Heterogeneous Communication Networks Research Articles

FedStar: Efficient Federated Learning on Heterogeneous Communication Networks

FedStar: Efficient Federated Learning on Heterogeneous Communication Networks

Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels

Aiming at the communication network optimization problem of the Internet of Inland Vessels, this work presented a network model and deployment strategy with shore-based cooperative units as network nodes. Firstly, the system architecture and communication mode of the Internet of Inland Vessels were analyzed. The three-layer model of service, data, and transmission of ship–shore communication was established to calculate the ship–shore communication data volume of the system. Then, considering the comprehensiveness of the signal coverage of the base station, a coverage model of two-layer heterogeneous network communication was established. Furthermore, an optimization model of shore-based cooperative unit deployment was established with power consumption, cost, and data transmission rate as the objectives. The multi-objective optimization model was solved by the genetic algorithm. Finally, the proposed deployment strategy was verified through simulation cases. The simulation results showed that the proposed deployment strategy could reduce the deployment cost of shore-based cooperative units based on meeting the communication demand and deploy regional shore-based cooperative units.

Функциональная модель объединенного источника комплексной системы синхронизации и доставки шкалы времени для крупной и распределенной системы технологического назначения

Purpose: to develop scientific and technical proposals and requirements for building sources of synchrosignals and messages of a single time for the integrated system of synchronization and delivery of the time scale depending on the requirements in frequency-time support signals and the current communication chain architecture in the framework of solving a unified synchronization system in phase time and the frequency in large territorially distributed and industrial technological systems. Methods: the applied research methods include the fundamental provisions of the theory of constructing synchronization systems, as well as infocommunication systems and networks. Results: a functional model of a united source of the integrated system of synchronization and delivery of the time scale, which includes a complete set of elements to perform the functions of synchronization and delivery of single-time signals in existing and promising complex heterogeneous networks with various architecture and heterogeneous equipment that differs from the existing fully taking into account the existing ones in Currently, technologies for delivery of single time and frequency signals, both on the basis of switching packages and on the basis of switching of channels. The proposed functional model of the united source of the integrated system of synchronization and delivery of the time scale will allow to reasonably approach the design of the means of transmitting and delivering single time and frequency signals to various and heterogeneous communication networks, taking into account all the needs for frequency-time support signals and limiting existing communication networks. Practical significance: the use of sources of single-time and frequency signals, which are based on the proposed functional model, will ensure a flexible infrastructure to ensure the unity of the unity of the unified time and clock network synchronization necessary for the correct functioning of communication equipment; Satisfy the requirements of synchronization signals and uniform messages for promising and existing consumers as much as possible; ensure the possibility of a phased transition of the construction of communication networks from the technologies of the channels to the technology of switching packages due to flexibility in the use of routes and transmission systems necessary for the spread of synchrosignals and uniform messages.

A highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy

New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.

Outage analysis of downlink communications in heterogeneous networks under backhaul constraint

Outage analysis of downlink communications in heterogeneous networks under backhaul constraint

Методика формирования комплексной системы синхронизации и доставки шкалы времени для крупной распределенной системы технологического назначения

Purpose: To develop scientific and technical solutions for constructing a comprehensive synchronization system and delivery of the time scale, which includes subsystems of network clock synchronization (NCS) and unified time (UT), as a unified timekeeping system addressing the challenges of frequency, time, and phase synchronization in large-scale distributed technological purpose systems. Methods: Research methods are based on the fundamental provisions of the theory of systems and the theory of decision-making. Results: A methodology for the formation of a comprehensive synchronization system and the delivery of a time scale is proposed, which is characterized by an integrated approach to building unified time and frequency systems in the communication networks of the technological segment, the independence of the types of generator equipment used and the means of delivery of NCS and UT signals, as well as meeting the requirements of potential consumers of frequency and time services. The proposed methodology for the formation of a comprehensive synchronization system and delivery of a time scale will allow to reasonably approach building systems for delivering unified time and frequency signals in complex heterogeneous communication networks. Practical significance: Solutions based on the presented methodology will allow the provision of an infrastructure independent of external communication operators to ensure the unity of network clock synchronization and unified time. This is necessary for the correct operation of telecommunication equipment of the technological communication network. The methodology will also meet the needs for providing a full range of synchronization and unified time signal services to existing and prospective consumers. Additionally, it will enable a gradual transition of the organization of communication networks from channel-swithching to packet-oriented technologies due to a flexible approach to the routes and transmission systems used for the distribution of synchronization signals and unified time.

Collision-Risk-Based Event-Triggered Optimal Formation Control for Mobile Multiagent Systems Under Incomplete Information Conditions

This article deals with collision-risk-based event-triggered optimal formation control problems for mobile multiagent systems. First, several collision-risk-related definitions, such as collision-free margin, moving direction angle, collision risk angle, and collision risk level, are proposed for the moving agents. Then, a collision-risk dependent, time-varying, event-triggered heterogeneous communication network topology is developed, where the agent starts obtaining information of the neighboring agents only when collision risks occur among them. Third, an anti-collision control law, which is composed of a switch function, a control force direction function, and a control strength function, is designed to guarantee the collision avoidance formation of multiagents. Fourth, to ensure the formation quality and save control cost of the multiagent system, an optimal formation control scheme with feedforward compensation is designed. Simulation results illustrate that: 1) by using the collision risk information of mobile agents, the proposed control scheme is effective to realize the collision avoidance optimal formation task and 2) the anti-collision formation controller can be implemented with incomplete information of the agents.

Handover Network Selection for Vehicle in LTE-A Communication Networks

One of the challenges to achieving smooth communications in heterogeneous networks is to find the right balance between various handover parameters for network selection. The Receive Signal Strength (RSS) from a network's Point of Attachment (PoA) is the primary factor used in traditional approaches to network selection. Therefore, network selection is vital for a successful handover in the vehicle communications network. However, vehicles moving through such diverse networks faced difficulties like frequent unwanted handovers, selecting the wrong candidate networks for handovers, and taking too long to finish the drawn-out handover procedure, which results in failed handovers. All of these factors will impair the efficiency of the handover process, which will affect the smooth operation of the vehicles. Hence, the main idea of this research is to improve the handover network performance selection performance. The proposed work is for vehicles to self-evaluate a candidate list of Access Points (AP) that are located in the vehicle movement direction and select the best underlying candidate network. In this project, the network selection framework of the vehicle communications network with the distance between the target candidate and the trajectory of the vehicle movement was developed as well as the vehicle mobility information. The important parameters which are the relative direction index, proximity index and residence time index are chosen to identify the best candidate network. Once the target of the base station has been identified, then the optimum value of the distance is calculated using the Particle Swarm Optimization (PSO) technique to initiate the handover process. The performance of the proposed handover network selection is simulated and evaluated using MATLAB software. The results show that the proposed handover network selection method improved the successful handover rate as compared to the conventional method.

Performance of Q-learning based resource allocation for D2D communications in heterogeneous networks

This study investigates energy efficiency issues of device-to-device (D2D) communications in heterogeneous networks. To minimize the total transmitted power, an approach based on Q-learning together with adaptive ɛ-greedy is proposed to optimize the connection of user equipment (UE) with base station (BS) or access point (AP). The proposed adaptive ɛ-greedy can conduct the adequate exploration and exploitation operations for effective optimization. Simulation results indicate that in the single-cell scenario, the proposed method can attain performance close to the best solution.

IoT Based System for Sewage Overflow Prevention using Heterogeneous Communication Networks

The increasing population along with water scarcity give rise to water management practices. Water scarcity can be eradicated by wastewater treatment that would in turn prevent contamination of water bodies. One of the challenges in wastewater treatment is to efficiently transport and treat the sewage in a Sewage Treatment Plant (STP). Most of the existing STPs in closed campuses of Indian scenarios such as academic institutions, industries, and residential apartments employ several pumping stations in a campus and pump motors in each station that pump sewage to STP for treatment. Manual operation of such motors would lead to sump overflows that negatively impact public health and sanitation. Therefore, an IoT-based system for monitoring and controlling sewage flow is proposed in this paper that uses water level sensors and prevents sewage overflows in pumping station sumps, resulting in the utilisation of all the collected sewage for treatment. Since each campus might have different communication networks such as GPRS, WiFi, RF, or Ethernet, the proposed system is designed to work with any network. The proposed system was tested in the college campus with three pumping stations. Moreover, a test bed was simulated and tested with 100 pumping stations. It is observed that the proposed approach prevents sewage overflows in various scenarios with different constraints.

COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.

Digital Object Architecture for IoT Networks

The Internet of Things (IoT) is a recent technology, which implies the union of objects, “things”, into a single worldwide network. This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices. Device identification is one of these challenges that becomes complicated with the increase of network devices. Despite this, there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications. In this regard, one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks. Many unique software and hardware solutions are used as a unique global identifier; however, such solutions have many limitations. This article proposes a novel solution, based on the Digital Object Architecture (DOA), that meets the requirements of identifying devices and applications of the IoT. This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks. We propose a model of an identification system based on the architecture of digital objects, which differs from the well-known ones. The proposed model ensures an acceptable quality of service (QoS) in the common architecture of the existing public communication networks. A novel interaction architecture is developed by introducing a Middle Handle Register (MHR) between the global register, i.e., Global Handle Register (GHR), and local register, i.e., Local Handle Register (LHR). The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented. The developed model is simulated for a wide-area network with allocated registers, and the results are introduced and discussed.

Heterogeneous Communication Networks and Localization for Industry 4.0 Applications in Small and Medium-Sized Enterprises: A Systematic Literature Review

This study demonstrates the result of a systematic literature review using the preferred reporting items for systematic review and meta-analysis (PRISMA) method. The main objective is to find issues that still need research on heterogeneous communication, especially 5G cellular and localization networks for small and medium-sized enterprises (SMEs) in the context of Industry 4.0. The research query is formulated based on the research questions and implemented in three selected databases. Then, the query results are categorized into six sections. Consequently, open issues addressed by the assessed papers in five general categories are analyzed and discussed.

Heterogeneous communication network planning based on ant colony algorithm in smart grid

With the development of smart grids, highly automated power systems increasingly rely on communication networks, and research on the reliability, safety, and economy of heterogeneous communication networks is particularly important. Traditional network planning methods do not take into account network load problems, resulting in serious network congestion and long delays. Aiming at the above problems, this paper proposes a heterogeneous communication network planning method for a smart grid based on an improved ant colony algorithm. This method improves the original ant colony algorithm by updating the pheromone concentration of the network node in real time and realizes the planning of heterogeneous communication networks in the smart grid. Simulation results show that the proposed method has the advantages of avoiding local network congestion, reducing the average delay, and improving network stability.

A Baseline-Resilience Assessment Method for UAV Swarms Under Heterogeneous Communication Networks

The resilience assessment for unmanned aerial vehicle (UAV) swarms is an effective method of measuring the risk resistance of UAV swarms. Although preliminary studies have been performed on the resilience assessment for UAV swarms, these studies still have limitations that cannot be ignored, such as network homogeneity, unreasonable resilience process models, and stringent mission baselines. These limitations reduce the reference value of the evaluation results. Therefore, a new baseline-resilience assessment method for UAV swarms is proposed in this article. First, a topological model of a UAV swarm heterogeneous communication network is constructed by introducing the concept of heterogeneous graphs. Second, regarding the integrity of the resilience process, an innovative resilience process model is established. Third, a mission-oriented UAV swarm baseline-resilience assessment method is proposed based on the topological and resilience process models. Finally, the effectiveness and the superiority of the proposed evaluation method are verified by simulations. The results show that the proposed evaluation method is more in line with actual situations than other evaluation methods and the obtained resilience value is more valuable for reference.

Mobility-Aware Data Caching to Improve D2D Communications in Heterogeneous Networks

User Equipment (UE) is equipped with limited cache resources that can be utilized to offload data traffic through device-to-device (D2D) communications. Data caching at a UE level has the potential to significantly alleviate data traffic burden from the backhaul link. Moreover, in wireless networks, users exhibit mobility that poses serious challenges to successful data transmission via D2D communications due to intermittent connectivity among users. Users’ mobility can be exploited to efficiently cache contents by observing connectivity patterns among users. Therefore, it is crucial to develop an efficient data caching mechanism for UE while taking into account users’ mobility patterns. In this work, we propose a mobility-aware data caching approach to enhance data offloading via D2D communication. First, we model users’ connectivity patterns. Then, contents are cached in UE’ cache resources based on users’ data preferences. In addition, we also take into account signal-to-interference and noise ratio (SINR) requirements of the users. Hence, our proposed caching mechanism exploits connectivity patterns of users to perform data placement based on users’ own demands and neighboring users to enhance data offloading via cache resources. We performed extensive simulations to investigate the performance of our proposed mobility-aware data caching mechanism. The performance of our proposed caching mechanism is compared to most deployed data caching mechanisms, while taking into account the dynamic nature of the wireless channel and the interference experienced by the users. From the obtained results, it is evident that our proposed approach achieves 14%, 16%, and 11% higher data offloading gain than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of users, cache capacity, and number of contents, respectively. Moreover, we also analyzed cache hit rates, and our proposed scheme achieves 8% and 5% higher cache hit rate than the least frequently used, the Zipf-based probabilistic, and the random caching schemes in case of an increasing number of contents and cache capacity, respectively. Hence, our proposed caching mechanism brings significant improvement in data sharing via D2D communications.

Electronically Steerable Antennas for Future Heterogeneous Communication Networks: Review and Perspectives

Satellite, fifth generation (5G), and sixth generation (6G) mobile communication systems operating at micro- and millimeter wave frequencies are an essential pillar in advanced network architectures for high-throughput low latency services. The decisive factors for this current development were the significant technological advances accomplished over the last two decades, which meanwhile enable highly integrated, feature-rich, and cost-effective realizations of complete phased array transceiver topologies. Motivated through the today's trend for heterogeneous constellation types to provide truly global coverage, this contribution reviews the current state-of-the-art of electronically steerable antennas for terrestrial and non-terrestrial communication systems up to 100 GHz. First, the potential benefits and limitations of the most relevant technologies are contrasted and put into context with recent system architectures for adaptive beamforming. Their operating principles along with various experimental implementation and achievements providing advanced capabilities such as multi-band/multi-beam operation, polarization agility, and wide-angle scanning are thoroughly presented. Particular emphasis is laid on the review of direct radiating arrays, quasi-optical antenna configurations, and metasurface-based antennas.

A differential privacy‐preserving deep learning caching framework for heterogeneous communication network systems

Caching and artificial intelligence technology are means to improve the content transmission rate and solve the delay problem in heterogeneous communication networks, which provide an important technical basis for telemedicine diagnosis, disease monitoring, and other medical and healthcare fields. However, since the data used for caching recommendations and mining usually includes a lot of sensitive information, collecting these data will inevitably cause users to worry about personal privacy security. This paper proposes a differential privacy-preserving deep learning caching framework (DP-DLCF) for heterogeneous communication networks. First, the multivariate randomized response mechanism is used to perturb the mobile client's sensitive information while introducing the least square method to balance the data privacy and utility, and realize the adaptive allocation of the privacy budget. Then use selective integrated learning methods to further improve the local cache accuracy of the neural network training model. Therefore, the framework not only improves the prediction accuracy of the cache network but also solves the problem of privacy leakage in data sharing. Finally, four simulation examples verify the DP-DLCF framework's effectiveness.

HERA: High-Efficiency Resource Allocation Scheme for Newly Joined Mobile Terminal in Heterogeneous Wireless Network

Objectives:To design an efficient resource allocation design for provisioning service of varied traffic classes to Multi-Mode Mobile Terminals (MMTs) in Heterogeneous Wireless Networks (HWNs). In HWNs, users prerequisite certain Quality of Service (QoS); thus, for meeting QoS, theMMTs are handoff to the new network. However, handoff MMTs might induce interference with the ongoing communication of existing MMTs. As resources are shared, resource allocation becomes a challenging task. Methods:This study presents a High-Efficiency Resource Allocation optimization model for HWNs. The HERA scheme first employs Channel State Information (CSI) estimation for mitigating interference and establishing channel availability. Second, using game theory optimal resource is allocated to MMTs and proves the existence of Nash equilibrium (NE) for spectrum resource allocation to MMTs in HWNs. Finally, a hybrid resource allocation algorithm combining contentionless and contention-based is designed to provide an improved resource allocation mechanism. Findings:The experiment outcome shows the HERA scheme allocates resources more efficiently in comparison with Access Fairness Resource Allocation (AFRA)(1), Joint Resource Allocation with Power Optimization (JRA-PO)(2), Resource Allocation and Node Placement (RANP)(3), and Existing Resource Allocation (ERA)(4). The HERA improves throughput by 19.07% and reduces collision by 42.96% in comparison with ERA(4) . Novelty: existing model predominantly focused on either addressing interference or maximizing throughput by leveraging either contention or contention-less resource allocation mechanism. However, in this study both contention-less and contention-based mechanisms are merged to maximize throughput and reduce collision. Keywords: Interference; Quality of service; Resource allocation; Softcomputing; Spectrum utilization; Heterogeneous communication network

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

Purpose With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy. Design/methodology/approach With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. Findings Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0. Originality/value The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.