To meet rising demands for computing resources, information technology service providers need to select cloud-based services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learning-based virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.

Comparing with previous mobile communications, the fifth generation (5G) mobile communication provides three different types of services including enhanced mobile broadband, massive machine type communications, and ultra-reliable and low latency communications. To fulfil various requirements of these services, 5G defines new technologies and architectures such as the next-generation core network (NGC) and the new radio of the access network (5G-AN). The user plane function (UPF), which is an essential component in 5G NGC, connects 5G-AN and packet data networks (e.g. internet). Typically, the UPF provides the tunnelling, internet protocol/port translation, and forwarding functions. To provide flexibility and scalability deployment for 5G NGC, this study virtualises the UPF by using the Docker container. However, the virtualisation architecture degrades the performance of the UPF, and the performance of the UPF dominates the performance (e.g. throughput) of the 5G user-plane transmission. To provide high-performance packet processing, this study utilises Intel Data Plane Development Kit to develop the UPF, reduces the number of memory copy on header processing, and investigates the CPU core allocation methods for the UPF deployment. Based on the results of this study, the proposed UPF can provide the UPF functions and process the packets up to 40 Gbps on a x86-based platform.

As vehicle speed is fast and on board units' (OBUs) computing power, storage space, bandwidth and other resources are very limited, the efficiency of message transmission in vehicular ad hoc network is of great concern. Certificate-based aggregate signature scheme provides a way to combine numerous signatures into one short signature. The authors propose a certificate-based aggregate signature scheme for vehicular ad hoc networks. In this study, the authors prove that the proposed scheme is secure against forgery attack in the random oracle model and under the computational Diffie–Hellman problem. It is efficient and suitable for vehicular ad hoc networks.

In this study, the problem of grouping motion for a second-order multi-agent network with both matched disturbances and mismatched disturbances is investigated, and finite-time convergent algorithm of the dynamic system is proposed. By designing the disturbance observers to eliminate disturbances in the multi-agent systems, a compound communication protocol with active disturbance compensation is presented by means of the sliding-mode control. By applying finite-time stable theory and input-to-state stable theory, the finite-time group convergence of multi-agent systems with multiple disturbances is analysed, and group flocking can be achieved asymptotically with the cooperative controller. The numerical simulations demonstrate the validity of the results.

With the rapid emergence of the Internet of Things (IoT), the development of IoT architecture that facilitates network control and remote management is not only an interesting but also challenging research direction. Designing a systematic framework allowing to remotely (re)reprogram heterogeneous IoT devices requires stringent technical challenges on the interoperability, the scalability and the adaptability. Recently, software defined network (SDN)-based technologies have been considered as a dominant solution for the IoT network architecture. In this study, the authors firstly review current state-of-the-art approaches of SDN solutions and existing efforts on remote (re)programming IoT systems. The literature review figures out current leading-edge technologies on SDN-based solutions and over-the-air (OTA) reprogramming approaches in IoT systems as well as indicates the existing challenges that encourage conducting more and more appropriate solutions to fulfil the requirements of the future Internet. Then, a framework called SD-IoTR (software defined IoT reprogramming) is proposed. The proposed SD-IoTR framework is based on the SDN architecture and intensively focuses on OTA programming IoT systems. This framework is designed to deal with high demands for dynamic adaptability and scalability. Finally, two case studies are also presented as proofs-of-concept that convince the feasibility of applying the proposed framework to real-world deployments.

One of the challenging issues in wireless sensor networks (WSNs) is to provide secure communication as the channel used is wireless. User authentication is a mechanism that is used to provide secure communication. The authors analyse the security of a recent authentication scheme for WSNs and show that it is not resistant to smart card loss, insider, and denial of service attacks besides the user traceability problem. Here, they design an improved user authentication scheme using the chaotic maps for WSNs. The chaotic maps-based public key cryptosystem helps a scheme to consider a large integer as the secret key rather than a large prime number and makes it fast. They show its formal security analysis using the random oracle model. They also analyse its security informally to show that it is resistant to various known attacks. They show that their scheme offers more security features than the existing schemes.

Virtual machines migration is an essential feature of virtualisation technology which brings many advantages in cloud computing management process such as proactive fault tolerance, load balancing, and power management. There have been many virtual migration algorithms that have been developed but it is hard to find one that best suits all applications as well as ensures the desire of all stakeholders. The optimal virtual machines migration of cloud computing is usually NP-hard or NP-complete. In addition, determining when virtual machines migrate is a challenge because of the difficulty of fault detection in cloud computing. In this study, the authors propose a virtual machines migration game approach for multi-tier application in infrastructure-as-a-service cloud computing where they first proposed fault detection model based on Takagi–Sugeno fuzzy system with metric's physical machines and then optimal or near optimal migration solution is approximated based on Nash equilibrium. The results of experiment demonstrate the efficiency of their proposal. The proposed virtual machines migration algorithm has been benchmarked in order to highlight their strength and feasibility.

In recent years, hazardous e-mails arose, such as the e-mails infected with ‘viruses’ or ‘worms’ spreading destructive programs and the ‘Phishing Mails’ defrauding e-mail accounts of the users. The number of spams continue to grow. With the related problems of spam coming to be more severe, the spam topics have become significant in various research domains. The common filtering methods include black/white list, rule learning, and those based on text classification, such as Naïve Bayes, support vector machine, and boosting trees, multi-agent and genetic algorithm. Among these, the methods based on text classification are most widely applied. Moreover, some efficient methods were proposed to consider only the e-mail's header section, based on which both operating efficiency and classification efficiency could be improved. By applying machine learning technique and decision tree data mining algorithm C4.5, this study aims to propose an efficient spam filtering method with the following features: (i) proposing a two-phase filtering mechanism to scan mainly e-mail's header and auxiliary content. (ii) Reducing the problem of false positive. The experimental results show that the authors’ method has a considerably high accuracy rate of 98.76%. Compared with some other methods of using the same spam data sets or of deep learning-based, their method obviously has an excellent performance.

The usage of dedicated short-range communication, vehicular ad hoc network (VANET) significantly increases the security of stakeholders and improves the efficacy of the intelligent transportation system. It supports multifarious applications to make transportation convenient through routing of the messages. Changing the topological scenario, high acceleration of vehicles and intermittent communication result in the reduction of successful packet transmission and overhead. To overcome such constraints, a new routing protocol for the city environment named ant colony optimisation and intersection-based routing (ACO–IBR) is proposed. The key objective is to identify the necessity of intersection oriented routing and selection of the route as a task of optimisation. It uses a new metric called intersection rating and a greedy approach to forward a packet through the intersection to minimise the time required to discover the route. The efforts have been made to calculate intersection rating using three parameters such as the distance between vehicles, travel direction and the time taken to reach the intersection. The performance of ACO–IBR is determined by overhead, packet delivery ratio, delay and throughput. The results are compared with the standard protocols such as greedy perimeter stateless routing, intersection-based routing, AntHocNet and zone routing protocol. On the basis of extensive simulation, experimental results show that the ACO–IBR minimises routing overhead and improves the packet delivery ratio.

Sustainable operation of energy-restrained wireless network services requires multiple objectives to be satisfied synchronously. Among these objectives, reduced spectrum outage, energy conservation, and minimal packet transmission failures considerably affect the energy harvesting operation of these networks. These three objectives are associated with disparate protocol layers incorporating the transport, medium access control, and physical layers of traditional networking architecture. The authors investigate energy harvesting wireless communications by formulating the multi-objective optimisation problem comprising these global networking criteria, which are simultaneously optimised with the heuristic design procedure. For this, they employ a Pareto-based evolutionary genetic algorithm technique built in the wireless network design and operation to find the optimal set of all non-dominated solutions traversing the entire design search space. Besides, iterative implementation of the presented genetic optimisation model with distinct feasible integrations of crossover and mutation operations is performed to evaluate the proficiency of the proposed scheme for evaluating the Pareto-optimal frontier set. The influence of different combinations of these operations is examined and adaptively applied with appropriate genetic parameters tuning for efficient meta-heuristic search through the candidate solution space. Simulation results demonstrate that the proposed hybrid genetic mechanism outperforms the existing methods in terms of throughput, energy efficiency, and loss rate.

Recent developments in networking include the interest in software-defined networking, in which OpenFlow switches are in charge of forwarding traffic by matching incoming packets with forwarding rules in their flow tables. Flow tables are commonly implemented in ternary content addressable memories (TCAMs). Typically, this type of memory tends to be extremely expensive and to be power-hungry devices. The price and power dissipation of TCAM depend on the capacity of the memory. A blind design of memory size can result in dramatic degradation in performance and power efficiency of the entire network. In this study, the authors clarify this impact and show how the optimum value of TCAM capacity is attainable. They also propose a power consumption model for TCAM-based OpenFlow switches. Simulation results show that the proposed solution reduces the average power consumption by 24% compared to the minimum link on method and by 11.5% compared to the minimum node on (MNO) method and thus power efficiency is better achieved when each memory is supplied by appropriate TCAM size.

The opportunistic network has become an appealing paradigm since it can reduce the energy consumption and cost on cellular network connections and relieve the communication burden of cellular networks. The probabilistic transmission scheme has been proposed as one of the typical opportunistic transmission schemes. However, most existing probabilistic transmission schemes neglect that, opportunistic transmission tasks in some applications have transmission time and cost budgets, and to set proper delay and hop count thresholds on encountering probabilities between relay nodes and their destination nodes can help to filter redundant long-delay and high-cost opportunistic paths for packets. In this study, the authors proposed a delay-aware and cost-efficient probabilistic transmission method for opportunistic networks. In this method, direct and indirect encountering probabilities related to delay and hop count are defined, and accordingly, a relay node selection algorithm is provided with preset encountering delay and hop count thresholds to compare probabilities of nodes to encounter the destination node within the preset thresholds as their transmission quality metrics. The simulation results verify that the proposed method can reduce transmission delay and cost efficiently.