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Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

The design concepts of the in-robot network (IRN) architectures of humanoid robots are proposed in this paper. First, this paper reveals the network requirements for humanoid robots to realize perception abilities and action execution abilities near those of human beings. Humanoid robots need to be equipped with many sensors to collect surrounding environmental information. It is also necessary to use many motion actuators to enhance the degree of freedom to achieve the smooth motion abilities of humans. To maintain reliable data transmission between a number of nodes, an efficient and reliable IRN architecture is needed. This paper first discusses the limitations of existing humanoid robots in the number of sensors and degrees of freedom and points out that one of the reasons for this limitation is the lack of reliable network architectures. In-vehicle networks (IVNs) and various network technologies are used. These include time-sensitive networks and control area networks (CANs) to name a few. Additionally, heterogeneous network protocols are used. IRNs also include networks of sensors and actuators with different performance parameters such as bandwidth, delay, and transmission speed. IRNs may adopt design ideas similar to those of IVNs to satisfy the network requirements for humanoids. To accomplish this, three feasible IRN architectures are proposed and analyzed. Finally, to compare and analyze the three proposed IRN architectures, this paper uses OMNeT++ simulation software.

Edge computing is a promising alternative to cloud computing for offloading computationally heavy tasks from resource-constrained mobile user devices. Placed at the edge of the network, edge computing is particularly advantageous to delay-limited applications for having a short distance to end-users. However, when a mobile user moves away from the service coverage of the associated edge server, the advantage gradually vanishes, increasing response time. Although service migration has been studied to address this problem focusing on minimizing the service downtime, both zero-downtime and the amount of traffic generated as a result of migration need further study. In this paper, an optimal live migration for containerized edge computing service is studied. This paper presents three zero-downtime migration techniques based on state duplication and state reproduction techniques, and then, proposes an optimal migration technique selection algorithm that jointly minimizes the response time and network traffic during migration. For validation and performance comparison, the proposed migration techniques are implemented on off-the-shelf hardware with Linux operating system. The evaluation results showed that compared with a naive migration, the optimal approach reduced the response time and network load by at least 74.75% and 94.79%, respectively, under considered scenarios.

The exploitation of aerial base stations (A-BSs) in conjunction with terrestrial base stations (T-BSs) is envisioned as a promising solution to provide connectivity to devices and user-equipment (UE) in crowded situations (viz. in the sports event) and emergency situations (viz. in the disaster management). However, the use of A-BSs with existing terrestrial networks intensifies the inter-cell interference (ICI) to the devices and UEs, therefore leading to a degraded signal-to-interference-ratio (SIR). This paper addresses this issue by exploiting different radio access technology (RAT) (mmWave/microwave) for aerial and terrestrial networks. Indeed, the network connectivity is always a top priority for all applications. However, there are also some applications such as remote patient monitoring, and remote working, which requires both coverage and high data-rates. But, most of the existing research claims the trade-off between the coverage and the data-rate performance. Whereas this paper aims to improve coverage and rate simultaneously in an aerial-terrestrial networks by employing an optimal combination of mmWave and microwave RAT based on the proposed association strategy. The essential analysis of such an integrated network involves the evaluation of parameters based on the analytic model. Hence, this paper analytically obtains the coverage probability (CP) and average rate expressions for the proposed integrated aerial-terrestrial networks. The analysis is supported by probabilistic models-based simulations that agree closely with analytical results. The results claim that the proposed model leads to improved performance in terms of both CP and average rate. Also, the paper provides parametric analysis for CP and rate with A-BSs height and A-BSs density to enable its practical implementation in 5G/6G technologies

Small cells (SCs) are expected to be ultra-densely deployed in or close to the traffic hot-spots in the fifth generation (5G) mobile networks to provide wireless capacity cost-effectively. Traffic hot-spots change over time, which means SCs cannot be deployed in a one-off manner as macrocells normally do, rather they should be constructed in a staged process. Hence, mathematical models that capture the time-varying staged-construction process, are urgently needed for operators to effectively predict the construction period, but are currently lacking. In this paper, inspired by the foam bursting process-a natural phenomenon that can be observed in daily life such as hand-washing, we first propose a novel model that can predict the time-varying expectation and logarithmic variance of SC coverage areas. Then, we verify the model by real network deployment cases. Additionally, in order to extract parameters from historical base station deployment data, a parameter estimation algorithm is designed and verified. The findings of the paper reveal that mobile operators should construct ultra-dense SC networks in a staged manner like how larger foams split into smaller ones.

Buffer sizing for switching and routing devices is of significance for guaranteeing the Quality of Service (QoS) of critical services on the Internet of Things (IoT), continuously evolving scheduling mechanisms and complex traffic characteristics pose new challenges for the traditional method of static buffer sizing based on rule-of-thumb. In this paper, the scope of buffer sizing is extended from a basic scheduling system under homogeneous arrival traffic input to an integrated scheduling system under heterogeneous arrival traffic input which is more ubiquitous. In this context, Voices, videos and other heterogeneous data in the IoT are categorized into short-range-dependent (SRD) and long-range-dependent (LRD) traffic, and the integrated scheduling system is decomposed into single-server-single-queue (SSSQ) systems by not only decoupling the complex dependencies among heterogeneous traffic inputs but also taking the impact of SRD and LRD traffic burstiness on the buffer sizing into account. On this basis, expressions for the relation between the minimum buffer size and the maximum overflow probability are presented. The numerical analysis results and simulation analysis results reveal that the average arrival rate, traffic burst level and scheduling priority are positively correlated with the required buffer size, and once the overflow probability is set, the minimum buffer size can be determined correspondingly. The achievements of this paper will provide theoretical guidance for IoT manufacturers and technicians to set buffers more reasonably and use resources more efficiently.

The coupling of communication networks plays an indispensable role in modern power systems; however, it also leads to many problems due to cascading failure. Although most current research focuses on the single-layer, non-interacting system and physical layer network, few studies have combined the complex network theory to ignore the influence of communication networks. To address this problem, this paper introduces a cascading failure model of a cyber physical power system (CPPS) that considers the operation characteristics of the communication layer, especially the influence of transmission delay and connectivity on stability control. First, the influence mode of the communication layer failure node acting on the physical layer is analyzed, and the interaction mechanism between the communication layer and physical layer is clarified. Furthermore, the propagation process of failure in the CPPS system is modeled, and the influence of information transmission delay on the stability control process is considered. In this manner, the development process of cascading failure in a CPPS system is described accurately. Finally, according to the loss rate of the physical layer load, the influence of the communication layer node is evaluated to verify the accuracy of the model. This paper also provides a practical model of a CPPS system, which is helpful for building a secure and reliable energy Internet.

© Copyright 2021, A. O. Nyanteh et al. Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end–to–end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end- to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) is developed in this paper. To maximize the spectrum efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy efficient optimization problem based on coordinated scheduling is formulated, which is a mixed-integer nonlinear fractional programming problem and is intractable to solve directly. To tackle this, a two-step GA based scheme is proposed to solve the optimization problem. In the first step, the resource blocks matrix is solved by normal GA in the spectral efficiency aspect with fixed power distribution matrix, and then the power distribution matrix is obtained in the second step by non-dominated sorting genetic algorithm II (NSGA-II) with obtained resource blocks allocation matrix. Finally, the system level numerical evaluation process is provided to illustrate the effectiveness of the developed scheme.

Recently, application of network systems (e.g., cloud computing systems) is increasingly prevalent for achieving integration, sharing and efficient utilization of various resources. A parallel task in a network system has multiple subtasks that can be executed in different servers in parallel. However, failures of any subtask inevitably result in that the entire task cannot be complete. To avoid such a situation, the network system can create some copies from a subtask and make them run on different servers simultaneously. This redundant parallel execution manner is an efficient approach to improve performance and guarantee reliability. However, it also brings complexity in modeling, evaluation and optimization. For example, link failures inevitably lead to inaccessibility of some servers, and server failures also result in that subtasks hosted on the server cannot be complete. This is the complicated failure correlation that cannot be ignored in modeling and evaluation. This paper first presents a reliability-performance correlation model for a redundant parallel task in the network system. The model captures precedence constraints of subtasks, multiple types of failures and complicated failure correlations to improve fidelity. This paper also design an algorithm that encompasses the Graph theory and the Bayesian theorem to evaluate a performability metric, which can be used to quantify important reliability-performance correlation. Finally, a heuristic algorithm is designed to search an optimal task execution strategy that maximizes the performability metric. Illustrative examples are presented.

Due to the rising popularity of the Internet of Things (IoT), interconnected infrastructures with better flexibility and efficiency are being developed for industry 4.0. The use of IoT in industry 4.0 introduces a new term “Industrial IoT (IIoT)”, which allows remote monitoring of wide-ranging industrial processes. IIoT tends to function with a large number of heterogeneous devices, therefore, data interoperability is the biggest challenge in achieving seamless real-time communication. To achieve data interoperability, there is a need to integrate various IoT application layer protocols; separately, as well as, with other data acquisition protocols along with hardware/software platforms. Although such integrations are frequently presented in the state-of-the-art, a comprehensive investigation and analysis of such approaches in single research work are hard to find. Therefore, in this article, a Systematic Literature Review (SLR) is presented by investigating 34 influential research works, published during 2014-2020, where interoperability of application layer protocols is performed for IIoT. Consequently, the selected research studies are divided into three categories: 1) Integration of IoT application layer protocols— 13 papers; 2) Integration of IoT and data acquisition protocols—6 papers; 3) Integration of IoT protocols with hardware/software platforms—15 papers to resolve interoperability issues. Moreover, nineteen (19) tools used for integration in the selected research works are identified. Furthermore, leading approaches to perform such integrations are recognized, e.g., Gateways. It has been concluded that although researchers frequently propose centralized (e.g., gateway or protocol converter) and distributed intermediator (e.g., Middleware) to achieve required interoperability for IIoT, it is a need of a day to develop a device level solution without the involvement of intermediator. This will solve different interoperability problems in industries like scalability, load balancing, and a single point of failure.

Wireless sensor networking is being used extensively in agricultural activities to increase productivity and reduce losses in various ways. The greenhouse simplifies the concept of planting, which has several benefits in agriculture. In agricultural models, soil pH sensors and gas sensors are commonly used. These sensors are applicable in various Internet of Things (IoT) integrated agricultural activities. The paper discusses the hardware design and working of the proposed model. In addition, various agricultural models used for evapotranspiration are also explained. The key factors such as congestion control are evaluated using the Penman-Monteith equation. This paper focuses on implementing more than two references parameters like evapotranspiration and humidity under different conditions, which aids in splitting the relationship evenly by the number of sources. Furthermore, the paper shows the implementation done with MATLAB and values are adjusted using the code. The paper claims to achieve similar variations with the same source value, validating the proposed model’s efficiency and fairness. In an optimal region, these schemes also demonstrate higher throughput and lower delay rates. The improved packet propagation through the IoT network is demonstrated using visualization tools, and the feedback is computed to determine the overall access amount (A1 + A2) obtained. The experimental results show that the propagation rate is 1.24, more significant than the link capacity value. The claims are verified by showing the improved congestion control as it outperforms different parameters, considering an additive increase condition by 0.3% and multiplicative decrease condition by 1.2 %.

As the manufacturing industry gradually expands and tends to be intelligent, the industrial networks have been greatly applied and are of paramount importance. Furthermore, taking the advantages of wireless networks into account, such as flexible deployment, low cost, and easy maintenance, etc., this paper is dedicated to the research of industrial wireless networks. Facing the problem of limited radio resource, in order to make full use of frequency band, this paper proposes a resource scheduling scheme with puncturing, which guarantees the reliability of subsequent ultra-reliable and low-latency communications (URLLC) services under the conditions of existing enhanced mobile broadband (eMBB) services. By optimizing the bandwidth pre-allocation for eMBB services, puncture weight and transmit power, this scheme can effectively meet the needs of the system. Specifically, the purpose of this paper is to minimize the decoding error rate of the devices carrying URLLC service while ensuring the demand for throughput of eMBB services. The problem proposed is a nonlinear stochastic optimization problem involving complex Q-function. The closed-form expression of the objective function is obtained through linear approximation and probability theory, which is further decoupled into three sub-problems. To this end, a block coordinate descent optimization (BCDO) algorithm is proposed to obtain the optimal bandwidth allocation, puncture weight and transmit power. The simulation results verify the rationality of the theoretical analysis and the effectiveness of the proposed algorithm, and also elaborate the influence of different parameters on the decoding error rate of the URLLC devices.

Resource allocation has a direct and profound impact on the performance of resource-limited smart grids with diversified services that need to be timely processed. In this paper, we investigate a joint communication, computing, and caching resource allocation problem with distinct delay requirement of services in smart grids. This paper aims to optimize the long-term system utility based on reward and loss function. Considering the unknown dynamic environment as well as the huge state and action space in smart grids, a deep reinforcement learning algorithm based on the polling method is exploited to learn the policy by interacting with the environment. Specifically, the edge nodes (ENs) act as agents to enable the services to schedule resources appropriately. Then, the agents that are allocated based on the service requirements are queried according to the polling mechanism and the well-designed reward function is utilized to update the strategy. Extensive simulation results show that the proposed algorithm outperforms three known baseline schemes in terms of network performance with decision results. Besides, in the face of a large number of services in the smart grids, the proposed system still surpasses that of existing several baseline schemes, especially in the improvement of cache hit rate and the decrease of computing delay.

The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.

The proliferation of on-demand internet services delivered over a network of a heterogeneous set of computing devices has created the need for high-performing dynamic systems in real-time. Services such as audio and video streaming, self-driving cars, the Internet of things (IoT), or instant communication on social networks have forced system designers to ethink the architectures and tools for implementing computer systems. Reactive programming has been advocated as a programming paradigm suitable for implementing dynamic applications with complex and heterogeneous architectural needs. However, there is no consensus on the core set of features that a reactive framework must-have. Furthermore, the current set of features proposed in reactive tools seems very restricted to cope with the actual needs for concurrency and distribution in modern systems. In this paper, several alternative semantics for distributed reactive languages are investigated, addressing complex open issues such as glitch avoidance, explicit distribution support, and constructs for explicit time management. First, we propose a reactive event-based programming language with explicit support for distribution, concurrency, and explicit time manipulation (ReactiveXD). Second, we present a reactive event-based semantic framework called Distributed Reactive Rewriting Framework (DRRF). The framework uses rewriting logic to model the components of a distributed base application, observables, and observers, and predicates supporting explicit time manipulation. Finally, to validate the proposal, the paper discusses the specification of the semantics of ReactiveXD and a scenario describing a case of intrusion detection on IoT networks, La proliferación de servicios de Internet bajo demanda entregados a través de una red de una heterogeneidad conjunto de dispositivos informáticos ha creado la necesidad de sistemas dinámicos de alto rendimiento en tiempo real. Servicios como transmisión de audio y video, automóviles autónomos, Internet de las cosas (IoT) o comunicación instantánea en las redes sociales han obligado a los diseñadores de sistemas a repensar las arquitecturas y herramientas para implementar sistemas informáticos. La programación reactiva se ha defendido como un paradigma de programación adecuado para implementando aplicaciones dinámicas con necesidades arquitectónicas complejas y heterogéneas. Sin embargo, hay No hay consenso sobre el conjunto básico de características que debe tener un marco reactivo. Además, el conjunto actual de las características propuestas en las herramientas reactivas parece muy restringido para hacer frente a las necesidades reales de concurrencia y Distribución en sistemas modernos. En este artículo, varias semánticas alternativas para lenguajes reactivos distribuidos se investigan, abordando problemas abiertos complejos como la prevención de fallas, el soporte de distribución explícito y constructos para la gestión explícita del tiempo. Primero, proponemos un lenguaje de programación reactivo basado en eventos con soporte explícito para distribución, simultaneidad y manipulación explícita del tiempo (ReactiveXD). Segundo, presentamos un marco semántico reactivo basado en eventos llamado Distributed Reactive Rewriting Framework (DRRF). El marco utiliza la lógica de reescritura para modelar los componentes de una aplicación base distribuida, observables y observadores y predicados que apoyan la manipulación explícita del tiempo. Finalmente, para validar el propuesta, el documento analiza la especificación de la semántica de ReactiveXD y un escenario que describe un caso de detección de intrusiones en redes IoT, This work was supported in part by the Escuela Colombiana de Ingeniería Julio Garavito through the Project Diseño y Construcción de Herramientas Reactivas con Aplicaciones a Middleware Distribuido Para el Procesamiento de Grandes Volumenes de Datos, and in part by the Department of Applied Mathematics and Computer Science, Universidad del Rosario., Received April 27, 2020, accepted June 16, 2020, date of publication July 20, 2020, date of current version August 17, 2020.

With the rapid growth of application demands and the real-time change of environmental situations, the defects of the UAV task network in adaptability, flexibility, and resilience are becoming more and more prominent. The current network architecture that the junction of points and lines is fixed cannot dynamically provide capacity requirements in real-time due to the failure nodes encountered in the Unmanned Aerial Vehicle (UAV) task scheduling process. To address this challenging issue, this paper proposes a flexible network architecture supporting dynamic fault-tolerant task scheduling model (DSM-FNA) for the UAV cluster. To be specific this paper resorts to super network theory, combining the management theory of flexible network and resilience network to carry out the organizational calculation on the model, and also draw upon linear transformation function to weight and stratify the capability value according to the ability requirement required by the task. Then, a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed, and the substitution strategy is designed for the failure point, to realize the capability and dynamically adapt to the task. Finally, compared with the classical Max-Min algorithm and other algorithms, it is verified that the FDSA algorithm performs better dynamic adjustment for quick response in case of UAV cluster emergencies.

In the era of big data, the global data is growing explosively. The huge growth rate makes data processing and storage difficult, especially in the field of transportation. Based on the above background, this paper aims to study the autonomous coordinated control strategy for the complex process of traffic information physical fusion system based on big data. In this paper, the information physical fusion system is applied to the modern transportation system, and it is used to realize the high integration of computation, communication and control. Realize the independent and coordinated control of the transportation system. This paper proposes an autonomous traffic management mechanism based on multi-agent CPS system. In view of the instability and untimely of the original control strategy, a new traffic optimization control strategy conflict reduction control strategy is proposed. In order to solve the complexity of traffic system, the generation method of CPS autonomous control strategy based on multi-agent is studied and analyzed. Through the evaluation and verification of the conflict reduction control strategy and the online simulation of the incremental data synchronization strategy, it can be seen that the inconsistency ratio curves of message quantity and byte transmission quantity are always kept at a relatively low level, 1% and 2%, respectively. During the whole experiment, the average number of inconsistent messages and byte transmission of the agent are ideally controlled at 1.2 messages / train and 0.5kb/train.

With the popularization and development of the IoT(Internet of Things), more and more data needs to be transmitted over the Internet, which leads to the deterioration of network quality. The CDN (Content Distribution Network) technology is an important theoretical model to ensure network QoS (Quality of Service). To improve the QoS, we extend current CDN system to the hierarchical CDN system, that traditional CDN system is just a special case of hierarchical CDN system. In the traditional CDN system and the hierarchical CDN system, by analyzing the bandwidth between subsystems, we found the inter-system bandwidth is the bottleneck that impedes CDN system expansion. To address this problem, a new kind of distributed system architecture is proposed in this paper. This new architecture uses broadcast channels to distribute broadcast type data and still using bidirection uni-cast channels for other type of data, so we call the new architecture as CHCDN (Channel Heterogeneous CDN) in this paper. The new architecture is analyzed and compared with the traditional CDN system architecture and hierarchical CDN system architecture. Moreover, the experimental simulation result has shown that the CHCDN system features better in real-time cache updating and features with higher data transmission efficiency than the hierarchical CDN system, which indicating that the new architecture has great potential for being widely used in distributed computing.

Intelligent Transportation Systems (ITS) will become an essential part of every city in the near future. They should support various vehicle-to-everything (V2X) applications that improve road safety or even enable autonomous driving. Recently, the European Telecommunications Standards Institute (ETSI) introduced a multi-access (mobile) edge computing concept as a promising solution to satisfy the V2X delay and computational requirements. Based on this concept, the tasks generated by V2X applications can be offloaded to servers at the edge of the radio access network (RAN). There is a need for a task offloading algorithm that minimizes the ITS operator expenses connected with the servers deployment and maintenance while satisfying the requirements of the V2X applications. Most of the existing papers in the literature do not pay much attention to queuing delays at servers. In this paper, the queuing delays are analyzed by considering a general-type task computational time distribution. A non-linear optimization problem is formulated to minimize the ITS operator expenses subject to delays and computational resources constraints. The flexibility is also improved by considering that a delay constraint is satisfied with a given probability. To solve this problem, a method for linearization of the problem is proposed, and consequently, an algorithm based on Integer Linear Programming (ILP) is designed. A heuristic algorithm called Cost-effective Heuristic Algorithm for Task offloading (CHAT) is also introduced that provides close to optimal results and has much lower computational complexity than the ILP algorithm. The efficiency of the CHAT algorithm is studied in several scenarios in terms of the computational time, delays, and the total server energy consumption as the cost function. The results show that the CHAT algorithm satisfies the requirements of the V2X applications in all the considered scenarios and reduces the ITS operator expenses over twice compared with other algorithms proposed in the literature.

This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

Safety message dissemination in a vehicular communication network requires vehicle-to-vehicle (V2V) communication with low latency and high reliability. To fulfill the stringent requirements in terms of reliability associated with safety applications, a control information assisted three-step (CIAT) resource selection scheme based on cellular vehicle-to-everything (C-V2X) technology for future vehicular communication network is investigated in this paper. To get a mathematical understanding of the process and performance of the CIAT scheme, in this paper, we develop an effective analytical approach to characterize the properties of the CIAT resource selection scheme in vehicular communication network by using the tool of stochastic geometry. In particular, the resource selection process and the key performances are analyzed mathematically; The closed-form expressions for the key performances are derived, which include the resource exclusion ratio, the average successful transmission probability and the packet inter-reception time; Furthermore, the performances of CIAT resource selection scheme are compared with those of random resource selection scheme mathematically. All the analytical results in this paper are validated through extensive Monte Carlo simulations, and the results provide insights to proper parameter settings for future vehicular communication network.

Automated manufacturing systems (AMSs) consist of computer-controlled interconnected manufacturing components (MCs) that are used to transport and process different product types. Each product type requires a certain sequence of processing steps in different MCs. Hereby, multiple product types can share processing steps on the same MC and the paths of different products types can overlap. In this paper we consider the modeling of AMSs in the scope of supervisory control for discrete event systems (DES). On the one hand, a suitable AMS model must allow the representation of sequential and concurrent processing steps in MCs. On the other hand, such model must be able to track different product types traveling through the AMS so as to process the products correctly. While previous work is commonly concerned with the first requirement, this paper identifies that the existing literature lacks a general treatment of the second requirement. Accordingly, we first introduce order-preserving (OP) languages that preserve the order of different product types in MCs and we propose a suitable finite state automaton model for OP languages. Then, we show that the composition of OP languages again leads to an OP language. That is, modeling MCs by OP languages, an OP model of a complete AMS that is suitable for supervisory control is obtained. In addition, it is possible to use both OP models and non-OP models for general AMSs, where MCs have different properties. We demonstrate the applicability of the proposed modeling technique by a flexible manufacturing system example.

In the practice of cloud manufacturing, there still exist some major challenges, including: 1) cloud based big data analytics and decision-making cannot meet the requirements of many latency-sensitive applications on shop floors; 2) existing manufacturing systems lack enough reconfigurability, openness and evolvability to deal with shop-floor disturbances and market changes; and 3) big data from shop-floors and the Internet has not been effectively utilized to guide the optimization and upgrade of manufacturing systems. This paper proposes an open evolutionary architecture of the intelligent cloud manufacturing system with collaborative edge and cloud processing. Hierarchical gateways connecting and managing shop-floor things at the “edge” side are introduced to support latency-sensitive applications for real-time responses. Big data processed both at the gateways and in the cloud will be used to guide continuous improvement and evolution of edge-cloud systems for better performance. As software tools are becoming dominant as the “brain” of manufacturing control and decision-making, this paper also proposes a new mode - “AI-Mfg-Ops” (AI enabled Manufacturing Operations) with a supporting software defined framework, which can promote fast operation and upgrading of cloud manufacturing systems with smart monitoring-analysis-planning-execution in a closed loop. This research can contribute to the rapid response and efficient operation of cloud manufacturing systems.

This paper studies imperfect underlay device-to-device (D2D) association in spectrum-shared cellular networks. It addresses important system and design interference constraints, processing load limitations and transmit power constraints at D2D terminals. The paper proposes decentralized schemes for D2D communication between D2D terminals when downlink channel resources can be reused in the D2D network. The D2D transmitters are characterized considering their processing load limitation and allocated transmit power constraints. Moreover, the downlink channels that can be reused in the D2D network are quantified while meeting interference constraints imposed by the primary cellular network. Two schemes to identify reusable channels, which vary in terms of their efficiency, communication overhead requirement and implementation complexity, are described. Moreover, two D2D association schemes, namely the simultaneous and sequential D2D associations, are proposed and both aim to concurrently maximize the desired link quality and minimize the effect of interference effect at D2D receivers. Generalized analytical results that are applicable for various imperfect association scenarios are presented. The findings are applicable for any D2D channel models and performance metrics. They provide insights into various imperfect underlay D2D association scenarios under the practical system and design constraints.

Utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control.

Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution.

The node selecting problem of traffic network is a significant issue and is difficult to be solved. In this paper, an artificial slime mold method is proposed to help us solve the problem. First, the chief components of an artificial slime mold are introduced to simulate the foraging behavior of a true slime mold, including external food sources, plasmodium, myxamoeba, nucleus, and nutrients. Then the learning mechanism of nutrient concentration for the artificial slime mold is illustrated, though there is no brain or neuron in its body. After that, the node selecting approach is described according to the propagation capabilities of nodes. Second, the algorithm flow is designed to show how to solve this kind of complex selecting problem. The algorithm flow to select important traffic nodes by artificial slime mold is composed of 4 main steps, including initialization, food searching, feeding, and selecting for output. Third, a comprehensive example is designed and derived from references to certificate that the proposed artificial slime mold can help us select important traffic nodes by their generated traffic topologies. The contributions of this paper are important both for traffic node selecting and artificial learning mechanism in theoretical and practical aspects.

Petri nets, even though very useful for modeling of discrete event systems, suffer from some weaknesses such as huge size, huge state space, and slow in simulation. Due to the huge state space, model checking a Petri net is difficult. Also, due to the slowness in simulation, discrete-timed Petri nets cannot be used for real-time applications. Thus, modular Petri nets are suggested as a way of overcoming these difficulties. In modular Petri nets, modules are designed, developed, and run independently, and the modules communicate with each other via inter-modular connectors. This approach is suggested for developing newer Petri net models. However, there exists a large number of Petri net models of real-life systems, and these legacy models are enormous and non-modular. And, these models cannot be discarded as large amounts of time and money were spent to develop these models. This paper presents a unique algorithm for extracting modules from large and legacy Petri net models. The algorithm extracts modules (known as “Petri modules”) that are well-defined for inter-modular collaboration. Also, the extraction method preserves the structural properties. The goal of the paper is to introduce a methodology by which Petri nets can be moved to a new level in which a modular Petri net model can be made of Petri modules. The Petri modules are independent and can be hosted on different computers. These modules communicate via inter-modular components such as TCP/IP sockets. Since Petri modules are compact, also run faster, thus become suitable for supervisory control of real-time systems.

The Web of Things (WoT) standard recently promoted by the W3C constitutes a promising approach to devise interoperable IoT systems able to cope with the heterogeneity of software platforms and devices. The WoT architecture envisages interconnected IoT scenarios characterized by a multitude of Web Things (WTs) that interact according to well-defined software interfaces; at the same time, it assumes static allocations of WTs to hosting devices, and it does not cope with the intrinsic dynamicity of IoT environments in terms of time-varying network and computational loads. In this paper, we extend the WoT paradigm for cloud-edge continuum deployments, hence supporting dynamic orchestration and mobility of WTs among the available computational resources. Differently from state-of-art Mobile Edge Computing (MEC) approaches, we heavily exploit the W3C WoT, and specifically its capability to standardize the software interfaces of the WTs, in order to propose the concept of a Migratable WoT (M-WoT), in which WTs are seamlessly allocated to hosts according to their dynamic interactions. Three main contributions are proposed in this paper. First, we describe the architecture of the M-WoT framework, by focusing on the stateful migration of WTs and on the management of the WT handoff process. Second, we rigorously formulate the WT allocation as a multi-objective optimization problem, and propose a graph-based heuristic. Third, we describe a container-based implementation of M-WoT and a twofold evaluation, through which we assess the performance of the proposed migration policy in a distributed edge computing setup and in a real-world IoT monitoring scenario.

It has become a strategic consensus of the international community for accelerating the deployment of 5G network. This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem for the site selection and location of 5G macro and micro base stations. An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base stations. A series of numerical examples are solved in the paper to demonstrate the proposed approach, and a cost-benefit analysis is also conducted to determine the optimal deployment plan for the number of macro and micro base stations. In the conclusion, a balanced executable solution is presented to make the signal strength of all demand points in the studied 5G network reach the strongest under the budget constraint.

Quantum computing can provide a solution for many problems that classical communication networks encounter, but the knowledge gap between these two is not bridged yet. Accordingly, this paper has proposed directions, evaluations, and futuristic quantum solutions to cloud-based cellular communications. This paper shows the effect of using quantum entanglement phenomena upon the classical cloud network, particularly using an entanglement theory to decrease the enlarged signaling cost delay that the traditional cloud network faces. Through modeling the latency of both paradigms, this paper promises a delay reduction when adapting a quantum method into the mobile cloud networks. This paper also models the power consumption (PC) and energy efficiency of the traditional and quantum-based cloud networks. This paper also shows, via modeling the PC, that installing a quantum-based paradigm is not a power costly method; rather, it shows identical power and energy efficiency figures with a possibility of improvement.

The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

This paper focuses on the design of modular Robotic Mobile Fulfillment Systems. A Robotic Mobile Fulfillment System is an automated storage and retrieval system, in which mobile robots are deployed to deliver storage shelves for picking operation. This paper proposes a modular robotic system with aisle-captive robots for small and medium-sized logistics warehouses. Then analytical models, including a bottleneck-based model and an open queueing network model, are developed to estimate system throughput and average order flow time. Last, a two-stage design framework is proposed to rapidly identify an optimal system configuration. The main contributions are that: first, the proposed modular robotic system is free of traffic congestion and blocking; second, the comprehensive framework highlights some significant guidelines for warehouse developers during the “conceptualization” phase of system development. Simulation experiments indicate that the open queueing network model can provide accurate system performance estimation. The effectiveness of the proposed design framework is validated through practical application in real cases.