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The application of Internet of Things (IoT) within industrial environments is fostering the adoption of the digital twin (DT) approach, applied at the edge of the network to handle heterogeneity stemming from siloed application management solutions and from protocols originated by different manufacturing tools and enterprise services. In this challenging context, network heterogeneity also represents a critical element that can significantly limit the design and deployment of DT-oriented applications. This article proposes the Application-driven digital twin networking middleware with the twofold objective of: 1) Simplifying the interaction among heterogeneous devices by allowing DTs to exploit IP-based protocols instead of specialized industrial ones and to enhance packet content expressiveness, by enriching data via well-defined standards. 2) Dynamically managing network resources in edge industrial environments, applying software defined networking to exploit the communication mechanisms most suitable to application requirements, ranging from native IP to more articulated based on packet content.

In this paper, end devices are considered here as agent, which makes its decisions on whether the network will offload the computation tasks to the edge devices or not. To tackle the resource allocation and task offloading, paper formulated the computation resource allocation problems as a sum cost delay of this framework. An optimal binary computational offloading decision is proposed and then reinforcement learning is introduced to solve the problem. Simulation results demonstrate the effectiveness of this reinforcement learning based scheme to minimize the offloading cost derived as computation cost and delay cost in industrial internet of things scenarios.

In the industrial Internet of things (IIoT), there is always a strong demand for real-time information transfer. Especially when deploying wireless/wired hybrid networks in smart factories, the requirement for low delay interaction is more prominent. Although tree routing protocols have been successfully executed in simple networks, more challenges in transmission speed can be observed in the manufacturing broadband communication system. Motivated by the progresses in deep learning, a smart collaborative routing protocol with low delay and high reliability is proposed to accommodate mixed link scenarios. First, we establish a one-hop delay model to investigate the potential affects of Media Access Control (MAC) layer parameters, which supports the subsequent design. Second, forwarding, maintenance, and efficiency strategies are created to construct the basic functionalities for our routing protocol. Relevant procedures and key approaches are highlighted as well. Third, two sub-protocols are generated and the corresponding implementation steps are described. The experimental results demonstrate that the end-to-end delay can be effectively cut down through comprehensive improvements. Even more sensor nodes and larger network scale are involved, our proposed protocol can still illustrate the advantages comparing with existing solutions within IIoT.

The Industrial Internet of Things (IIoT) can transform an existing isolated industrial system to a connected network. The IIoT and the related wireless connectivity requirements for industrial sensors are very significant. The deployed sensors in IIoT monitors the conditions of the industrial devices and machines. Therefore, reliability and security become the most important concerns in IIoT. This introduces many familiar and ever-increasing risks associated with the industrial system. The IIoT devices can be vulnerable to vast array of viruses, threats, and attacks. Therefore, an efficient protection strategy is required to ensure that the millions of IIoT devices are safe from these risks. However, resource constraint IIoT devices have not been designed to have effective security features. Due to this, in recent years, cloud, fog, and edge-based IIoT has received great attention in the research community. The computationally intensive tasks such as security, data analytics, decision making, and reporting are performed at the cloud or fog using a powerful computing infrastructure. The data security of the IIoT device has been provided by employing improved Rivest-Shamir-Adelman (RSA) and hash signatures. The proposed RSA algorithm has a four-prime number of 512- bits. The device authentication is performed by employing a hash signature. For long network life, an efficient clustering technique for the sensor devices which is based on node degree(N), distance from the cluster(D), residual energy(R), and fitness (NDRF) has been proposed. The fitness of the sensor nodes is computed using the Salp swarm algorithm (SSA). In order to reduce the latency and communication overhead for IIoT devices a resource scheduling using SoftMax deep neural network (DNN) is proposed. All the requests coming from the cluster head are classified using SoftMax-DNN for best resource scheduling on the basis of storage, computing, and bandwidth requirements. The proposed framework produces superior results, especially in terms of energy consumption, latency, and strength of security.

Industry 4.0 encompasses a promise of a new industrial revolution in terms of providing secure, intelligent, autonomous and self-adaptive industrial IoT (IIoT) networks. Key industrial applications and systems will be significantly more complex due to the involvement of the vast number of different devices and diverse nature of various stakeholders and service providers. These complex industrial processes, services and applications also have strict requirements in terms of performance — latency in particular — and resource-efficiency, together with high standards for security and trust. In this context, Blockchain and Edge Computing emerge as prominent technologies to address the mentioned essential requirements and to further strengthen the rise of the new era of digitization. The Edge computing paradigm ensures low latency services for IIoT applications while optimizing the network usage, whereas Blockchain provides a decentralized way for ensuring data integrity, trust and security. In this paper, we propose a “BlockEdge” framework that combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. We verify the feasibility of our approach by evaluating the performance and resource-efficiency of BlockEdge in terms of latency, power consumption and network usage, through simulations against non-Blockchain solution.

The fourth industrial revolution, or Industry 4.0 (I4.0), makes use of wireless technologies together with other industrial Internet-of-Things (IIoT) technologies, cyber–physical systems (CPS), and edge computing to enable the optimization and the faster re-configuration of industrial production processes. As I4.0 deployments are ramping up, the practical integration of 5G wireless systems with existing industrial applications is being explored in both Industry and Academia, in order to find optimized strategies and to develop guidelines oriented towards ensuring the success of the industrial wireless digitalization process. This paper explores the challenges arisen from such integration between industrial systems and 5G wireless, and presents a framework applicable to achieve a structured and successful integration. The paper aims at describing the different aspects of the framework such as the application operational flow and its associated tools, developed based on analytical and experimental applied research methodologies. The applicability of the framework is illustrated by addressing the integration of 5G technology into a specific industrial use case: the control of autonomous mobile robots. The results indicate that 5G technology can be used for reliable fleet management control of autonomous mobile robots in industrial scenarios, and that 5G can support the migration of the on-board path planning intelligence to the edge-cloud.

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. Ultra-Reliable and Low Latency Communications (URLLC) is the 5G service category used by critical mechanisms, with a millisecond end-to-end delay and reduced probability of failure. 5G defines new numerologies, together with mini-slots for a faster scheduling. The main challenge of this is to select the appropriate numerology according to radio conditions. This fact is very important in industrial scenarios, where the fundamental problems are interference and multipath propagation, due to the presence of concrete walls and large metallic machinery and structures. Therefore, this paper is focused on analyzing the impact of the numerology selection on the delay experienced at radio link level for a remote-control service. The study, which has been carried out in a simulated cellular factory environment, has been performed for different packet sizes and channel conditions, focusing on outliers. Evaluation results show that not always a higher numerology, with a shorter slot duration, is appropriate for this type of service, particularly under Non-Line-of-Sight (NLOS) conditions.

The potential of blockchain has been extensively discussed in the literature and media mainly in finance and payment industry. One relatively recent trend is at the enterprise-level, where blockchain serves as the infrastructure for internet security and immutability. Emerging application domains include Industry 4.0 and Industrial Internet of Things (IIoT). Therefore, in this paper, we comprehensively review existing blockchain applications in Industry 4.0 and IIoT settings. Specifically, we present the current research trends in each of the related industrial sectors, as well as successful commercial implementations of blockchain in these relevant sectors. We also discuss industry-specific challenges for the implementation of blockchain in each sector. Further, we present currently open issues in the adoption of the blockchain technology in Industry 4.0 and discuss newer application areas. We hope that our findings pave the way for empowering and facilitating research in this domain, and assist decision-makers in their blockchain adoption and investment in Industry 4.0 and IIoT space.

The 5G System is being developed and enhanced to provide unparalleled connectivity to connect everyone and everything, everywhere. The first version of the 5G System, based on the Release 15 (“Rel-15”) version of the specifications developed by 3GPP, comprising the 5G Core (5GC) and 5G New Radio (NR) with 5G User Equipment (UE), is currently being deployed commercially throughout the world both at sub-6 GHz and at mmWave frequencies. Concurrently, the second phase of 5G is being standardized by 3GPP in the Release 16 (“Rel-16”) version of the specifications which will be completed by March 2020. While the main focus of Rel-15 was on enhanced mobile broadband services, the focus of Rel-16 is on new features for URLLC (Ultra-Reliable Low Latency Communication) and Industrial IoT, including Time Sensitive Communication (TSC), enhanced Location Services, and support for Non-Public Networks (NPNs). In addition, some crucial new features, such as NR on unlicensed bands (NR-U), Integrated Access & Backhaul (IAB) and NR Vehicle-to-X (V2X), are also being introduced as part of Rel-16, as well as enhancements for massive MIMO, wireless and wireline convergence, the Service Based Architecture (SBA) and Network Slicing. Finally, the number of use cases, types of connectivity and users, and applications running on top of 5G networks, are all expected to increase dramatically, thus motivating additional security features to counter security threats which are expected to increase in number, scale and variety. In this paper, we discuss the Rel-16 features and provide an outlook towards Rel-17 and beyond, covering both new features and enhancements of existing features. 5G Evolution will focus on three main areas: enhancements to features introduced in Rel-15 and Rel-16, features that are needed for operational enhancements, and new features to further expand the applicability of the 5G System to new markets and use cases.

Industry 4.0 is generalizing the use of wireless connectivity in manufacturing and logistics. Specifically, in Smart Logistics, novel Industry 4.0 technologies are used to enable agile supply chains, with reduced management, energy and storage costs. Cellular networks allow connectivity throughout all the scenarios where logistics processes take place, each having their own challenges. This paper explores such scenarios and challenges, and proposes 5G technology as a global unified connectivity solution. Moreover, this paper proposes a system for exploiting the application-specific optimization capabilities of 5G networks to better cater for the needs of Smart Logistics. An application traffic modeling process is proposed, along with a proactive approach to network optimization that can improve the Quality of Service and reduce connectivity costs.

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.

Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption., This is the version accepted at Sensors 2021

International audience; The Internet of Things (IoT) has become ubiquitous due to its flexibility, ease-of-use, and reduced cost. As a consequence, the industry is adopting these systems in its transformation into Industry 4.0. However, the strict Quality of Service (QoS) requirements of the industry are not met with the default best-effort provisions of the IoT. Most industrial applications require strict guarantees in terms of end-to-end network reliability and latency. For instance, consecutive packet losses can lead to communication disruptions in supply chain systems. Therefore, adaptations are being made to fulfill these requirements with the IEEE Std 802.15.4-2015 Time slotted Channel Hopping (TSCH) link-layer standard and the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) standard at the Internet Engineering Task Force (IETF). However, even by employing such industrial protocols, it is still difficult to achieve the expected QoS levels. Considering that RPL constructs and maintains a single-path from a source to a destination if there are potential issues on this path (e.g., queue overflow, variable wireless link quality) packets may suffer unexpected delays and even drops. If we consider a multi-path implementation where each node can replicate a packet into several paths, the transmission reliability improves since each packet copy is used to forward the packet information. However, uncontrolled replication can lead to network flooding, resulting in excessive power consumption. In this article, we present On-Demand Selection (ODeSe), a novel multi-path routing algorithm, which improves our previous work, the Common Ancestor (CA) algorithms, by selecting the most suitable upward forwarders. Moreover, we use the Packet Automatic Repeat reQuest, Replication and Elimination, and Overhearing (PAREO) functions in order to improve network reliability and availability. In order to control the number of relay nodes during a transmission, ODeSe forces each node of the same relay level to select the same Preferred Parent (PP) and the same Alternative Parent (AP). Thus, we address the trade-off between the total number of traversed nodes in the network and high reliability. Using the Cooja network simulator running the Contiki OS, we compare ODeSe against single-path RPL and multi-path RPL with different alternative parent selection algorithms. The results demonstrate that ODeSe outperforms single-path RPL in terms of reliability, and multi-path RPL in terms of energy consumption while maintaining a 99.14% packet delivery ratio.

Thanks to its inherent capabilities (such as fairly long radio coverage with extremely low power consumption), long-range wide area network (LoRaWAN) can support a wide spectrum of low-rate use-cases in Industry 4.0. In this article, both plain and energy harvesting (EH) industrial environments are considered to study the performance of LoRa radios for industrial automation. In the first instance, a model is presented to investigate LoRaWAN in Industry 4.0 in terms of battery life, battery replacement cost, and damage penalty. Then, the EH potential, available within an Industry 4.0, is highlighted to demonstrate the impact of harvested energy on the battery life and sensing interval of LoRa motes deployed across a production facility. The key outcome of these investigations is the cost trade-off analysis between battery replacement and damage penalty along different sensing intervals which demonstrates a linear increase in aggregate cost up to £1500 in case of 5 min sensing interval in the plain (nonenergy harvesting) industrial environment while it tends to decrease after a certain interval up to five times lower in EH scenarios. In addition, the carbon emissions due to the presence of LoRa motes and the annual $\text{CO}_{2}$ emission savings per node have been recorded up to 3 kg/kWh when fed through renewable energy sources. The analysis presented herein could be of great significance toward a green industry with cost and energy efficiency optimization.

International audience; This paper describes our work on a flexible centralized controller for scheduling wireless networks. The context of this work encompasses wireless networks within the wider Internet of Things (IoT) field and in particular addresses the requirements and limitations within the narrower Industrial Internet of Things (IIoT) sub-field. The overall aim of this work is to produce wireless networking solutions for industrial applications. The challenges include providing high reliability and low latency guarantees, comparable to existing wired solutions, within a noisy wireless medium and using generally computationally-and energy-restrained network nodes. We describe the development of a centralized controller for Wireless Industrial Networks, currently aimed at IEEE Std 802.15.4-2015 Time Slotted Channel Hopping protocol. Our controller takes a high-level network-centric problem description as input, translates it to a low-level representation and uses that to retrieve a solution from a Satisfiability Modulo Theories (SMT) solver, translating the solution back to a higher-level network-centric representation. The advantages of our solution are the ability to gain the added flexibility, higher ease of deployment, and lower deployment cost offered by wireless networks by generating configurable and flexible schedules for these applications.

The proposed work describes a dynamic regression based event-tracker for high speed production process. The methodology discussed is a causal system and provides trends and estimations of the sensors based on a flexible regression model of the historical sensor values. A safety threshold is defined that provides a boundary of the tolerant working for the regime condition of production. This threshold is used as a reference to calculate the remaining useful life of the critical component. The estimated remaining useful life is compared with the Weibull reliability analysis. The proposed methodology provides a remaining useful life of ∼ 10 weeks for the thermal regulator use-case when compared to ∼ 9 weeks for Weibull analysis. The overestimation of the methodology is discussed and along with the alternative methodology. The sensitivity analysis is conducted on the noise and training periods are studied for better prediction. European Union’s Horizon 2020

International audience; The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto routing protocol for Low Power and Lossy Networks (LLNs). It is a proactive and link-layer agnostic routing protocol standardized as RFC 6550 by the Internet Engineering Task Force (IETF). Based on the distance-vector technique, RPL builds a Destination Oriented Directed Acyclic Graph (DODAG) topology. To establish and maintain the routes, RPL uses DODAG Information Object (DIO) control packets, that are transmitted in broadcast, for RPL nodes to propagate the DODAG related information, while its transmission frequency depends on Trickle timer algorithm, i.e., the less stable the network the more DIOs are transmitted. Thus, when a new node intends to join the RPL DODAG, it listens for a DIO message from nearby nodes, which may take a very long time if the network is in a stable state. Therefore, RFC 6550 is equipped with the DODAG Informational Solicitation (DIS) message to solicit DIOs from nearby RPL nodes, similar to the Router Solicitation in IPv6 Neighbor Discovery. However, the solicitation procedure is not the most efficient one, since it resets the Trickle timers in the nodes that receive the DIS message and, thus, they transmit an unnecessarily large number of DIOs that congest the network and consume energy in the nodes. In this paper, we propose to augment RFC 6550, the RPL routing protocol, with additional DIS flags and options that allow a RPL node to better control how the nearby RPL nodes will respond to its solicitation for DIOs. Our performance evaluation in Contiki-NG & COOJA demonstrates that we can reduce the control packets in the network by up to 45.5%.

The reliable acquisition of monitoring information is critical for several industrial use cases relying on wireless sensor network deployments. However, missing sensor measurements are typical in industrial systems empowered by cellular connectivity due to the stochastic nature of the wireless channel. In this paper, we propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements. Based on an analytical framework for the network model and a closed-form expression for the outage probability, the impact on the reconstruction error performance is thoroughly explored. Considering a dataset with high spatiotemporal correlation in the sensor observations, our proposed scheme is shown to outperform two baseline data recovery methods in terms of reconstruction error for various network configurations. In addition, despite the presence of imperfect cellular connectivity, our proposed scheme exhibits high event-detection accuracy.

International audience; IEEE Std 802.15.4-2015 Time Slotted Channel Hopping (TSCH) is the de facto Medium Access Control (MAC) mechanism for industrial applications. It renders communications more resilient to interference by spreading them over the time (time slotted) and the frequency (channel hopping) domains. The 6TiSCH architecture bases itself on this new MAC layer to enable high reliability communication in Wireless Sensor Networks (WSNs). In particular, it manages the construction of a distributed communication schedule that continuously adapts to changes in the network. In this paper, we first provide a thorough description of the 6TiSCH architecture, the 6TiSCH Operation Sublayer (6top), and the Minimal Scheduling Function (MSF). We then study its behavior and reactivity from low to high traffic rates by employing the pythonbased 6TiSCH simulator. Our performance evaluation results demonstrate that the convergence pattern of MSF is the root cause of the majority of packet losses observed in the network. We also show that MSF is subject to over-provisioning of the network resources, especially in the case of varying traffic load.

International audience; With the growth of the Internet of Things appliances in industrial environments, known as Industry 4.0, wireless multi-hop network solutions have been attracting more attention in the past years. The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto protocol for Low-power and Lossy Networks specially designed for industrial use-cases. However, the default operation of RPL does not provide a high level of network reliability and low jitter performance. The use of IEEE Std 802.15.4-2015 Time Slot Channel Hopping (TSCH) at the Medium Access Control layer can mitigate the effects of external interference by re-transmitting over different radio frequency. Still, this standard does not support possible link failures or node over-the-air programming. In this paper, we propose the use of multiple disjoint paths to enforce reliability and availability when both RPL and TSCH standards are used. Indeed, we propose and compare two approaches based on the principle of replication: i) packet replication at the source node only, and ii) packet replication at the source node and scattering if two replicas merge. We implemented these two algorithms in Contiki OS and evaluated their trade-offs over the simulated network environment provided by COOJA. Finally, we compare these solutions against the state-of-the-art Packet Automatic Repeat reQuest (ARQ), Replication and Elimination (RE), and Overhearing (PAREO) technique that proposes a braided multipath pattern.

In this paper, a novel paradigm of adapting wireless communications based on the factory schedules in an indoor factory floor is explored. Since the fourth industrial revolution (4IR) strengthens the industries with a wide range of advanced applications such as digital twin and predictive maintenance, a large number of devices and systems are deployed densely near machines in a factory floor. Such new devices and systems, along with the existing systems, are networked by the Industrial Internet of Things (IIoT) wirelessly for their functioning. Devices closer to machines have a greater probability of interference from the operation of the machine. Since the operation of machines in a factory are often scheduled, their impact on wireless communication can be learnt and predicted. The first step towards it is, to estimate the effect of various processes in a factory schedule on wireless communication. This research empirically investigates the extent and nature of the impact of factory schedules and presents its findings. The obtained results indicate that the machines influence the packet reception rate (16% on an average), positively for certain nodes while negatively for others. Furthermore, based on these findings, this paper categorizes the factory schedules into two broad categories (‘Macro’ and ‘Micro’ processes) and discusses the challenges in developing a mechanism to detect and classify them. The interference detection and identification can have applications such as scheduling delay-tolerant traffic and adjusting power levels while transmitting a packet apart from increasing reliability of IIoT.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.

Modern sensor networks work on the basis of intelligent sensors and actuators, their connection is carried out using conventional or specifically dedicated networks. The efficiency and smooth transmission of such a network is of great importance for the accuracy of measurements, sensor energy savings, or transmission speed. Ethernet in many networks is typically based on the TCP/IP protocol suite. Regardless of whether or not the network transmission is wired or wireless, it should always be reliable. TCP ensures transmission reliability through retransmissions, congestion control and flow control. But TPC is different in networks based on the UDP protocol. The most important here is the transmission speed achieved by shortening the header or the lack of an acknowledgment mechanism. Assuming the network is an automatic control system, it has interconnected elements that interact with each other to perform some specific tasks such as speed control, reliability and security of transmission, just the attributes that define stability being one of the fundamental features of control systems. Such a system returns to equilibrium after being unbalanced. There are many definitions of stability, e.g. Laplace or Lupanov. To check the stability of the sensor network connected to the Internet, different stability criteria should be used. We are going to analyze the stability of a computer network as a dynamic linear system, described by the equations known in the literature. In this paper, we propose the method of testing stability for positive systems using the Metzlner matrix in sensor networks such as IoT or IIoT. We will carry out tests in a place where wide area networks connect to sensor networks, that is in gates.

This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and machine learning algorithms. We propose a general framework based on three layers (physical, data and decision) that defines the possible designing options so that the rare events/anomalies can be detected ultra-reliably. This general framework is then applied in a well-known benchmark scenario, namely Tennessee Eastman Process. We then analyze this benchmark under three threads related to data processes: acquisition, fusion and analytics. Our numerical results indicate that: (i) event-driven data acquisition can significantly decrease the number of samples while filtering measurement noise, (ii) mutual information data fusion method can significantly decrease the variable spaces and (iii) quantitative association rule mining method for data analytics is effective for the rare event detection, identification and diagnosis. These results indicates the benefits of an integrated solution that jointly considers the different levels of data processing following the proposed general three layer framework, including details of the communication network and computing platform to be employed., Grant numbers : FIREMAN - Framework for the Identification of Rare Events via MAchine learning and IoT Networks.© 2020 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.

The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed production lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its operational processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.

The wide application of Internet of Things (IoT) has fostered the development of Industry 4.0. In manufacturing domain, Industrial IoT (IIoT) are key components of the Factories of the Future (FoF). The big IIoT data are the foundation of implementing data-driven strategies. In current industrial practice, most of these IIoT data are wasted or fragmented in data silos due to security and privacy concerns. Novel data management approaches are required to replace traditional centralized data management systems. The rapid development of blockchain technologies provides a novel solution for this challenge lever-aging its unique characteristics such as decentralization, immutability and traceability. However, blockchain is inefficient for exchanging big data due to transaction throughput limits. The peer-to-peer InterPlanetary File System (IPFS) provides a suitable complement for blockchain. Therefore, this paper aims to propose a decentralized IIoT data management approach based on blockchain and IPFS technology. The architecture and enabling technologies of the proposed system are introduced. A proof-of-concept implementation is realized and relevant experiments are conducted. The results demonstrated the feasibility of the proposed approach.

Maintenance Management is a key pillar in companies, especially energy utilities, which have high investments in assets, and so for its proper contribution has to be integrated and aligned with other departments in order to conserve the asset value and guarantee the services. In this line, Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and analyze data from industrial assets. They are based on the use of digital technologies in industry. Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper states that the IAMPs should also support a much better balanced and more strategic view in existing asset management and concretely in maintenance management. The real transformation can be achieved if these platforms help to understand business priorities in work and investments. In this paper, we first discuss about the factors explaining IAMP growth, then we explain the importance of considering, well in advance, those managerial aspects of the problem, for proper investments and suitable digital transformation through the adoption and use of IAMPs. A case study in the energy sector is presented to map, or to identify, those platform modules and Apps providing important value-added features to existing asset management practices. Later, attention is paid to the methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate this point, a methodology for the development of the asset criticality analysis process data model is proposed. Finally, the paper includes conclusions of the work and relevant literature to this research

The Industrial Internet of Things (IIoT) provides automation solutions for industrial processes through the interconnection of different sensors, actuators and robotic devices to the Internet, enabling for the automation of manufacturing processes through Factory Automation. However, IIoT processes are often critical, and require very high Quality of Service (QoS) to work properly, as well as network scalability and flexibility. Fog computing, a paradigm that brings computation and storage devices closer to the edge of the network to enhance QoS, as well as Software-Defined Networking (SDN), which enables for network scalability and flexibility, can be integrated into IIoT architectures in the form of fog nodes that integrate both, computation resources and SDN capabilities, to meet these needs. However, the QoS of the IIoT system depends on the placement of these fog nodes, creating a need to obtain placements that optimize QoS in order to meet the requirements by minimizing the latency between the fog nodes and the IIoT devices that consume their services. In this paper, this fog node placement problem is formalized and solved by means of Mixed Integer Programming. We also show relevant experimental results of our formulation and analyze its performance.

International audience; The main requirement of Industrial Internet of Things (IIoT) is reliability with a targeted reliability of above 99.999%. Then come latency and energy-efficiency requirements. The next step for the IIoT is to target time-critical applications. Even if IIoT technologies are now adopted worldwide, challenges remain and some of the limits of the technologies are still not fully understood. In this paper, we address TSCH-based (Time Slotted Channel Hopping) Wireless Sensor Networks and study their latency and lifetime limits under real-world conditions. We compute theoretical bounds on the end-to-end latency in a perfect radio environment and then in real deployments with unreliable links. We compare the performance of different scheduling algorithms and evaluate the impact of unreliable links on end-to-end latency and network lifetime, by means of simulations using traces collected from real deployed TSCH networks.

There are a lot of blockchain platform implementations available today. To be integrated into the smart space for Industrial IoT the blockchain platform should support not only token exchange but also smart contract distribution and launching, fault tolerance consensus mechanism and equivalence between participants to create and implement new blocks and contracts. The paper provides analysis of the most used consensus mechanisms, specific features of public (permissionless) and private (permissioned) blockchains. Also a description of blockchain platforms that satisfy the requirements for the IIoT platform development is provided. By the result of the analysis the platform and specific modules have been selected for implementation of blockchain for industrial IIoT platform.

Industrial IoT (IIoT) refers to the application of IoT in industrial management to improve the overall operational efficiency. With IIoT that accelerates the industrial automation process by enrolling thousands of IoT devices, strong security foundations are to be deployed befitting the distributed connectivity and constrained functionalities of the IoT devices. Recent years witnessed severe attacks exploiting the vulnerabilities in the devices of IIoT networks. Moreover, attackers can use the relations among the vulnerabilities to penetrate deep into the network. This paper addresses the security issues in IIoT network because of the vulnerabilities existing in its devices. As graphs are efficient in representing relations among entities, we propose a graphical model representing the vulnerability relations in the IIoT network. This helps to formulate the security issues in the network as graph-theoretic problems. The proposed model acts as a security framework for the risk assessment of the network. Furthermore, we propose a set of risk mitigation strategies to improve the overall security of the network. The strategies include detection and removal of the attack paths with high risk and low hop-length. We also discuss a method to identify the strongly connected vulnerabilities referred as hot-spots. A use-case is discussed and various security parameters are evaluated. The simulation results with graphs of different sizes and structures are presented for the performance evaluation of the proposed techniques against the changing dynamics of the IIoT networks.

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. Therefore, ensuring quality of service (QoS) through service-level agreements (SLAs) for such cloud-based services is crucial for both the service providers and service consumers. As SLAs are critical for cloud deployments and wider adoption of cloud services, the management of SLAs in cloud and IoT has thus become an important and essential aspect. This paper investigates the existing research on the management of SLAs in IoT applications that are based on cloud services. For this purpose, a systematic mapping study (a well-defined method) is conducted to identify the published research results that are relevant to SLAs. This paper identifies 328 primary studies and categorizes them into seven main technical classifications: SLA management, SLA definition, SLA modeling, SLA negotiation, SLA monitoring, SLA violation and trustworthiness, and SLA evolution. This paper also summarizes the research types, research contributions, and demographic information in these studies. The evaluation of the results shows that most of the approaches for managing SLAs are applied in academic or controlled experiments with limited industrial settings rather than in real industrial environments. Many studies focus on proposal models and methods to manage SLAs, and there is a lack of focus on the evolution perspective and a lack of adequate tool support to facilitate practitioners in their SLA management activities. Moreover, the scarce number of studies focusing on concrete metrics for qualitative or quantitative assessment of QoS in SLAs urges the need for in-depth research on metrics definition and measurements for SLAs.

Industrial IoT (IIoT) is a novel concept of a fully connected, transparent, automated, and intelligent factory setup improving manufacturing processes and efficiency. To achieve this, existing hierarchical models must transition to a fully connected vertical model. Since IIoT is a novel approach, the environment is susceptible to cyber threat vectors, standardization, and interoperability issues, bridging the gaps at the IT/OT ICS (industrial control systems) level. IIoT M2M communication relies on new communication models (5G, TSN ethernet, self-driving networks, etc.) and technologies which require challenging approaches to achieve the desired levels of data security. Currently there are no methods to assess the vulnerabilities/risk impact which may be exploited by malicious actors through system gaps left due to improper implementation of security standards. The authors are currently working on an Industry 4.0 cybersecurity project and the insights provided in this paper are derived from the project. This research enables an understanding of converged/hybrid cybersecurity standards, reviews the best practices, and provides a roadmap for identifying, aligning, mapping, converging, and implementing the right cybersecurity standards and strategies for securing M2M communications in the IIoT.

In recent years, integration of wireless sensor networks in industrial environments has greatly increased. With this trend, new fields such as industrial IoT have arisen, which in turn have opened the doors to new possibilities that are shaping the future of industrial automation. In contrast to regular wireless networks, however, industrial applications of WSN are characterized for being time-critical systems with highly stringent requirements that challenge all available technologies. Because of its ultra-low energy properties, compatibility with most mobile units, reduced production costs, robustness and high throughput, Bluetooth low energy (BLE) is a potential candidate for these settings. This article explores the potential of BLE of meeting the real-time demands found in the domain of industrial process automation and industrial IoT. In order to evaluate the suitability of the protocol for these scenarios, the effect of adaptations in the retransmission scheme on the reliability and timeliness performance are thoroughly studied. Three retransmission schemes are evaluated and simulation results proved that by optimally modifying the BLE retransmission model, a maximum delay below 46 ms and a packet loss rate in the order of $$10^{-5}$$ can be obtained, enabling BLE to fulfill the requirements of even the most demanding cases within the considered range of applications.

This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.

The Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least R % of messages generated by sensor nodes are delivered to the sink with a latency &le, L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. M F a i r is very easy to compute, whereas M O p t minimizes the total number of transmissions necessary for a message to reach the sink. M F a i r and M O p t are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions M a x T r a n s is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming M a x T r a n s is fixed. For both TSCH networks, we show that M O p t provides a better reliability and a longer lifetime than M F a i r , which provides a shorter average end-to-end latency. M O p t provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler.

Blockchain is a technology that can ensure data integrity in a distributed network, and it is actively applied in various fields. Recently, blockchain is gaining attention due to combining with the Internet of Things (IoT) technology in the industrial field. Moreover, many researchers have proposed the Industrial IoT (IIoT) architecture with blockchain for data integrity and efficient management. The IIoT network consists of many heterogeneous devices (e.g., sensors, actuators, and programmable logic controllers (PLC)) with resources-constrained, and the availability of the network must be preferentially considered. Therefore, applying the existed blockchain technology is still challenging. There are some results about the technique of constructing blockchain lightly to solve this challenge. However, in these results, the analysis in perspective of cryptographic performance (area, throughput, and power consumption) has not been considered sufficiently, or only focused on the architecture of the blockchain network. The blockchain technology is based on cryptographic techniques, and the main part is a cryptographic hash function. Therefore, if we construct the blockchain-based IIoT architecture, we have to consider the performance of the hash function. Many lightweight hash functions have been proposed recently for the resource-constrained environment, and it can also be used to the blockchain. Therefore, in this paper, we analyze the considerations of lightweight blockchain for IIoT. Also, we conduct an analysis of lightweight hash for blockchain, and propose a new lightweight hash-based blockchain architecture that can change the hash algorithm used for mining adjust to network traffic.

This paper introduces an industrial cyber-physical system (CPS) based on the Internet of Things (IoT) that is designed to detect rare events based on machine learning. The framework follows the following three generic steps: (1) Large data acquisition / dissemination: A physical process is monitored by sensors that pre-process the (assumed large) collected data and send the processed information to an intelligent node (e.g., aggregator, central controller); (2) Big data fusion: The intelligent node uses machine learning techniques (e.g., data clustering, neural networks) to convert the received ("big") data to useful information to guide short-term operational decisions related to the physical process; (3) Big data analytics: The physical process together with the acquisition and fusion steps can be virtualized, building then a cyber-physical process, whose dynamic performance can be analyzed and optimized through visualization (if human intervention is available) or artificial intelligence (if the decisions are automatic) or a combination thereof. Our proposed general framework, which relies on an IoT network, aims at an ultra-reliable detection/prevention of rare events related to a pre-determined industrial physical process (modelled by a particular signal). The framework will be process- independent, however, our demonstrated solution will be designed case-by-case. This paper is an introduction to the solution to be developed by the FIREMAN consortium. Post-print / Final draft

open access article The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

In 2016, the IEEE task group ah (TGah) released a new standard called IEEE 802.11ah, and industrial Internet of Things (IoT) is one of its typical use cases. The restricted access window (RAW) is one of the core MAC mechanisms of IEEE 802.11ah, which aims to address the collision problem in the dense wireless networks. However, in each RAW period, stations still need to contend for the channel by Distributed Coordination Function and Enhanced Distributed Channel Access (DCF/EDCA), which cannot meet the real-time requirements of most industrial applications. In this paper, we propose a channel-aware contention window adaption (CA-CWA) algorithm. The algorithm dynamically adapts the contention window based on the channel status with an external interference discrimination ability, and improves the real-time performance of the IEEE 802.11ah. To validate the real-time performance of CA-CWA, we compared CA-CWA with two other backoff algorithms with an NS-3 simulator. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11ah.

In moving towards highly connected integrated systems, the Industrial Internet of Thing (IIoT) promises a wealth of ben- efits. Enhanced usage of existing data sources, and integration of additional generation points, provide system users with greater visibility of industrial processes. This visibility can be used to identify and address inefficiencies. Within the context of discrete manufacturing, examples include reduction of waste materials and energy consumption. However, while one becomes engrossed in the use of big-data analytics, cloud technologies, and seamless adoption through hardware gateways, decade old systems are dropped into a technological melting pot of modern IoT, with little consideration of additional cyber security risks. Numerous works have provided evidence to suggest industrial systems are highly vulnerable to cyber attacks, from both a device and communication protocol perspective, yet efforts to automatically identify vulnerabilities are limited. This presents a significant gap, with vulnerability exploitation harbouring potentially life-threatening impact. Here we address this gap through the development of PIVoT Scan, an industrially-aware vulnerability scanner, capable of assessing a diverse range of devices and communication protocols predominantly situated within the legacy layers of IIoT environments — “The forgotten I”. Furthermore, we demonstrate PIVoT Scan’s ability to outperform a leading vulnerability scanner, Nessus.

Industrial Control Systems (ICS) play an important role in the monitoring, control and automation of critical infrastructure such as water, gas, oil and electricity. Recent years have seen a number of high profile cyber attacks on such infrastructure exemplified by Stuxnet and the Ukrainian Power Grid attacks. This naturally begs the question: how should we manage cyber security risks in such infrastructure on which the day-to-day functioning of societies rely? What are the complexities of managing security in a landscape shaped by the often competing demands of a variety of stakeholders, e.g., managers, control engineers, enterprise IT personnel and field site operators? What are the challenges posed by the convergence of Internet of Things (IoT) and critical infrastructure through the so-called Industrial Internet of Things (IIoT)? In this paper, we discuss insights from a multi-year programme of research investigating these issues and the challenges to addressing them.

The Blockchain is a novel technology with a wide range of potential industrial applications. Despite a vast range of tests, prototypes, and proof of concepts implemented in the last years, the industrial use of Blockchain technology is still in the early stages. Enabling the interaction of industrial Internet of Things (IOT) platforms with Blockchain might be challenging because standards are still missing in both these technologies. Moreover, integrating productive assets with distributed data exchange and storage technologies is a kind of activity that needs to take into account various aspects, in particular: interoperability, portability, scalability, and security that need to be guaranteed by design. This paper describes the implementation of a portable, platform-agnostic and secure Blockchain Tokenizer for Industrial IOT trustless applications. The Industrial Blockchain Tokenizer (IBT) is based on an industrial data acquisition unit able to gather data from both modern and legacy machines while also interfacing directly with sensors. Acquired data can be processed locally enabling an edge filtering paradigm and then sent to any Blockchain platform. The system has been designed, implemented and then tested on two supply chain scenarios. Tests demonstrated the system capability to act as a bridge between industrial assets and Blockchain platforms enabling the generation of immutable and trust-less “digital twins” for industrial IOT applications.

The Industrial Internet of Things (IIoT) is a key pillar of the Fourth Industrial Evolution or Industry 4.0. It aims to achieve direct information exchange between industrial machines, people, and processes. By tapping and analysing such data, IIoT can more importantly provide for significant improvements in productivity, product quality, and safety via proactive detection of problems in the performance and reliability of production machines, workers, and industrial processes. While the majority of existing IIoT research is currently focusing on the predictive maintenance of industrial machines (unplanned production stoppages lead to significant increases in costs and lost plant productivity), this paper focuses on monitoring and assessing worker productivity. This IIoT research is particularly important for large manufacturing plants where most production activities are performed by workers using tools and operating machines. With this aim, this paper introduces a novel industrial IoT solution for monitoring, evaluating, and improving worker and related plant productivity based on workers activity recognition using a distributed platform and wearable sensors. More specifically, this IIoT solution captures acceleration and gyroscopic data from wearable sensors in edge computers and analyses them in powerful processing servers in the cloud to provide a timely evaluation of the performance and productivity of each individual worker in the production line. These are achieved by classifying worker production activities and computing Key Performance Indicators (KPIs) from the captured sensor data. We present a real-world case study that utilises our IIoT solution in a large meat processing plant (MPP). We illustrate the design of the IIoT solution, describe the in-plant data collection during normal operation, and present the sensor data analysis and related KPI computation, as well as the outcomes and lessons learnt.

The IEEE 802.15.4 time-slotted channel hopping (TSCH) is a medium access control layer protocol designed for industrial internet of things applications. The TSCH focuses on the media access control layer only, while the construction of the routes relies on network layer protocols such as the routing protocol for low-power and lossy networks. The selection of the routes toward the sink plays a significant role in the nodes duty cycle, the delay, and the reliability of the network. In this paper, we formulate a multiobjective problem taking into account the schedule length (duty cycle/delay), the average number of hops (delay), and the cost of constructing the routes (reliability). To solve this problem, we use a scalarizing version where the objective function is defined as a convex combination of the three aforementioned parameters. Optimal computational as well as simulation results are presented. The findings of the current study can be used either as an optimal static routing solution when the link qualities are known and do not considerably change through time, or as a benchmark when designing low-power distributed protocols for TSCH networks.

The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments.

Exploiting the advantages brought by long-range radio communications and extremely low power consumptions, LoRaWAN is capable to support low rate industry 4.0 services. Despite being energy efficient, LoRa motes can still undergo frequent battery replenishments caused by the monitoring requirements of industrial applications. Duty-cycle constrained operations can partially face this issue at the expense of increased communication delays, which, in turn, inflate higher costs due to damaged products on the production line. This letter proposes a model to analyze this cost tradeoff against different sensing intervals. It further highlights the impact of energy harvesting sources on this cost relationship mapping a way toward improved production efficiency.

The Internet of Things (IoT) is increasingly part of daily life. However, the development of IoT applications still faces many problems, such as heterogeneity, complex management, and other difficulties. In this paper, first, the open source technologies of IoT are surveyed. We compare these technologies from the point of view of different levels of technical requirements, such as device management, data management, communication, intelligent data processing, security and privacy protection, we also look at requirements of application development and deployment. Second, an IoT integrated development platform architecture for IoT applications based on open source ecosystem is proposed and evaluated in an industrial setting. We applied P2P technology to distributed resource management and blockchain-based smart contract mechanics for resource billing management. The results show that the IoT gateway based on an open source ecosystem had a stable and reliable system performance with a certain data size and concurrency scale. These conditions satisfy the application requirements of the IoT in most sensing environments.