A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Securing IP Mobility Management for Vehicular Ad Hoc Networks

The proliferation of Intelligent Transportation Systems (ITSs) applications, such as Internet access and Infotainment, highlights the requirements for improving the underlying mobility management protocols for Vehicular Ad Hoc Networks (VANETs). Mobility management protocols in VANETs are envisioned to support mobile nodes (MNs), i.e., vehicles, with seamless communications, in which service continuity is guaranteed while vehicles are roaming through different RoadSide Units (RSUs) with heterogeneous wireless technologies. Due to its standardization and widely deployment, IP mobility (also called Mobile IP (MIP)) is the most popular mobility management protocol used for mobile networks including VANETs. In addition, because of the diversity of possible applications, the Internet Engineering Task Force (IETF) issues many MIP's standardizations, such as MIPv6 and NEMO for global mobility, and Proxy MIP (PMIPv6) for localized mobility. However, many challenges have been posed for integrating IP mobility with VANETs, including the vehicle's high speeds, multi-hop communications, scalability, and ef ficiency. From a security perspective, we observe three main challenges: 1) each vehicle's anonymity and location privacy, 2) authenticating vehicles in multi-hop communications, and 3) physical-layer location privacy. In transmitting mobile IPv6 binding update signaling messages, the mobile node's Home Address (HoA) and Care-of Address (CoA) are transmitted as plain-text, hence they can be revealed by other network entities and attackers. The mobile node's HoA and CoA represent its identity and its current location, respectively, therefore revealing an MN's HoA means breaking its anonymity while revealing an MN's CoA means breaking its location privacy. On one hand, some existing anonymity and location privacy schemes require intensive computations, which means they cannot be used in such time-restricted seamless communications. On the other hand, some schemes only achieve seamless communication through low anonymity and location privacy levels. Therefore, the trade-off between the network performance, on one side, and the MN's anonymity and location privacy, on the other side, makes preservation of privacy a challenging issue. In addition, for PMIPv6 to provide IP mobility in an infrastructure-connected multi-hop VANET, an MN uses a relay node (RN) for communicating with its Mobile Access Gateway (MAG). Therefore, a mutual authentication between the MN and RN is required to thwart authentication attacks early in such scenarios. Furthermore, for a NEMO-based VANET infrastructure, which is used in public hotspots installed inside moving vehicles, protecting physical-layer location privacy is a prerequisite for achieving privacy in upper-layers such as the IP-layer. Due to the open nature of the wireless environment, a physical-layer attacker can easily localize users by employing signals transmitted from these users. In this dissertation, we address those security challenges by proposing three security schemes to be employed for different mobility management scenarios in VANETs, namely, the MIPv6, PMIPv6, and Network Mobility (NEMO) protocols. First, for MIPv6 protocol and based on the onion routing and anonymizer, we propose an anonymous and location privacy-preserving scheme (ALPP) that involves two complementary sub-schemes: anonymous home binding update (AHBU) and anonymous return routability (ARR). In addition, anonymous mutual authentication and key establishment schemes have been proposed, to authenticate a mobile node to its foreign gateway and create a shared key between them. Unlike existing schemes, ALPP alleviates the tradeoff between the networking performance and the achieved privacy level. Combining onion routing and the anonymizer in the ALPP scheme increases the achieved location privacy level, in which no entity in the network except the mobile node itself can identify this node's location. Using the entropy model, we show that ALPP achieves a higher degree of anonymity than that achieved by the mix-based scheme. Compared to existing schemes, the AHBU and ARR sub-schemes achieve smaller computation overheads and thwart both internal and external adversaries. Simulation results demonstrate that our sub-schemes have low control-packets routing delays, and are suitable for seamless communications. Second, for the multi-hop authentication problem in PMIPv6-based VANET, we propose EM3A, a novel mutual authentication scheme that guarantees the authenticity of both MN and RN. EM3A thwarts authentication attacks, including Denial of service (DoS), collusion, impersonation, replay, and man-in-the-middle attacks. EM3A works in conjunction with a proposed scheme for key establishment based on symmetric polynomials, to generate a shared secret key between an MN and an RN. This scheme achieves lower revocation overhead than that achieved by existing symmetric polynomial-based schemes. For a PMIP domain with n points of attachment and a symmetric polynomial of degree t, our scheme achieves t x 2^n-secrecy, whereas the existing symmetric polynomial-based authentication schemes achieve only t-secrecy. Computation and communication overhead analysis as well as simulation results show that EM3A achieves low authentication delay and is suitable for seamless multi-hop IP communications. Furthermore, we present a case study of a multi-hop authentication PMIP (MA-PMIP) implemented in vehicular networks. EM3A represents the multi-hop authentication in MA-PMIP to mutually authenticate the roaming vehicle and its relay vehicle. Compared to other authentication schemes, we show that our MA-PMIP protocol with EM3A achieves 99.6% and 96.8% reductions in authentication delay and communication overhead, respectively. Finally, we consider the physical-layer location privacy attacks in the NEMO-based VANETs scenario, such as would be presented by a public hotspot installed inside a moving vehicle. We modify the obfuscation, i.e., concealment, and power variability ideas and propose a new physical-layer location privacy scheme, the fake point-cluster based scheme, to prevent attackers from localizing users inside NEMO-based VANET hotspots. Involving the fake point and cluster based sub-schemes, the proposed scheme can: 1) confuse the attackers by increasing the estimation errors of their Received Signal Strength (RSSs) measurements, and 2) prevent attackers' monitoring devices from detecting the user's transmitted signals. We show that our scheme not only achieves higher location privacy, but also increases the overall network performance. Employing correctness, accuracy, and certainty as three different metrics, we analytically measure the location privacy achieved by our proposed scheme. In addition, using extensive simulations, we demonstrate that the fake point-cluster based scheme can be practically implemented in high-speed VANETs' scenarios

A Framework for the Verification and Validation of Artificial Intelligence Machine Learning Systems

An effective verification and validation (V&V) process framework for the white-box and black-box testing of artificial intelligence (AI) machine learning (ML) systems is not readily available. This research uses grounded theory to develop a framework that leads to the most effective and informative white-box and black-box methods for the V&V of AI ML systems. Verification of the system ensures that the system adheres to the requirements and specifications developed and given by the major stakeholders, while validation confirms that the system properly performs with representative users in the intended environment and does not perform in an unexpected manner. Beginning with definitions, descriptions, and examples of ML processes and systems, the research results identify a clear and general process to effectively test these systems. The developed framework ensures the most productive and accurate testing results. Formerly, and occasionally still, the system definition and requirements exist in scattered documents that make it difficult to integrate, trace, and test through V&V. Modern system engineers along with system developers and stakeholders collaborate to produce a full system model using model-based systems engineering (MBSE). MBSE employs a Unified Modeling Language (UML) or System Modeling Language (SysML) representation of the system and its requirements that readily passes from each stakeholder for system information and additional input. The comprehensive and detailed MBSE model allows for direct traceability to the system requirements. xxiv To thoroughly test a ML system, one performs either white-box or black-box testing or both. Black-box testing is a testing method in which the internal model structure, design, and implementation of the system under test is unknown to the test engineer. Testers and analysts are simply looking at performance of the system given input and output. White-box testing is a testing method in which the internal model structure, design, and implementation of the system under test is known to the test engineer. When possible, test engineers and analysts perform both black-box and white-box testing. However, sometimes testers lack authorization to access the internal structure of the system. The researcher captures this decision in the ML framework. No two ML systems are exactly alike and therefore, the testing of each system must be custom to some degree. Even though there is customization, an effective process exists. This research includes some specialized methods, based on grounded theory, to use in the testing of the internal structure and performance. Through the study and organization of proven methods, this research develops an effective ML V&V framework. Systems engineers and analysts are able to simply apply the framework for various white-box and black-box V&V testing circumstances

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Near hybrid passenger vehicle development program, phase 1. Appendices A and B. Mission analysis and performance specification studies report, volume 1

The three most promising vehicle use patterns (missions) for the near term electric hybrid vehicle were found to be all-purpose city driving, commuting, and family and civic business. The mission selection process was based principally on an analysis of the travel patterns found in the Nationwide Transportation Survey and on the Los Angeles and Washington, D.C. origin-destination studies data. Travel patterns in turn were converted to fuel requirements for 1985 conventional and hybrid cars. By this means, the potential fuel savings for each mission were estimated, and preliminary design requirements for hybrid vehicles were derived

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe