IoT) connected devices in use. This would be a nearly threefold increase from the IoT installed base in 2019. What is the Internet of Things? The IoT refers to a network of devices that are connected to the internet and can “communicate” with each other. Such devices include daily tech gadgets such as the smartphones and the wearables, smart home devices such as smart meters, as well as industrial devices like smart machines. These smart connected devices are able to gather, share, and analyze information and create actions accordingly. By 2023, global spending on IoT will reach *** trillion U.S. dollars. How does Internet of Things work? IoT devices make use of sensors and processors to collect and analyze data acquired from their environments. The data collected from the sensors will be shared by being sent to a gateway or to other IoT devices. It will then be either sent to and analyzed in the cloud or analyzed locally. By 2025, the data volume created by IoT connections is projected to reach a massive total of **** zettabytes. Privacy and security concerns   Given the amount of data generated by IoT devices, it is no wonder that data privacy and security are among the major concerns with regard to IoT adoption. Once devices are connected to the Internet, they become vulnerable to possible security breaches in the form of hacking, phishing, etc. Frequent data leaks from social media raise earnest concerns about information security standards in today’s world; were the IoT to become the next new reality, serious efforts to create strict security stands need to be prioritized.

The graph shows the statistics of the output of the vulnerability assessment of IoT devices at European Organization for Nuclear Research(CERN).

The graph shows the number of articles published in the discipline of ^.

Application enablement resulted in being the layer with the highest share of revenue in terms of Internet of Things (IoT) solutions in 2023, with 40 percent of the total revenue, followed by professional services with about 30 percent.

Network of 46 papers and 74 citation links related to "A Comprehensive Review of Internet-of-Things (IoT) Botnet Detection Techniques".

The revenue in the 'Automotive IoT' segment of the internet of things market worldwide was modeled to be 251.91 billion U.S. dollars in 2024. Following a continuous upward trend, the revenue has risen by 138.8 billion U.S. dollars since 2018. Between 2024 and 2029, the revenue will rise by 119.7 billion U.S. dollars, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Automotive IoT.

Article abstractWe study the problem of ultrareliable and low-latency slicing in multiaccess edge computing (MEC) systems for the next-generation Internet of Things (IoT) and mobile applications operating in the space-Air-ground integrated network. The network has a dynamic topology formed by multiple nonstationary nodes with unstable communication links and unreliable processing/transmission resources. Each node can be in one of two hidden states: 1) reliable-in which the node generates no data errors and no losses and 2) unreliable-when the node can generate/propagate random data errors/losses. Solving this problem is difficult, as it represents the nondeterministic polynomial-Time (NP) hard nonconcave nonsmooth stochastic maximization problem which depends on the unknown hidden nodes' states and private information about local, dynamic parameters of each node, which is known only to this node, and not to other nodes. To address these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states. We then propose a novel algorithm based on the online alternating direction method of multipliers (ADMMs)-an extension of the well-known classical 'static' ADMM to dynamic settings, where our slicing problem can be solved distributedly, in real time, without revealing local (private) information of the nodes. We show that our algorithm converges to a global optimum of the slicing problem and has a good consistent performance even in highly dynamic, unreliable scenarios.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Change-Receivables Time Series for Leedarson IoT Technology Inc. Leedarson IoT Technology Inc. engages in the research and development, production, and sale of Internet of things (IoT) products, LED bulbs, fixtures, luminaires, light sources, and other products. The company provides sensors, controls, cameras, and smart appliances; smart lighting products; home security products, such as alarms, smoke alarms, water leakage alarms, video surveillance, and other products; energy management solutions; smart campus solutions, including air quality sensor, lighting products, smart curtain, scene control panel, wireless bridge and measurement connector, PIR sensor, temperature and humidity sensor, and air switch. It also offers smart kitchen appliances; hub/gateway, remote control, and range extender; and ZigBee, Bluetooth, Wi-Fi, Z-Wave, and combo modules. The company was formerly known as Xiamen Leedarson Lighting Group Co., Ltd. and changed its name to Leedarson IoT Technology Inc. in January 2020. The company was founded in 2000 and is based in Xiamen, China.

Network of 35 papers and 41 citation links related to "Internet of Things (IoT) – A Research Agenda for Information Systems".

Graph Database for Telecom Networks Market Outlook

According to our latest research, the global graph database for telecom networks market size is valued at USD 1.34 billion in 2024, reflecting a robust adoption rate across the telecom sector. The market is experiencing a strong upward trajectory with a CAGR of 22.7% from 2025 to 2033. By 2033, the market is projected to reach a substantial USD 10.15 billion, driven by the increasing complexity of telecom networks and the urgent need for advanced data management and analytics solutions. The primary growth factor is the surging demand for real-time network analytics and fraud detection capabilities, which are critical for telecom operators seeking operational efficiency and competitive advantage.

The rapid proliferation of connected devices, 5G rollouts, and the exponential growth of data traffic are fundamentally transforming the telecom industry landscape. Telecom networks are evolving into highly complex, dynamic ecosystems that generate vast amounts of interconnected data. Traditional relational databases are often inadequate for handling such intricate relationships and real-time analytics requirements. Graph database solutions are uniquely positioned to address these challenges by enabling telecom operators to model, analyze, and visualize complex network topologies, customer interactions, and transactional data with unparalleled speed and flexibility. This technological shift is a key growth driver, as telecom providers increasingly seek scalable, agile, and intelligent data management platforms to enhance customer experience, optimize network performance, and accelerate digital transformation initiatives.

Another significant growth factor for the graph database for telecom networks market is the escalating threat landscape, particularly in the domain of fraud detection and cybersecurity. Telecom operators are frequent targets of sophisticated fraud schemes, including SIM card cloning, subscription fraud, and network intrusion attempts. Graph databases excel at identifying hidden patterns, relationships, and anomalies within massive datasets, enabling telecom companies to detect and mitigate fraud in real time. The ability to perform advanced analytics on interconnected data sets is empowering telecom operators to proactively safeguard their networks, reduce financial losses, and comply with stringent regulatory requirements. As the complexity of cyber threats intensifies, the adoption of graph database solutions for security and fraud prevention is expected to surge, further fueling market growth.

The growing emphasis on customer-centricity and personalized service delivery is also propelling market expansion. Telecom operators are leveraging graph databases to gain a 360-degree view of customer journeys, preferences, and interactions across multiple touchpoints. This holistic understanding facilitates targeted marketing, churn prediction, and tailored service offerings, which are essential for customer retention and revenue growth in a highly competitive market. The convergence of telecom networks with emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) is amplifying the need for graph-based analytics, as these technologies rely on real-time, context-aware insights derived from complex data relationships. As a result, the integration of graph databases into telecom network architectures is becoming a strategic imperative for industry leaders.

From a regional perspective, North America currently leads the global graph database for telecom networks market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the early adoption of advanced analytics technologies, robust digital infrastructure, and the presence of major telecom and technology companies. Asia Pacific is emerging as the fastest-growing region, driven by massive investments in 5G networks, expanding mobile subscriber base, and increasing focus on digital transformation across telecom operators. Europe is also witnessing significant adoption of graph database solutions, particularly in the context of regulatory compliance and network optimization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by ongoing telecom sector modernization and rising demand for advanced data analytics. The global market outlook remains highly promising, with all regions poised to contribute to sustained growth over the forecast period.<b

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

Digital Twin Synchronization Graph Market Outlook

According to our latest research, the Digital Twin Synchronization Graph market size reached USD 2.3 billion in 2024, with a robust year-over-year growth driven by the increasing integration of digital twin technology across industrial and commercial sectors. The market is expected to expand at a CAGR of 27.8% from 2025 to 2033, reaching a projected value of USD 22.8 billion by 2033. This growth is primarily attributed to the rising adoption of Industry 4.0 practices, the proliferation of IoT devices, and the growing need for real-time data synchronization to optimize business processes and asset management across various industries.

The primary growth factor for the Digital Twin Synchronization Graph market is the accelerating pace of digital transformation initiatives across industries such as manufacturing, healthcare, and smart cities. Organizations are increasingly leveraging digital twins to create virtual replicas of physical assets, enabling them to monitor, simulate, and optimize operations in real time. The synchronization graph component plays a critical role in ensuring that data from multiple sources remains consistent, accurate, and up-to-date, which is essential for effective decision-making. The adoption of digital twins is further fueled by the need to reduce operational costs, improve product quality, and minimize downtime, all of which contribute to enhanced competitiveness in a rapidly evolving marketplace.

Another significant driver of market growth is the rapid advancement of enabling technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies facilitate the seamless integration and synchronization of vast amounts of data generated by sensors, devices, and systems. Digital twin synchronization graphs enable organizations to visualize complex relationships and dependencies within their operations, providing actionable insights that lead to predictive maintenance, optimized resource allocation, and improved asset lifecycle management. The increasing investment in smart infrastructure and the growing emphasis on sustainability and energy efficiency are further bolstering the demand for digital twin solutions, particularly in sectors like energy & utilities and smart cities.

Furthermore, the expanding ecosystem of cloud computing and edge computing solutions is catalyzing the adoption of digital twin synchronization graph platforms. Cloud-based deployments offer scalability, flexibility, and cost-effectiveness, making it easier for organizations of all sizes to implement and scale digital twin initiatives. The rise of edge computing allows for real-time data processing closer to the source, enhancing the responsiveness and reliability of digital twin systems. As organizations seek to harness the power of data-driven insights to drive innovation and operational excellence, the demand for advanced synchronization and visualization tools is expected to surge, positioning the digital twin synchronization graph market for sustained growth over the forecast period.

Regionally, North America currently leads the global market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technology innovators, established industrial players, and significant investment in research and development are key factors underpinning North America's dominance. Europe is experiencing rapid growth, driven by the widespread adoption of Industry 4.0 practices and government initiatives supporting digital transformation. Meanwhile, Asia Pacific is emerging as a high-growth region, with countries like China, Japan, and South Korea investing heavily in smart manufacturing, urban infrastructure, and digital health solutions. As these trends continue, regional dynamics are expected to play a pivotal role in shaping the future trajectory of the digital twin synchronization graph market.

Component Analysis

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Device Graph for Security Market Outlook

According to our latest research, the global Device Graph for Security market size reached USD 2.14 billion in 2024, demonstrating robust demand across diverse industries driven by the escalating sophistication of cyber threats and the proliferation of connected devices. With an impressive CAGR of 20.1% projected over the forecast period, the market is expected to climb to USD 11.27 billion by 2033. This remarkable growth trajectory is underpinned by the urgent need for advanced security frameworks, particularly as enterprises strive to secure increasingly complex device ecosystems and digital identities. The adoption of device graph technologies is being propelled by their ability to provide comprehensive visibility, contextual intelligence, and real-time risk mitigation across interconnected devices and platforms.

The primary growth factor for the Device Graph for Security market is the exponential increase in connected devices within enterprise and consumer environments. The Internet of Things (IoT) revolution has resulted in billions of devices coming online, each representing a potential entry point for cyber threats. Traditional security solutions are often ill-equipped to handle the dynamic and interconnected nature of these device networks. Device graph technology, leveraging advanced analytics and artificial intelligence, enables organizations to map, monitor, and secure device relationships in real time. This capability is critical for detecting anomalous behavior, preventing unauthorized access, and mitigating the risk of lateral movement by malicious actors. As organizations continue their digital transformation journeys, the demand for robust device-centric security solutions is expected to rise sharply.

Another significant driver is the increasing regulatory scrutiny and compliance requirements across industries such as BFSI, healthcare, and government. Regulatory frameworks like GDPR, HIPAA, and PCI DSS mandate stringent controls over data access, identity management, and threat detection. Device graph solutions empower organizations to achieve compliance by providing granular visibility into device interactions, access patterns, and user behaviors. These insights facilitate proactive risk management, audit readiness, and rapid incident response. Furthermore, the integration of device graph technology with existing security information and event management (SIEM) platforms enhances the overall security posture, enabling organizations to address evolving regulatory challenges while ensuring operational continuity.

Technological advancements in machine learning, big data analytics, and cloud computing are also catalyzing market growth. Device graph platforms are increasingly leveraging these technologies to scale their capabilities, deliver actionable intelligence, and support real-time decision-making. The shift towards cloud-based deployments is particularly noteworthy, as it enables organizations to harness elastic compute resources, reduce infrastructure costs, and accelerate time-to-value. Moreover, the growing adoption of zero-trust security models, which emphasize continuous verification of devices and users, aligns seamlessly with the core functionalities of device graph solutions. These factors collectively contribute to the sustained growth and innovation within the Device Graph for Security market.

From a regional perspective, North America currently dominates the Device Graph for Security market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of major technology vendors, early adoption of advanced security frameworks, and a high concentration of cyber-attacks are key factors driving market leadership in these regions. Meanwhile, Asia Pacific is emerging as a high-growth market due to rapid digitalization, expanding IoT ecosystems, and increasing investments in cybersecurity infrastructure. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by regulatory reforms and growing awareness of cyber risks. The regional dynamics are expected to evolve further as global enterprises prioritize device security and resilience in the face of escalating cyber threats.

Graph Database Platform Market Outlook

According to our latest research, the global graph database platform market size reached USD 2.5 billion in 2024, demonstrating robust demand across various sectors. The market is projected to expand at a CAGR of 22.7% from 2025 to 2033, reaching an estimated value of USD 19.1 billion by 2033. This impressive growth is primarily attributed to the increasing need for advanced data analytics, real-time intelligence, and the proliferation of connected data across enterprises worldwide.

A key factor propelling the growth of the graph database platform market is the surging adoption of big data analytics and artificial intelligence in business operations. As organizations manage ever-growing volumes of complex and connected data, traditional relational databases often fall short in terms of efficiency and scalability. Graph database platforms offer a more intuitive and efficient way to model, store, and query highly connected data, enabling faster insights and supporting sophisticated applications such as fraud detection, recommendation engines, and social network analysis. The need for real-time analytics and decision-making is driving enterprises to invest heavily in graph database technologies, further accelerating market expansion.

Another significant driver for the graph database platform market is the increasing incidence of cyber threats and fraudulent activities, especially within the BFSI and e-commerce sectors. Graph databases excel at uncovering hidden patterns, relationships, and anomalies within vast datasets, making them invaluable for fraud detection and risk management. Financial institutions are leveraging these platforms to identify suspicious transactions and prevent financial crimes, while retailers use them to optimize customer experience and personalize recommendations. The versatility of graph databases in supporting diverse use cases across multiple industry verticals is a major contributor to their rising adoption and market growth.

The rapid digital transformation of enterprises, coupled with the shift towards cloud-based solutions, is also fueling the graph database platform market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations to seamlessly integrate graph databases into their existing IT infrastructure. The growing prevalence of Internet of Things (IoT) devices and the emergence of Industry 4.0 have further increased the demand for platforms capable of handling complex, interconnected data. As businesses strive for agility and innovation, graph database platforms are becoming a strategic asset for gaining competitive advantage.

From a regional perspective, North America currently dominates the graph database platform market, driven by the presence of leading technology providers, early adoption of advanced analytics, and substantial investments in digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid economic development, expanding IT sectors, and increasing awareness of data-driven decision-making. Europe also holds a significant market share, supported by strong regulatory frameworks and widespread digital transformation initiatives. The market landscape is highly dynamic, with regional trends influenced by technological advancements, regulatory policies, and industry-specific demands.

Component Analysis

The graph database platform market is segmented by component into software and services. The software segment holds the largest share, as organizations increasingly deploy advanced graph database solutions to manage and analyze complex data relationships. These software platforms provide robust features such as data modeling, visualization, and high-performance querying, enabling users to derive actionable insights from connected data. Vendors are continuously enhancing their offerings with AI and machine learning capabilities, making graph database software indispensable for modern data-driven enterprises.
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Network of 45 papers and 62 citation links related to "A Quantum-Safe Software-Defined Deterministic Internet of Things (IoT) with Hardware-Enforced Cyber-Security for Critical Infrastructures".

Ebitda Time Series for Leedarson IoT Technology Inc. Leedarson IoT Technology Inc. engages in the research and development, production, and sale of Internet of things (IoT) products, LED bulbs, fixtures, luminaires, light sources, and other products. The company provides sensors, controls, cameras, and smart appliances; smart lighting products; home security products, such as alarms, smoke alarms, water leakage alarms, video surveillance, and other products; energy management solutions; smart campus solutions, including air quality sensor, lighting products, smart curtain, scene control panel, wireless bridge and measurement connector, PIR sensor, temperature and humidity sensor, and air switch. It also offers smart kitchen appliances; hub/gateway, remote control, and range extender; and ZigBee, Bluetooth, Wi-Fi, Z-Wave, and combo modules. The company was formerly known as Xiamen Leedarson Lighting Group Co., Ltd. and changed its name to Leedarson IoT Technology Inc. in January 2020. The company was founded in 2000 and is based in Xiamen, China.

Cogs-Excluding-Depreciation-and-Amortization Time Series for Leedarson IoT Technology Inc. Leedarson IoT Technology Inc. engages in the research and development, production, and sale of Internet of things (IoT) products, LED bulbs, fixtures, luminaires, light sources, and other products. The company provides sensors, controls, cameras, and smart appliances; smart lighting products; home security products, such as alarms, smoke alarms, water leakage alarms, video surveillance, and other products; energy management solutions; smart campus solutions, including air quality sensor, lighting products, smart curtain, scene control panel, wireless bridge and measurement connector, PIR sensor, temperature and humidity sensor, and air switch. It also offers smart kitchen appliances; hub/gateway, remote control, and range extender; and ZigBee, Bluetooth, Wi-Fi, Z-Wave, and combo modules. The company was formerly known as Xiamen Leedarson Lighting Group Co., Ltd. and changed its name to Leedarson IoT Technology Inc. in January 2020. The company was founded in 2000 and is based in Xiamen, China.