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This paper proposes a new Heterogeneous Multi‐hop Cellular IP (MCIP) network that integrates multi‐hop communication with Cellular IP. MCIP increases the coverage of the wireless network and improves the network robustness against adverse propagation phenomena by supporting communication in dead zones and areas with poor radio coverage. MCIP includes three components: location management, connection management and route reconfiguration. Location management is responsible for maintaining the location information for Mobile Stations (MSs) in a local domain. Connection management establishes an initial path for data transmission and a route reconfiguration mechanism is proposed to take advantage of various multi‐hop connection alternatives available based on terminal interfaces, network accessibility and topology. Our simulation results show that MCIP performs well in networks of various sizes including scalability, throughput, and packet delay.

Several new network types have emerged over the last couple of years. Many of these networks are being connected together to provide mobile users with the capability of staying always connected to the Internet which requires seamless transitions from one network to another without causing interruption to on‐going connections. To maintain connectivity during handoff, all the networks that are accessible to the mobile station need to be known. The paper aims to propose a novel energy‐efficient network discovery approach that enables fast discovery and selection of available access networks.

The performance of the proposed approach over an actual network testbed consisting of heterogeneous networks (such as wireless local area, cellular) is evaluated.

It was found that the approach yields between 12.4 and 19.1 per cent improvement in battery power consumption over other network discovery approaches.

The paper proposes and implements a novel scheme to discover wireless networks in a multi‐access environment consisting of heterogeneous networking technologies.

Vertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme.

This paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.

The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps.

In this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

Coexistence of various wireless access networks and the ability of mobile terminals to switch between them make an optimal selection of serving networks for multicast groups a challenging problem. Since optimal network selection requires large dimensions of data to be collected from several network locations and sent between several network components, the scalability can easily become a bottleneck in large-scale systems. Therefore, reducing data exchange within heterogeneous wireless networks is important. The paper aims to discuss these issues.

The authors study the decision-making process and the data that need to be sent between different network components. To analyze the operation of the wireless heterogeneous network, the authors built a mathematical model of the network. The objective is defined as a minimization of multicast streams in the system. To evaluate the heuristic solutions, the authors define the upper and lower bounds to their operation.

The proposed heuristic solutions substantially reduce the usage of bandwidth in mobile networks and exchange of information between the network components.

The authors proposed the approach that allows network selection in a decentralized manner with only limited information shared among the decision makers. The authors studied how different sets of information available to decision makers influenced the performance of the system. The work also investigates the usage of multiple paths for multicast in heterogeneous mobile environments.

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.

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.

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.

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.

Emerging real‐time multimedia services over IP have penetrated into the daily lives of normal people with the advent of advanced broadband communications and innovated interconnection technologies. To protect shared Internet from unfairness and further congestion collapse, rate control plays a crucial role for many multimedia services such as streaming applications. Streaming applications such as video on demand (VoD) or voice over IP (VoIP) services face some critical problems such as insufficient bandwidth and improper performance of transmission protocols. Besides, the new generation networks are anticipated to integrate all heterogeneous wired and wireless networks and offer seamless customized multimedia services anywhere, anytime. However, wireless networks usually with low and variable bandwidth, and non‐congestion related loss do bring the challenge to the existing transport protocol, such as TCP, TFRC etc.

The paper uses simulations and compares other methods. The purpose of this paper is to address these issues. The paper, proposes a rate control scheme named Jitter‐based Rate Control (JRC) to fit wired‐wireless hybrid network which has better performance than the current rate control scheme.

Extensive simulations and comparisons with other methods verify the effectiveness of our method for accurate and smooth estimation, no matter whether the wireless links are located.

The paper introduces the JRC.

Mobility management for single‐hop cellular networks has received much research attention in the last few years. One of the research challenges for 4G wireless systems is the design of mobility management techniques that integrate cellular and ad‐hoc networks. Currently, there are no structured mobility management schemes for these heterogeneous multi‐hop networks. This paper aims to address these issues.

This paper describes techniques for tracking a mobile node (MN) in an integrated architecture with minimum overhead. This paper proposes group rerouting concept.

The paper implements and evaluates the proposed protocol by using the network simulator (NS‐2). The proposed protocol increases performance compared to broadcasting schemes.

This scheme considers devices with two interfaces only.

The paper proposes a scheme to extend the coverage of cellular base stations by using ad‐hoc devices.

This paper describes techniques for tracking an MN in an integrated architecture with minimum overhead. This scheme is independent of the routing protocol used in a multi‐hop network.

Heterogeneous network is mainly focused to enrich the demands of network's traffic and data rate. Heterogeneous network is an integrated system composed of diverse radio access technologies such as Wi-Fi, UMTS and WiMAX soon. To exchange the packets among these different wireless technologies, an adoptable vertical handoff (VHO) is required.

Various types of techniques have been proposed in line with VHO. However these algorithms lead to the threat of high dropping rate and poor Quality of Service (QoS). We modulate a new methodology by integrating Convolutional Neural Network with Fuzzy Logic (CNN-FL) for an optimum vertical handoff decision process (VHDP). The CNN-FL–based VHDP is very effective because of nonlinearity and generalization capability with uncertain data inputs.

The performance results exhibit that proposed methods show efficient for selecting the appropriate network with maximum throughput. At the same time, the proposed method reduces computational complexity and dropping rate considerably.

This paper proposes a novel CNN model along with a FL model for achieving a vertical hand off.

Fifth-generation (5G) networks play a significant role in handover methods. 5G wireless network is open, flexible and highly heterogeneous along with the overlay coverage and small cell deployments. Handover management is one of the main problems in the heterogeneous network. Also, handover satisfies the needs of ultra-reliable communications along with very high reliability and availability in 5G networks. Handover management deals with every active connection of a user’s device, which moves the connection between the user’s device and the counterparty from one network point to another. Thus, the handover decision determines the best access network and also decides whether the handover is performed or not.

The main intention of this survey is to review several existing handover technologies in 5G. Using the categories of analysis, the existing techniques are divided into different techniques such as authentication-based techniques, blockchain-based techniques, software-defined-based techniques and radio access-based techniques. The survey is made by considering the methods such as used software, categorization of methods and used in the research works. Furthermore, the handover rate is considered for performance evaluation for the handover techniques in 5G. The drawbacks present in the existing review papers are elaborated in research gaps and issues division.

Through the detailed analysis and discussion, it can be summarized that the widely concerned evaluation metric for the performance evaluation is the handover rate. It is exploited that the handover rate within the range of 91%–99% is achieved by three research papers.

A survey on the various handover mechanisms in 5G networks is expected in this study. The research papers used in this survey are gathered from different sources such as Google Scholar and IEEE. Also, this survey suggests a further extension for the handover mechanism in 5G networks by considering various research gaps and issues.

An infrastructure based on multiple heterogeneous access networks is one of the leading enablers for the emerging paradigm of pervasive computing. The optimal management of diverse networking resources is a challenging problem. This paper aims to present a context‐aware policy mechanism with related end‐to‐end (E2E) evaluation algorithm for adaptive connectivity management in multi‐access wireless networks.

A policy is used to express the criteria for adaptive selection of the best local and remote network interfaces. The best connection can then be used for the establishment of a channel as well as for the maintenance of on‐going data transmission. Rich context information is considered in the policy representation with respect to user profile and preference, application characteristics, device capability, and network quality of service conditions. The decision of the best access networks to be used is made on the basis of an E2E evaluation process. The decision can be made in both master–slave and peer‐to‐peer modes, according to the decision matrixes generated in both ends. The paper focuses on the policy representation and connection evaluation algorithm. A case study is presented to show the usability of the proposed policy mechanism and decision‐making algorithm in the adaptiv management of heterogeneous networking resources.

The proposed policy mechanism is for the adaptive decision of connection selection in channel establishment and vertical handoff between heterogeneous access networks. A policy is represented as a four‐tuple, including the direction and the class of traffic, requirement expression, and concrete evaluation items. Three steps are involved in the evaluation process, namely policy traverse, decision matrix calculation, and decision‐making.

The policy mechanism can be easily extended to include adaptive selection of multiple user devices in addition to multiple connections.