More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

/pdf/internet-of-vehicles-from-intelligent-grid-to-autonomous-pma0tn3ofd.pdf

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach. 1 Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes. 1 Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Toward Edge-Based Deep Learning in Industrial Internet of Things

The evolution of the Internet of Things (IoT) over the past 20 years

The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous benefits (good use) that machine learning has brought, both in general, and specifically for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen.

/pdf/machine-learning-for-security-and-the-internet-of-things-the-29e8cxpjhl.pdf

Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly

State estimation plays a critical role in monitoring and managing operation of smart grid. Nonetheless, recent research efforts demonstrate that data integrity attacks are able to bypass the bad data detection mechanism and make the system operator obtain the misleading states of system, leading to massive economic losses. Particularly, data integrity attacks have become critical threats to the power grid. In this paper, we propose a deep-Q-network detection (DQND) scheme to defend against data integrity attacks in alternating current (AC) power systems. DQND is a deep reinforcement learning scheme, which avoids the problem of curse of dimension that conventional reinforcement learning schemes have. Our strategy in DQND applies a main network and a target network to learn the optimal defending strategy. To improve the learning efficiency, we propose the quantification of observation space and utilize the concept of slide window as well. The experimental evaluation results show that the DQND outperforms the existing deep reinforcement learning-based detection scheme in terms of detection accuracy and rapidity in the IEEE 9, 14, and 30 bus systems.

/pdf/defending-against-data-integrity-attacks-in-smart-grid-a-5eb6lr7537.pdf

Defending Against Data Integrity Attacks in Smart Grid: A Deep Reinforcement Learning-Based Approach

The widespread adoption of the Internet of Things (IoT) technologies has drastically increased the breadth and depth of attack surfaces in networked systems, providing new mechanisms for the intrusion. In the context of smart-world critical infrastructures and cyber-physical systems, the rapid adoption of the IoT systems and infrastructures without thorough consideration for the risks and vulnerabilities has the potential for catastrophic damage to the privacy, safety, and security of individuals and corporations. While the IoT systems have the potential to increase productivity, accountability, traceability, and efficiency, their potential weaknesses are also more abundant. In this paper, we provide critical consideration of the security of the IoT systems as applied to smart-world critical infrastructures. Particularly, we carry out a detailed assessment of vulnerabilities in IoT-based critical infrastructures from the perspectives of applications, networking, operating systems, software, firmware, and hardware. In addition, we highlight the three key critical infrastructure IoT-based cyber-physical systems, namely the smart transportation, smart manufacturing, and smart grid. Moreover, we provide a broad collection of attack examples upon each of the key applications. Furthermore, we introduce a case study, in which we assess the impacts of potential attacks on critical IoT-based systems, using the smart transportation system as an example. Finally, we provide a set of best practices and address the necessary steps to enact countermeasures for any generic IoT-based critical infrastructure system.

/pdf/secure-internet-of-things-iot-based-smart-world-critical-3lxmbqjmn7.pdf

Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities

With the development of the Internet of Things (IoT), massive numbers of IoT devices (smart sensors, cameras, phones, and so on) have been deployed and utilized in various environments, supporting numerous smart-world applications. Those devices communicate with computing infrastructure servers over network infrastructures to send relevant collected data in order to link physical objects to the cyber world. As the number of IoT devices increases rapidly, the volume of collected data likewise increases prodigiously. Thus, how to search for and through specific IoT datasets among the enormous amount of data become critical issues for potential data consumers. Moreover, various IoT devices and applications establish IoT-based systems, also known as the smart-world systems or smart cyber-physical systems (CPS), such as smart grids, smart transportation, smart healthcare, smart cities, smart homes, and smart manufacturing systems, among others. However, the individual CPS are independently designed and deployed, such that they collect and analyze data independently, with no information sharing or interconnection, raising serious challenges in searching for valuable information. Thus, in order to efficiently and precisely utilize the IoT datasets, suitable search techniques designed for the IoT environments are fundamental. In this paper, we first summarize popular web search techniques and survey existing research on the search and analysis related to the IoT. We then outline the opportunities and challenges of the IoT search techniques. Furthermore, we propose a problem space for the IoT search techniques and provide a clear view of potential future research directions.

/pdf/search-engine-for-the-internet-of-things-lessons-from-web-3535g7gd5n.pdf

Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval

/pdf/towards-efficient-and-intelligent-internet-of-things-search-10vwhed452.pdf

Towards Efficient and Intelligent Internet of Things Search Engine

The Internet of Things (IoT) now thoroughly suffuses our world, building on network communication and software technologies to increasingly interconnect sensors and actuators for unprecedented data collection. Yet, massively distributed IoT systems may be isolated in traditional intranets, creating unnecessary redundancy between different organizations that fulfill the same functions. To address these issues and develop an IoT Search Engine that can fully cope with the challenges of modern IoT deployment, in this paper we propose an IoT search engine platform using Python-based COAP (Constrained Application Protocol) which can discover and subscribe IoT sensors within the coverage of IoT search engine gateways. The search engine can retrieve data from sensors of different IoT systems, which we demonstrate using a traffic dataset to simulate autonomous vehicle queries. Our implementation also includes two designed algorithms to demonstrate mechanisms for data and data optimization.

Search Engine for Heterogeneous Internet of Things Systems and Optimization

Training machine learning models, such as reinforcement learning models, require a significant investment of time, and a trained model can only work on a specific system in a specific environment. When the application scenario of reinforcement learning changes, or the application environment changes, the reinforcement learning model needs to be retrained. Thus, it is critical to design techniques that can reduce the overhead of retraining reinforcement learning models, enabling them adapt to constantly changing environments. In this paper, toward improving the performance of learning models in dynamic Industrial Internet of Things (IIoT), we propose an online continuous reinforcement learning strategy. In our process, when the retraining condition is triggered, our online continuous learning strategy will re-engage the training process and update the well-trained model. To evaluate the performance of our proposed approach, we categorize the entire application space for applying reinforcement learning to IIoT systems into four scenarios, namely, non-continuous learning without learning model sharing, non-continuous learning with learning model sharing, continuous learning without learning model sharing, and continuous learning without learning model sharing. For each scenario, we design a Q-learning based reinforcement learning algorithm. Via extensive evaluation, our results show that the online continuous reinforcement learning approach that we propose can significantly reduce the overhead of retraining the learning model, enabling the learning algorithm to quickly adapt to a changing environment.

Cheng Qian

Papers

Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities

Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities

Towards Efficient and Intelligent Internet of Things Search Engine

Search Engine for Heterogeneous Internet of Things Systems and Optimization

Towards Online Continuous Reinforcement Learning on Industrial Internet of Things