An Improved Three-dimensional DV-Hop Localization Algorithm Optimized by Adaptive Cuckoo Search Algorithm

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Secure Intelligent Vehicular Network Including Real-Time Detection of DoS Attacks in IEEE 802.11P Using Fog Computing

VANET (Vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. Intermittent exchange of real-time safety message delivery in VANET has become an urgent concern, due to DoS (Denial of service), and smart and normal intrusions (SNI) attacks. Intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication need. In this research, Fog computing (FC) integrates with hybrid optimization algorithms (OAs) including: Cuckoo search algorithm (CSA), Firefly algorithm (FA) and Firefly neural network, in addition to key distribution establishment (KDE), for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme which is also termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) utilizes feedforward back propagation neural network (FFBP-NN). This is also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The proposed scheme is initially compared with the Cuckoo and FA, and the Firefly neural network to evaluate the QoS parameters such as jitter and throughput. In addition, VANET is a means whereby Intelligent Transportation System (ITS) has become important for the benefit of daily lives. Therefore, real-time detection of all form attacks including hybrid DoS attacks in IEEE 802.11p, has become an urgent attention for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate enormous amount of message. This leads to the RSU (roadside unit) or the CPU (central processing unit) overutilization for computation. Therefore, it is required that efficient storage and intelligence VANET infrastructure architecture (VIA), which include trustworthiness is desired. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computations, and communication need for VANET. This dissertation also utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence including: Cuckoo/CSA Artificial Bee Colony (ABC) Firefly/Genetic Algorithm (GA), in additionally to provide Real-time Detection of DoS attacks in IEEE 802.11p, using VFC for Intelligent Vehicular network. Vehicles are moving with certain speed and the data is transmitted at 30Mbps. Firefly FFBPNN (Feed forward back propagation neural network) has been used as a classifier to also distinguish between the attacked vehicles and the genuine vehicle. The proposed scheme has also been compared with Cuckoo/CSA ABC and Firefly GA by considering Jitter, Throughput and Prediction accuracy

Efficient Information Dissemination in Vehicular Networks with Privacy Protection

Vehicular ad hoc network (VANET) is a key component of intelligent transportation System (ITS). In VANETs, vehicles and roadside units exchange information for the purpose of navigation, safe driving, entertainment and so on. The high mobility of vehicles makes efficient and private communications in VANETs a big challenge. Improving the performance of information dissemination while protecting data privacy is studied in this research. Meet-Table based information dissemination method is first proposed, so as to improve the information dissemination, and to efficiently distribute information via utilizing roadside units, Cloud Computing, and Fog Computing. A clustering algorithm is proposed as well, to improve the stability for self-organized cluster-based dissemination in VANETs on highways. Then, fuzzy neural networks are used to improve the stability and security of routing protocols, AODV, and design a novel protocol, GSS-AODV. To further protect data privacy, a multi-antenna based information protection approach for vehicle-to-vehicle(V2V) communications is also proposed

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods