This paper introduces a novel dynamic multipath routing, modulation level, spatial and spectrum assignment algorithm for space division multiplexing (SDM) enabled elastic optical networks (EON) with the aim of minimizing the blocking probability and the energy consumed by bandwidth variable transponders (BVTs). The adopted multipath routing strategy allows the splitting of the demand into several sublightpaths using different fiber cores but ensuring that all of them utilize the same set of fibers in order to avoid differential delay. The method also imposes continuity constraints in both spectrum and core location in order to use cost-effective SDM Reconfigurable Optical Add and Drop Multiplexers (ROADMs) without lane change support. The complete usage of multi-core fibers (MCFs) in this kind of networks is restricted due to inter-core crosstalk (XT), which can reduce the quality of received signals. Therefore, the method besides using the most effective modulation format, also ensures that the XT of the lightpaths (or sublightpaths) does not exceed the threshold for each modulation format. A simulation study comparing our method with another similar proposal from the literature is presented for different types of topologies in terms of link distances. Simulation results demonstrate that the proposed multipath routing algorithm in networks including links close to or beyond 1000 kms significantly boost the dynamic performance in terms of blocking probability, energy consumption, and latency.

During the development of software systems, even with advanced planning, problems with quality and security occur. These defects may result in threats to program development and maintenance. Therefore, to control and minimise these defects, machine learning can be used to improve the quality and security of software systems. This special issue focuses on recent advances in architecture, algorithms, optimisation, and models for machine learning applied to quality and security in software systems. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 4 manuscripts. A summary of these accepted papers is outlined below. In the first paper entitled “Robust Malware Identification via Deep Temporal Convolutional Network with Symmetric Cross Entropy Learning” by Sun et al., the authors propose a robust Malware identification method using the temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface (API) function names in many cases. Here, considering the numerous unlabelled samples in practical intelligent environments, the authors pre-train the TCN model on an unlabelled set using a word embedding method, that is, word2vec. In the experiments, the proposed method is compared to several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real-world dataset. The performance comparisons demonstrate the better performance and noise robustness of the proposed method, that the proposed method can yield the best identification accuracy of 98.75% in real-world scenarios. In the second paper entitled “Just-In-Time Defect Prediction Enhanced by the Joint Method of Line Label Fusion and File Filtering” by Zhang et al., the authors propose a Just-in-Time defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF). First, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Second, to obtain semantics-enhanced code representation, the authors propose a cross-attention-based line label fusion method to perform complementary feature enhancement. Third, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF predicts defective software changes more effectively. In the third paper entitled “Android Malware Detection via Efficient API Call Sequences Extraction and Machine Learning Classifiers” by Wang et al., the authors propose a novel Android malware detection framework, where the authors contribute an efficient API call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, the authors propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids unnecessary repetitive path searching. The authors also propose a pruning search, which further reduces the number of paths to be searched. The developed algorithm greatly reduces the time complexity. The authors generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real-world APKs, and the results demonstrate that the proposed method reduces the running time and produces high detection accuracy. In the fourth paper entitled “Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction” by Zheng et al., the authors propose H-BRP, a Hybrid Blockchain Reliability Prediction model, to extract the blockchain reliability factors and then make the personalised prediction for each user. Connecting to unreliable blockchain peers is prone to resource waste and even loss of cryptocurrencies by repeated transactions. The proposed model primarily aims to select reliable blockchain peers and to evaluate and predict their reliability. Comprehensive experiments conducted on 100 blockchain requesters and 200 blockchain peers demonstrate the effectiveness of the proposed H-BRP model. Furthermore, the implementation and dataset of 2,000,000 test cases are released. The Guest Editors would like to express their deep gratitude to all the authors who have submitted their valuable contributions, and to the numerous and highly qualified anonymous reviewers. We think that the selected contributions, which represent the current state of the art in the field, will be of great interest to the community. We also would like to thank the IET Software publication staff members for their continuous support and dedication. We particularly appreciate the relentless support and encouragement granted to us by Prof. Hana Chockler, the Editor-in-Chief of IET Software. Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a Professor at the College of Future Industry, Gachon University, Korea. His research interests include Software Intelligence, Cloud/Edge Computing, and AI4Healthcare. He has publications in IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TMM, IEEE TSC, IEEE TCC, IEEE TFS, IEEE TNSE, IEEE TNSM, IEEE TCCN, IEEE TGCN, IEEE TCSS, IEEE TETCI, IEEE TCE, IEEE/ACM TCBB etc. He has broad working experience in cooperative industry-university-research. He is a European Union Institutions-appointed external expert for reviewing and monitoring EU Project, is a member of the EPSRC Peer Review Associate College for UK Research and Innovation in the UK, and a founding member of the IEEE Computer Society Smart Manufacturing Standards Committee. Prof. Gao is a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the British Computer Society (BCS), and a Member of the European Academy of Sciences and Arts (EASA). Dr. Walayat Hussain is a Visiting Fellow at the School of Computer Science. Currently he is a Senior Lecturer and the Head of Discipline-IT at the Australian Catholic University, Australia. He served as a Lecturer and Postdoctoral Research Fellow at the Victoria University, Melbourne, School of Information, Systems and Modelling, University of Technology Sydney Australia for several years. Prior to joining UTS, he worked as an Assistant Professor and the Postgraduate program coordinator at BUITEMS University for many years. Walayat's research areas are Distributed Systems, AI, Information Systems, Computational Intelligence, Machine Learning, Business Intelligence, Decision Support Systems, and Usability Engineering. His work has been published in different top-ranked reputable ERA-A*, A, Q1 journals and conferences such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Service Computing, Future Generation Computer Systems, Information Sciences, International Journal of Intelligent Systems, Information Systems, Journal of Ambient Intelligence and Humanized Computing, Neural Computing and Applications, The Computer Journal (Oxford University Press), Computer & Industrial Engineering, IEEE Access, ACM TOMM, IEEE TGCN, IEEE TETCI, International Journal of Communication Systems, Mobile Networks and Applications, GJFSM, FUZZ-IEEE, ICONIP, and many others. Ramón J. Durán Barroso received the degree in telecommunication engineering and the Ph.D. degree from the University of Valladolid, Spain, in 2002 and 2008 respectively. He currently works as an Associate Professor with the University of Valladolid. He is also the Coordinator of the Spanish Research Thematic Network “Go2Edge: Engineering Future Secure Edge Computing Networks, Systems and Services” composed of 15 entities and the H2020 IoTalentum Project. He has authored more than 150 papers in international journals and conferences. His current research interests include the use of artificial intelligence techniques for the design, optimisation, and operation of future heterogeneous networks, multi-access edge computing, and network function virtualisation. Dr. Junaid Arshad has 14 years of research experience and expertise in investigating and addressing cybersecurity challenges for diverse computing paradigms such as Grid computing, Cloud computing, IoT, and blockchain. He is actively engaged in cutting-edge R&D distributed ledger technologies including blockchains, Tangle and Hashgraphs, investigating novel challenges to improve state of the art for such technologies as well as their use to solve real-world challenges. Junaid is an alumnus of the Innovate UK & DCMS funded CyberASAP programme, commercially prototyping the CyMonD system for effective monitoring and defence of IoT-based systems against cyber-threats. Junaid has successfully achieved research funding from UK and overseas funding agencies, and has worked as a security specialist for a number of EU funded projects with experience of developing bespoke security solutions. He is also actively involved in research surrounding analysis of malware for mobile and IoT devices focusing on profiling malicious behavior to achieve runtime detection and defense. Junaid has successfully published high quality research within cybersecurity and has more than 50 publications at high quality venues including journals, book chapters, conferences and workshops. He is an Associate Editor for the Cluster Computing and IEEE Access journals and regularly serves on program and review committees of several journals and conferences. Yuyu Yin received the Ph.D. degree in computer science from Zhejiang University in 2010. He is currently a Professor with the College of Computer, Hangzhou Dianzi University, Hangzhou, China. He is also a Supervisor of master’s students with the School of Computer Engineering and Science, Shanghai University, Shanghai, China. He has authored or coauthored more than 40 articles in journals and refereed conferences, such as Sensors, Entropy, IJSEKE, Mobile Information Systems, ICWS, and SEKE. His research interests include service computing, cloud computing, and business process management. Dr. Yin is also a member of the China Computer Federation (CCF) and the CCF Service Computing Technical Committee. He has organised more than ten international conferences and workshops, such as FMSC 2011–2017 and DISA 2012 and 2017–2018. He has served as a Guest Editor for the Journal of Information Science and Engineering and International Journal of Software Engineering and Knowledge Engineering and a Reviewer for the IEEE Transaction on Industry Informatics, Journal of Database Management, and Future Generation Computer Systems.

In recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.

In Recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence has brought many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. This special issue aims to prompt Deep Transfer Learning techniques in Multi-modal Biomedical Computing. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 21 high-quality manuscripts. A summary is outlined below.

Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.

Mobile technologies have undergone a great leap forward in a few years, and while 5G networks are already being deployed, there are not yet many proven applications that can fully utilize the advantages of this new technology. Connected and autonomous vehicles are a specific and demanding case, particularly in terms of delay and bandwidth requirements, which can leverage not only 5G but also edge computing technologies. Therefore, the development of testbeds to demonstrate future applications is crucial to enable the full deployment of 5G and edge computing possibilities. In this paper, we present a flexible and modular testbed, targeted towards the evaluation of Cooperative, Connected, and Automated Mobility (CCAM) applications, and we demonstrate a use case (using a 4G system) where an autonomous vehicle offloads processing tasks to an edge server which analyzes images, makes routing decisions, and sends guidance commands back to the vehicle, thus proving the possibilities of edge computing and wireless technologies.

With the rapid development of internet-of-things (IoT) and communication technologies, the quality of our daily life has improved with the applications of smart communications and networking, such as intelligent transportation, mobile computing, and edge computing. How to enable such a smart life has become a popular research topic. 5G-powered communication supports massive data transmission ensuring mobile users to have high-quality experiences, which bridges the gap between IoT and cloud computing. For example, connected 4K cameras can handle object tracking using functions in the cloud computing platform via the support of smart communications. Moreover, in smart communications and networking, we can collect, measure and analyse vast volumes of data using the technologies of artificial intelligence and big data. Such an advantage will bring tremendous opportunities for smart cities, such as unmanned vehicles and smart transportation. However, there are also many issues to resolve as we need to study architecture, applications, and future challenges within smart communications and networking. We accepted 19 papers for publication in this special issue after peer review. These selected papers are categorised into three topics: Topic A (Optimisation in smart communications), Topic B (Networks security and optimisation) and Topic C (Smart technology in IoT). The summary of each topic is given below. Wu et al., in their paper ‘Completion time minimisation for UAV enabled data collection with communication link constrained’, study the completion time minimisation problem under the communication link contained for data collection via designing the safe flight trajectory of the UAV in a complex environment. The authors first transform the original problem to a TSP-like problem based on the hover point, which can satisfy the link constraints of data collection. Then A* algorithm and SCA algorithm are used to construct the adjacency matrix and, respectively, the classical DP is used to solve the TSP-like problem. Besides, the slack variables are introduced and the successive convex approximation is leveraged to reformulate the communication link constraint, obstacle avoidance constraints, and discrete region threat avoidance constraints. Compared with the TSP-like problem with hovering, a continuously flying UAV usually has less time to perform a mission. The simulation results are presented to verify the proposed two-path planning algorithms under various parameter configurations. Zhou et al., in their paper ‘User-centric data communication service strategy for 5G vehicular networks’, develop a UCDCS strategy and the ARSUGs are updated in real-time according to predictions of vehicle mobility. Then, after the vehicle sends out a data communication request, the network comprehensively considers the RSU load cost, throughput cost, and vehicle income to flexibly allocate vehicle service resources via ARSUGs. Finally, in the allocated ARSUG, the network sorts and scores the ARSU in the ARSUG according to the communication service preferences of different vehicles and assists the vehicles to select the best ARSU for data transmission. The simulation results show that, compared with the traditional IMM, NCNS, and THOM strategies, the downlink transmission rate, link reliability, and network delay of the UCDCS strategy are 47.74%, 0.21%, and 5.96% higher, respectively. The experimental results verify that the strategy proposed in this paper can achieve better network load balancing than previous strategies. Hu et al., in their paper ‘Orthogonal frequency division multiplexing with cascade index modulation’, propose a novel orthogonal frequency division multiplexing with cascade index modulation (OFDM-CIM), which combines the conventional IM with the multiple-mode IM together, to increase the proportion of the index bits in the transmission. Subcarrier-wise and sub-block-wise cascade IM schemes are proposed to achieve different spectral efficiency and diversity order for diverse scenarios in the next-generation wireless communications. The optimal maximum likelihood (ML) detector is proposed for OFDM-CIM. To reduce the demodulation complexity, a novel tree search-based detector and a log-likelihood ratio (LLR) based low complexity detector, which can avoid the illegal indices patterns in the searching process, are proposed for OFDM-CIM. Monte Carlo simulations show that the proposed scheme achieves better BER performance than OFDM-IM. Tong et al., in their paper ‘Low pilot overhead channel estimation for CP-OFDM-based massive MIMO OTFS system’, first analyse the CP-OFDM-based massive MIMO OTFS system channel with antenna directivity pattern and transform the burst sparsity in the angle domain into block sparsity by using non-uniform Fourier Transform (NUFT). Furthermore, according to the general sparsity in the delay domain, the block sparsity in the Doppler domain, and the angle domain, a three-dimensional dynamic support search (DSD) algorithm is proposed. Compared with the traditional OMP algorithm and the 3DSOMP algorithm, simulation results demonstrate the proposed DSD algorithm has higher channel estimation accuracy and lower pilot overhead. Gao et al., in their paper ‘Low drift visual inertial odometry with UWB aided for indoor localisation’, propose a low drift visual inertial odometry with ultra-wideband (UWB) aided for indoor localisation. First, a single UWB anchor was dropped in an unknown position, and a cost function was formed by the position information and the UWB ranging information to obtain the position of the anchor. Then, the single anchor position and the UWB ranging constraints were added to the tightly coupled visual inertial fusion algorithm framework, thereby improving the robustness of motion tracking and reducing the drift of the odometry. Finally, the effectiveness of the proposed method was verified in the actual indoor environment, and the experiment results demonstrated that, compared with state-of-the-art localisation methods, the positioning accuracy and robustness were improved significantly. Wang et al., in their paper ‘An approach to adaptive filtering with variable step size based on geometric algebra’, propose the novel approach to adaptive filtering with variable step size based on Sigmoid function and geometric algebra (GA). First, the proposed approach to adaptive filtering with variable step size based on geometric algebra represents the multi-dimensional signal as a GA multi-vector for the vectorisation process. Second, the proposed approach to adaptive filtering with variable step size based on geometric algebra solves the contradiction between the steady-state error and the convergence rate by establishing a nonlinear function relationship between the step size and the error signal. Finally, the experimental results demonstrate that the proposed approach to adaptive filtering with variable step size based on geometric algebra achieves better performance than that of the existing adaptive filtering algorithms. Zheng, et al., in their paper ‘Unequal Error Protection Transmission for Federated Learning’, design an unequal error protection (UEP) scheme based on multi-rate channel coding and multi-layer modulation. The numerical simulation verifies that the proposed UEP transmission schemes have significant benefits in accuracy, robustness and efficiency, especially when the channel condition is poor. Zhang et al., in their paper ‘Secrecy outage probability analysis of energy-aware relay selection for energy-harvesting cooperative systems’, employ relay selection to improve the physical-layer security for a CCR-EH system consisting of a CS, multiple CRs and a CD in the face of an E. To prevent confidential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In the ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed-form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. The classical round-robin relay selection (RRRS) is also analysed in terms of secrecy outage probability. Finally, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability. Wang et al., in their paper ‘Applying an auction optimisation algorithm to mobile edge computing for security’, study the mobile blockchain network based on edge computing and propose a new assumption regarding the mobile communication blockchain based on the traditional blockchain. By analysing attacks on the mobile blockchain, a security model based on edge computing is designed, and the smart contract in the blockchain is combined with a court trial. In the algorithm optimisation process, a price utility function is constructed based on maximising social welfare, and both models are used as joint optimisation indexes. The profit of the provider is guaranteed, which is conducive to the development of the blockchain. Simulation results verify that system security increases with the blocked funds and duration, and the forking attack success rate approaches zero as the number of validators increases. Zhang et al., in their paper ‘A PUF-based lightweight authentication and key agreement protocol for smart UAV networks’, propose a two-stage lightweight identity authentication and key agreement protocol for UAV. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physically capture attack. Moreover, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, real-or-random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost. The results show that the protocol has effectiveness and practicality. Akhunzada et al., in their paper ‘MalDroid: Secure DL-enabled intelligent malware detection framework’, present a secure by design efficient and intelligent Android detection framework against prevalent, sophisticated and persistent malware threats and attacks. A novel and highly proficient CUDA-enabled multi-class malware threat detection and identification deep learning (DL)-driven mechanism that leverages ConvLSTM2D and CNN is proposed. The devised approach is extensively evaluated on publicly available state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Standard and extended assessment metrics are employed to thoroughly evaluate the proposed technique. Moreover, the performance of the proposed algorithm is verified both with the constructed hybrid DL-driven algorithms and current benchmarks. Additionally, to explicitly show unbiased results, the proposed scheme is validated. Shang et al., in their paper ‘An efficient MAC protocol design for adaptive compressed sensing based underwater WSNs’, design an adaptive compressive sensing-based MAC protocol to optimise energy efficiency and bandwidth utilisation. In the feedback UWSN structure, based on the adaptive compressive sensing method, a TDMA mechanism is designed to collect data from both the compressive sensor nodes and non-compressive sensing nodes in the UWSN. Super-frame-based MAC protocol is designed to minimise the energy consumption per bit according to the designed UWSN. An optimisation problem is to solve the parameters of the super-frame to satisfy both data latency and recovery quality requests. Considering the compression sensing method and packets loss, the slot allocation algorithm is designed to maximise bandwidth utilisation. Simulations show that the proposed method performs better than most of the state-of-art protocols and also a testbed is built up to show that the battery life can be prolonged by 11%. Liu, et al., in their paper ‘Reliability Modelling and Optimization for Microservicebased Cloud Application Using Multi-agent System’, proposes a scheduling scheme of Multi-agent system to optimize the reliability of cloud applications through flexible resource combination. The reliability optimization (PCPRO) algorithm based on partial critical path is introduced. Experiments on scientific workflow verify the effectiveness of the proposed algorithm. Ai et al., in their paper ‘Anti-collision algorithm based on slotted random regressive-style binary search tree in RFID technology’, propose an anti-collision algorithm based on slotted random regressive-style binary search tree (SR-RBST). Based on slotted ALOHA (SA), the method proposed in this paper uses the regressive-style binary search tree (RBST) to process the RFID labels in the collision time slot. With the same size of tags, the SR-RBST algorithm needs less total time slot and has higher efficiency and shorter identification time, while with the increase of the number of tags, the SR-RBST anti-collision algorithm has more obvious advantages. The SR-RBST algorithm effectively improves the time slot utilisation efficiency of the system. Siddiqi et al., in their paper ‘FANET: Smart city mobility off to a flying start with self-organised drone-based networks’, propose a reliable RTA monitoring scheme using enhanced ant colony optimisation (eACO) technique based on self-organised drone FANETs. The proposed scheme addressed several challenges including coverage of larger geographical areas and data communication links between FANETs nodes. The experiment results are presented to compare the proposed technique against different network lifetimes and the number of received packets. The presented results show that the proposed techniques perform better compared to other state-of-the-art techniques. Guo et al., in their paper ‘Payoff-maximisation-based adaptive hierarchical wireless charging algorithm for the mobile charger in IoT’, propose a payoff-maximisation-based adaptive hierarchical wireless charging algorithm for the mobile charger. According to energy allocation, anchor point deployment, and time allocation, decomposing it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimised in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by the innovative gain recall mechanism. The trade-off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharge time in a fixed cycle, and mobile chargers can always work in efficient recharging positions, whose effect is exploited at the utmost. Wang et al., in their paper ‘Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods’, propose an offloading scheme combining mobile edge computing (MEC) and deep reinforcement learning (DRL). First, a realistic map is simulated, while initialising the tasks queue, and building a task offloading environment with the base station (BS), roadside units (RSUs), and idle vehicles. Then, an algorithm that combines deep learning with reinforcement learning, i.e., the deep Q-learning network (DQN) algorithm, is developed to optimise the offloading scheme, to further reduce the offload latency. Finally, given that the complete information cannot be observed effectively in the environment, the long short term memory (LSTM) model is applied to train neural networks within DQN to improve its learning efficiency, in consideration of the satisfactory performance of LSTM in processing time-series data. The simulation results show that the MEC-based vehicle task offloading can effectively reduce the latency of vehicle offloading. Hou et al., in their paper ‘A data-driven method to predict service level for call centers’, investigate how to use the data-driven method to solve the service level prediction problem. To solve this problem, the relationship between service level and other factors, such as number of calls, number of agents, and time is explored. To model the relationship between service level and input features, some features based on empirical analyses are extracted and proposed to use decision tree-based ensemble methods, like random forest and GBDT. The experiment results show that the proposed method outperforms other baselines significantly. Wang, et al., in their paper ‘Short-term Passenger Flow Forecasting Using CEEMDAN meshed CNN-LSTM-Attention Model Under Wireless Sensor Network’, propose a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention-based CNN-LSTM network to extract both temporal and spatial characteristics of passenger flow data. By adding the attention mechanism, the problem of insufficient peak value prediction can be solved effectively. The experiment result shows that the CEEMDAN-ConvLSTM-Attention model has a significant performance improvement than the existing network models. All of the papers selected for this special issue show the development of different emerging technologies and creative strategies in various fields. However, there are still many challenges in all of those fields that require future research attention. The authors have no conflict of interest to disclose. Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a Professor at Gachon University, South Korea. Prior to that, he was a research fellow with the Software Engineering Information Technology Institute at Central Michigan University, USA, and was an adjunct professor at Hangzhou Dianzi University, China. His research interests include software formal verification, industrial IoT networks, vehicle communication, and intelligent medical image processing. He has publications in IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TSC, IEEE TNSE, IEEE TNSM, IEEE TCCN, IEEETGCN, IEEE TCSS, IEEE TETCI, IEEE/ACM TCBB, IEEE IoT-J, IEEE JBHI, IEEE Network, ACM TOIT, ACM TOMM, ACM TOSN, ACM TMIS. He is the recipient of the Best Paper Award at IEEE TII 2020 and EAI CollaborateCom 2020. Xiong Luo currently works at the Department of Computer Science and Technology, University of Science and Technology Beijing. His main research interests include data mining and machine learning, complex system modelling and computational intelligence, cognitive neural networks, intelligent optimal control, Internet of Things applications. He is a senior member of IEEE, a senior member of The Chinese Computer Society, a member of the Intelligent Automation Committee of the Chinese Association of Automation, a deputy secretary general of the Intelligent Medical Committee of the Chinese Association for Artificial Intelligence, and a member of the Cognitive System and Information Processing Committee of the Chinese Association for Artificial Intelligence. Ramón J. Durán Barroso received ‘a telecommunication’ engineer degree in 2002 and obtained his PhD in 2008, both from Universidad de Valladolid (UVa), Spain. From 2002 to 2010, he was an Assistant Professor at Universidad de Valladolid, Spain. From 2010 to the present, he was an associate professor at Universidad de Valladolid, Spain. From 2004, he focused on communication networks, in particular in the following topics: design and optimisation of wavelength routed optical networks, hybrid optical networks (proposing polymorphic networks), cognitive heterogeneous optical networks (proposing CHRON networks) and access optical networks. He has also actively researched the use of ICT technologies in education. Moreover, he has actively collaborated with the other line of his research group devoted to research about wireless communications and location techniques using some methodologies previously used in his research in network optimisation. Walayat Hussain received a PhD from the University of Technology Sydney, Australia. Currently, he is serving as a lecturer (assistant professor) at Victoria University, Melbourne, Australia. Before joining Victoria University, he worked for six years as a lecturer and research ‘fellow’ at the FEIT, University of Technology Sydney, Australia. He has served as an assistant professor at BUITEMS University for many years. He has published in various top-ranked ERA-A*, JCR/SJR Q1 journals such as The Computer Journal, Info. Systems, Info. Sciences, IJIS, FGCS, IEEE Access, Comput & Ind Eng, MONET, Journal of AIHC, IEEE TETCI, IEEE TSC, IEEE TGCN, IJCS and WCMC. He has served as a guest editor in various Q1 journals. He has won multiple national and international research awards and recognitions. He is the recipient of the Best Paper Award at 3PGCIC 2015, 2016 Poland, South Korea, Ministry of Higher Education Govt. of Oman and FEIT HDR Publication Award by the UTS Australia. Guest Editorial.

A novel quality of transmission (QoT) estimator based on support vector machines (SVM) is proposed for classifying optical connections (lightpaths) into high or low quality categories in impairment-aware wavelength-routed optical networks (WRONs). The performance of the SVM-based estimator is evaluated in a long haul communications network and compared to previous semi-analytical and cognitive proposals. Results show that the SVM approach significantly reduces the necessary computing time to estimate the QoT of a given lightpath, critical aspect of design in these networks, and even slightly improves accuracy.

An emulation study has been carried to demonstrate the benefit of a proactive restoration technique in cognitive heterogeneous optical networks. Results show the advantages of that method in terms of recovery percentage and disruption time.

Communication networks are facing continuous variations of traffic patterns as well as occasional failures of network equipment. Wavelength-routed optical networks (WRONs) offer the possibility of dynamically adapting to traffic conditions by means of reconfiguring the logical topology, that is, the set of lightpaths embedded in it. However, reconfiguration has a cost, the number of packets lost during the reconfiguration process. Hence, it is necessary to use efficient reconfiguration policies and algorithms to design the logical topologies. In this paper, a new algorithm is proposed to design logical topologies that jointly minimizes the number of lightpaths changed (reconfiguration cost) and the network congestion (reconfiguration reward). This method is based on the combination of genetic algorithms with Pareto optimality techniques. Thus, the algorithm provides a set of optimal (or near-optimal) solutions in terms of both parameters, the Pareto optimal set. Moreover, a novel policy to minimize the packet loss ratio considering all the solutions provided by the algorithm is also proposed. A simulation study is presented to show how the combination of the new algorithm and policy can reduce in more than one order of magnitude the packet loss ratio in stationary state and respond to abrupt changes in less time when compared with previous work on logical topology reconfiguration.