With the increasing demand for Lithium-ion batteries in an electric vehicle (EV), it is always crucial to develop a highly accurate and low-cost state estimation method for the battery management system (BMS). Presently, the dual extended Kalman filter (DEKF) is extensively utilized for online SOC estimation. However, the problem of battery model parameter divergence from the true value greatly affects the estimation accuracy under realistic dynamic loading conditions. In this paper, the new dual forgetting factor-based adaptive extended Kalman filter (DFFAEKF) is proposed for SOC estimation. The proposed SOC estimation method is combined with the simple SOE estimation approach to develop the combined SOC and SOE estimation method. The quantitative relationships between SOC and SOE for all the test battery cells, which are established with the experimental data collected from different constant current discharge profiles are employed for SOE estimation. To evaluate the performance of the developed combined SOC and SOE estimation method, the three different chemistries battery cells are chosen for the testing under different dynamic loading profiles such as dynamic stress test (DST) and US06 drive cycle. For all the considered test battery cells, the experimental results indicated that the combined SOC and SOE estimation method using the proposed DFFAEKF can estimate the battery states under dynamic operating conditions with root mean square error (RMSE) less than 0.85% and 0.95% respectively. The proposed method also demonstrates fast convergence to its true value under erroneous initial conditions. Additionally, the order of worst-case big O notation complexity of the proposed DFFAEKF is equivalent to DEKF. Besides, the simplicity of the proposed method also supports to reduce the computational burden of the processor used in BMSs, and therefore it is well-suited for EV applications.

Internet of Drones (IoD) is a decentralized network and management framework that links drones' access to the controlled airspace and provides inter-location navigation services. The interconnection of drones in the IoD network is through the Internet of Things (IoT). Hence the IoD network is vulnerable to all the security and privacy threats that affect IoT networks. It is highly required to safeguard a good atmosphere free from security and privacy threats to get the desired performance from IoD applications. Security and privacy issues have significantly restricted the overall influence of the IoD paradigm. There are existing survey studies that helped lay a vital foundation for understanding the IoD security and privacy issues. However, not all have thoroughly investigated the level of security and privacy threats associated with the various drone categories. Besides, most existing review studies do not examine secured IoD architecture. This paper aims to assess the recent trends in the security and privacy issues that affect the IoD network. We investigate the level of security and privacy threats of the various drone categories. We then highlight the need for secured IoD architecture and propose one. We also give a comprehensive taxonomy of the attacks on the IoD network. Moreover, we review the recent IoD attack mitigating techniques. We also provide the performance evaluation methods and the performance metrics employed by the techniques. Finally, we give research future direction to help researchers identify the latest opportunities in IoD research.

The domain of underwater wireless sensor networks (UWSNs) had received a lot of attention recently due to its significant advanced capabilities in the ocean surveillance, marine monitoring and application deployment for detecting underwater targets. However, the literature have not compiled the state-of-the-art along its direction to discover the recent advancements which were fuelled by the underwater sensor technologies. Hence, this paper offers the newest analysis on the available evidences by reviewing studies in the past five years on various aspects that support network activities and applications in UWSN environments. This work was motivated by the need for robust and flexible solutions that can satisfy the requirements for the rapid development of the underwater wireless sensor networks. This paper identifies the key requirements for achieving essential services as well as common platforms for UWSN. It also contributes a taxonomy of the critical elements in UWSNs by devising a classification on architectural elements, communications, routing protocol and standards, security, and applications of UWSNs. Finally, the major challenges that remain open are presented as a guide for future research directions.

Internet of Things (IoT) facilitates the integration between objects and different sensors to provide communication among them without human intervention. However, the extensive demand for IoT and its various applications has continued to grow, coupled with the need to achieve foolproof security requirements. IoT produces a vast amount of data under several constraints such as low processor, power, and memory. These constraints, along with the invaluable data produced by IoT devices, make IoT vulnerable to various security attacks. This paper presents an overview of IoT, its well-known system architecture, enabling technologies, and discusses security challenges and goals. Furthermore, we analyze security vulnerabilities and provide state-of-the-art security taxonomy. The taxonomy of the most relevant and current IoT security attacks is presented for application, network, and physical layers. While most other surveys studied one of the areas of security measures, this study considers and reports on the most advanced security countermeasures within the areas of autonomic, encryption, and learning-based approaches. Additionally, we uncover security challenges that may be met by the research community regarding security implementation in heterogeneous IoT environment. Finally, we provide different visions about possible security solutions and future research directions.

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.

Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.