Tier Network Research Articles

A Survey on Reduction of Energy Consumption in Fog Networks-Communications and Computations.

Fog networking has become an established architecture addressing various applications with strict latency, jitter, and bandwidth constraints. Fog Nodes (FNs) allow for flexible and effective computation offloading and content distribution. However, the transmission of computational tasks, the processing of these tasks, and finally sending the results back still incur energy costs. We survey the literature on fog computing, focusing on energy consumption. We take a holistic approach and look at energy consumed by devices located in all network tiers from the things tier through the fog tier to the cloud tier, including communication links between the tiers. Furthermore, fog network modeling is analyzed with particular emphasis on application scenarios and the energy consumed for communication and computation. We perform a detailed analysis of model parameterization, which is crucial for the results presented in the surveyed works. Finally, we survey energy-saving methods, putting them into different classification systems and considering the results presented in the surveyed works. Based on our analysis, we present a classification and comparison of the fog algorithmic models, where energy is spent on communication and computation, and where delay is incurred. We also classify the scenarios examined by the surveyed works with respect to the assumed parameters. Moreover, we systematize methods used to save energy in a fog network. These methods are compared with respect to their scenarios, objectives, constraints, and decision variables. Finally, we discuss future trends in fog networking and how related technologies and economics shall trade their increasing development with energy consumption.

Edge caching and computing of video chunks in multi-tier wireless networks

This paper proposes a video caching and transcoding strategy for delay-constrained content delivery in multi-tier wireless networks. Particularly for multimedia services whose content can be encoded into multiple quality versions and consists of multiple chunks, we present an approach of caching chunks of an identical file separately in different network layers with different qualities. Given this caching policy, a complete file can be retrieved by receiving its chunks from different network tiers; therefore, a video transcoding and delivery method is jointly designed with caching to maximize the expected content quality with a strict delay constraint. Motivated by that caching is proactively conducted in advance of many user requests, the joint optimization problem is decomposed into caching and transcoding subproblems. The optimal transcoding policy and its transcoded qualities at different network tiers are analytically found by linear programming for a given cache state. Using the closed-form transcoded qualities, the caching policy is optimized to maximize the expected content quality with respect to randomness of user locations and channel gains. Due to the difficulty of expectation, the caching policy of each network layer is independently obtained by water-filling first, and an alternating optimization method is used to find the joint optimal caching policy satisfying the cache size constraints. Simulation results show that the proposed scheme succeeds in delivering content of 5% to 10% higher expected quality to users than comparison schemes which do not use partitioning for caching. Simultaneously, our approach meets the delay requirement and effectively manages the trade-offs arising from the varying storage and computational capacities within hierarchies, as well as video diversity.

Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

Multicriteria and Quality of Service- Aware Vertical Handover Solution for Vehicle-to-Infrastructure Communication in Multitier Heterogeneous Networks.

The growing integration of vehicle-to-infrastructure (V2I) communication within multitier heterogeneous networks (HetNets) introduces challenges in maintaining seamless connectivity and Quality of Service (QoS) for vehicular users. This paper introduces a modified Multicriteria and QoS-aware Vertical Handover Decision Algorithm (mV2I-MHA) tailored to address these challenges. By considering multiple criteria - such as packet loss ratio, cost, available bandwidth and packet latency - the proposed solution intelligently manages vertical handovers between different network tiers. Numerous Multi-Criteria Decision-Making (MCDM) techniques have been proposed to address the right target network selection aiming to reduce unnecessary handovers, incorrect network selections and the associated processing time. However, many of these solutions overlook the significance of varying criteria weights in vertical handover decisions made by their selection algorithms. Through comprehensive simulations and comparisons with existing techniques, the effectiveness of the proposed – in this paper - solution is demonstrated in enhancing handover success rate, reducing handover failure rate, minimizing latency, and overall elevating the QoS for V2I communication in multitier HetNets. MATLAB R2020a was utilized to simulate the work. Using packet latency and handover failure as the metrics for performance, the results were contrasted with those of an existing works in terms of handover failure rate and packet delay. The developed mV2I-MHA showed considerable percentage reductions of 10.5% and 23.9% in packet latency and handover failure rate, respectively.

HGL_GEO: Finer-grained IPv6 geolocation algorithm based on hypergraph learning

IP geolocation is necessary for applications such as location-aware ad recommendation, traceability, and fraud detection. Presently, graph neural network-based approaches focus on geolocation information transfer of neighboring nodes of the target IP node. However, terminal hosts in real physical proximity in tier networks are often not directly connected, graph learning tasks ignore geographic knowledge from more distant connecting relationships. To address the limitations of existing work, we propose a new HyperGraph Neural Network (hyperGNN)-based IPv6 geolocation framework HGL_GEO (HyperGraph Learning Geolocation), by modeling a set of IP host groups with strong geographic-location correlation using hyper-edges, and incorporating IP knowledge and connectivity relationships into the hypergraph. The target IP nodes then learn geolocation knowledge in the hypergraph convolution, thus enabling IPv6-based geolocation tasks. The final experimental results on a 10-month test data collection from three real regions (Shanghai, New York, and Tokyo) show that the median error distance ranges from 5.837 km to 8.52 km, and that the average error distance ranges from 7.012 km to 9.359 km, compared with those of TNN, MLP_Geo, and GWS_Geo IP geolocation algorithms and the GCN and GAT. The median error distance of HGL_GEO is at least 10.12% less and the average error distance is at least 3.3% less than those of deep learning models.

Distance‐based cell range extension and almost blank sub‐frame for load balancing and interference mitigation in 5G and beyond heterogeneous networks

AbstractThe goal of deploying LTE‐Advanced heterogeneous networks is to meet the growing need for Quality of Service (QoS), high data rates, and more coverage. Load balancing is one of the biggest problems, especially when User Equipment (UE) uses received signal strength to connect to different transmission power network tiers. Spectrum for small cells has not been used to its full potential. While all the cells are using the same frequency spectrum and the small cells overlap the Macro Cells, preventing interference is another big problem. The proposed Cell Range Extension (CRE) and Almost Blank Sub‐frame (ABS) provides a solution to overcome these challenges. The proposed ABS in this paper is novel because it is a partial muting one rather than the full muting ABS in recent research studies. In addition, the cell association method is by distance rather than conventional Reference Signal Received Power (RSRP). CRE‐ABS has been applied simultaneously to solve both load balancing and interference. Simulation results show that the proposed scheme achieves substantial throughput and Jain's Fairness Index (JFI).

A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology.

Ambient assisted technology (AAT), which has the potential to enhance patient care and productivity and save costs, has emerged as a strategic goal for developing e-healthcare in the future. However, since the healthcare sensor must be interconnected with other systems at different network tiers, distant enemies have additional options to attack. Data and resources integrated into the AAT are vulnerable to security risks that might compromise privacy, integrity, and availability. The gadgets and network sensor devices are layered with clinical data since they save personal information such as patients' names, addresses, and medical histories. Considering the volume of data, it is difficult to ensure its confidentiality and security. As sensing devices are deployed over a wider region, protecting the privacy of the collected data becomes more difficult. The current study proposes a lightweight security mechanism to ensure the data's confidentiality and integrity of the data in ambient-assisted technology. In the current study, the data are encrypted by the master node with adequate residual energy, and the master node is responsible for encrypting the data using the data aggregation model using a node's key generated using an exclusive basis system and a Chinese remainder theorem. The integrity of the data is evaluated using the hash function at each intermediate node. The current study defines the design model's layered architecture and layer-wise services. The model is further analyzed using various evaluation metrics, such as energy consumption, network delay, network overhead, time in generating hash, tradeoff between encryption and decryption, and entropy metrics. The model is shown to adequately perform on all measures considered in the analysis.

Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic

The COVID-19 pandemic has placed severe demands on healthcare facilities across the world, and in several countries, makeshift COVID-19 centres have been operationalised to handle patient overflow. In developing countries such as India, the public healthcare system (PHS) is organised as a hierarchical network with patient flows from lower-tier primary health centres (PHC) to mid-tier community health centres (CHC) and downstream to district hospitals (DH). In this study, we demonstrate how a network-based modelling and simulation approach utilising generic modelling principles can (a) quantify the extent to which the existing facilities in the PHS can effectively cope with the forecasted COVID-19 caseload; and (b) inform decisions on capacity at makeshift COVID-19 Care Centres (CCC) to handle patient overflows. We apply the approach to an empirical study of a local PHS comprising ten PHCs, three CHCs, one DH and one makeshift CCC. Our work demonstrates how the generic modelling approach finds extensive use in the development of simulations of multi-tier facility networks that may contain multiple instances of generic simulation models of facilities at each network tier. Further, our work demonstrates how multi-tier healthcare facility network simulations can be leveraged for capacity planning in health crises.

Future dynamic multimedia content access via aerial computing system

Multimedia content access sovereignty arises due to the intention of producers to enable subscribers from pre–defined regions access multimedia content. This limits the number of locations (with subscribers) that can access producer content. Therefore, the ability to access multimedia content across previously unconsidered locations is limited. The presented research addresses this challenge and focuses on multimedia content sharing among subscribers in arid and hyper–arid regions. The use of stratosphere based data centres (SBDCs) is proposed. The paper also presents multi–tier network architecture for network traffic management. This ensures that network traffic congestion does not limit access to multimedia content by subscribers across multiple regions. The use of SBDCs increases the number of locations that engage in the sharing of multimedia content. Evaluation shows that the proposed solution increases the number of data sharing locations by (75.8 – 88.2) % on average.

Two-Criteria Technique for the Resource-Saving Computing in the Fog and Edge Network Tiers

Introduction. At present, the concepts of fog and edge computing are used in a wide range of applications of various kinds. One of the key problems in the organization of computing in groups of mobile devices that make up the edge/fog layer is the mission assurance based on battery power availability. In this context, a lot of developments aimed at energy saving of device systems have been presented to date. However, one important aspect remains beyond the consideration of the problem of resource saving, namely, the issue of saving the residual resource of a computing device. The aim of this research is to formalize the workload distribution problem as two-criteria optimization problem, and to develop the basic solution technique.Materials and Methods. Within the framework of this article, an approach to resource saving is proposed. It is based on the evaluation of two device criteria: battery life and residual resource of a computing device. The residual resource of a computing device can be estimated using the probability of failure-free operation of the device, or as the reciprocal of the failure rate, taking into account that the exponential law of failure distribution is used in the simulation. From this, a model of the problem of two-criteria optimization is formulated, taking into account the dynamics of the network topology in the process of performing a user mission. The topology dynamics is reflected in the model as a sequence of topologies, each of which corresponds to a certain period of time of the system operation.Results. Based on the proposed model of the two-criteria optimization problem, a method was proposed for resource saving in the edge and foggy layers of the network. It reflected the specifics of the dynamic layers of the network, and also took into account the importance of the criteria for estimating the consumption of device resources. An experiment was conducted to evaluate the impact of the method of distributing tasks over a network cluster on the probability of failure-free operation of devices and on the average residual resource.Discussion and Conclusions. The conducted experiment has demonstrated the feasibility of using the developed method, since the distribution of tasks among executing devices had a significant impact (up to 25 % according to the results of the experiment) on the average residual resource of a computing device.

Hybrid Learning based Radio Resource Management in 5G Heterogeneous Networks

Ultradensification using different types of small cells (SCs) is one of the key enabling solutions to meet the multiple stringent requirements of 5G cellular networks. However, radio resource management (RRM) in ultra-dense heterogeneous networks (HetNets) is not easy due to interferences in multi-tiered architecture and dynamic network conditions. Interferences in 5G HetNets can be efficiently managed only through the techniques which are adaptive and self-organizing to handle dynamic conditions in 5G HetNets. In this article, a machine learning (ML) based self-adaptive resource allocation scheme is proposed based on the combination of independent and cooperative learning and evaluated for ultra-dense 5G HetNets. The proposed scheme aims to improve the QoS of all users associated with different network tiers in ultra-dense HetNets simultaneously. The proposed solution adaptively optimizes the SCs transmit power either through independent learning or cooperative learning based on the varying density of small cells to minimize the interferences and ensure minimum QoS requirements for all users in different network tiers. The proposed scheme not only maintains the minimum required capacities for QoS provision to all users simultaneously but has also shown a significant improvement in the capacities of users in different network tiers in high interference scenarios as compared to the use of a single learning scheme.

Security for Multi-hop Communication of Two-tier Wireless Networks with Different Trust Degrees

Many effective strategies for enhancing network performance have been put forth for wireless communications’ physical-layer security. Up until now, wireless communications security and privacy have been optimized based on a set assumption on the reliability or network tiers of certain wireless nodes. Eavesdroppers, unreliable relays, and trustworthy cooperative nodes are just a few examples of the various sorts of nodes that are frequently categorized. When working or sharing information for one another, wireless nodes in various networks may not always have perfect trust in one another. Modern wireless networks’ security and privacy may be enhanced in large part by optimizing the network based on trust levels. To determine the path with the shortest total transmission time between the source and the destination while still ensuring that the private messages are not routed through the untrusted network tier, we put forth a novel approach. Toexamine the effects of the transmit SNR, node density, and the percentage of the illegitimate nodes on various network performance components, simulation results are provided.

Resource Allocation in Macrocell–Femtocells via Combined Rain and Whale Optimization Algorithm

Femtocell (FC) dense exploitation imposes a novel challenge for the resource allotment in a 2-tier network, like: (1) dynamic resources allotment among the 2 tiers by considering the off-load traffic from macrocell (MC), (2) permitting access to users when ensuring QoS for FC subscribers, and (3) suitable power setting, which discovers cooperation among the overall bandwidth allotted to FCs and system performances. This research work examines the problems existing on cross-tier interfering as well as resource allocation alleviation in “full-duplex (FD) Orthogonal Frequency Division Multiple Access (OFDMA) oriented Heterogeneous Networks (HetNets), which involves macrocell with underlying femtocells”. Here, three primary contributions are concerned: a single objective issue together with power allocation, price allocation & subcarrier allocation of MC-FC networks. In addition, this research establishes a new hybrid algorithm termed Hybrid Whale and Rain Optimization (HW-RO) for solving the optimization problem in MC-FC Networks. Finally, the superiority of the suggested technique is evaluated in comparison to various existing approaches.

Coverage Enhancement of Underwater Internet of Things Using Multilevel Acoustic Communication Networks

Underwater acoustic communication networks (UACNs) are considered a key enabler to the Underwater Internet of Things (UIoT). UACN is regarded as essential for various marine applications, such as monitoring, exploration, and trading. However, a large part of existing literature disregards the 3-D nature of the underwater communication system. In this article, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier UACN that acts as a gateway that connects the UIoT with the space–air–ground–sea integrated system (SAGSIS). The proposed network architecture consists of several tiers along the vertical direction with adjustable depths. On the horizontal dimension, the best coverage probability (CP) is computed and maximized by optimizing the densities of surface stations (SSs) in each tier. On the vertical dimension, the depth of each tier is also optimized to minimize intertier interference and maximize overall system performance. Using tools from stochastic geometry, the total CP of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -tier network is analyzed. For given spatial distribution of UIoT device’s depth, the best CP can be achieved by optimizing the depths of the transceivers connected to the SSs through a tether. We verify the accuracy of the analysis using Monte Carlo simulations. In addition, we draw multiple useful system-level insights that help optimize the design of underwater 3-D networks based on the given distribution of UIoT device’s depths.

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment.

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

The Security Paradigm That Strikes a Balance Between a Holistic Security Mechanism and The WSN's Resource Constraints

There is a need for comprehensive holistic security methods that can provide a long-term response to the challenge of security in Wireless Sensor Networks (WSNs). Due to the widespread use of WSN around the world, particularly the Internet of Things (IoTs), quantum security is predicted to aid in the comprehensive security strategy for WSN environments. These quantum securities can be used in conjunction with WSN to provide data security anywhere and at any time. The goal of this study is to examine the security constraints of WSNs and provide a holistic strategy across all network tiers that will serve as a balanced security mechanism solution, as well as a solution to protect nodes from vital data attack, eavesdropping, disruption, destruction as well as alteration and other threats. WSNs are utilized for monitoring, tracking, and controlling applications in a variety of operations, including military, ecological, and health. However, the nature of their resource constraints causes difficulties. Low compute capabilities, tiny memory, limited energy resources, and unreliable communications are just a few of the factors that make data and node security problematic when employing WSN. As a result, as the need for WSNs grows, so does the requirement for improved security methods.

Multi-tier analysis of the medical equipment supply chain network: empirical analysis and simulation of a major rupture

PurposeWhile a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance.Design/methodology/approachUsing the mined data from five tiers of the backward supply chain of medical equipment, the authors constructed a large supply chain network consisting of close to 160,000 dyadic connections. The authors used various network centrality and clustering algorithms to measure the influence of each firm across the supply chain structure. Furthermore, the authors ran a scenario to simulate the elimination of Chinese firms from the global supply chain and recalculated all centralities. Regression analysis was used to measure the effect of supply chain network centralities on firms' performance across the supply chain with and without Chinese firms.FindingsThe complexity of global purchasing across global tiers of supply networks had been recognized as a source of uncertainty before the COVID-19 pandemic. This pandemic was the black swan that the medical supply chain professionals had noted its threat in recent years. While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance.Research limitations/implicationsThis research has three main implications. First, it provides a benchmark for manufacturing firms and distributors around the world operating in the post-COVID-19 business environment to better understand the relationship of their supply chain strategy and firms' financial performance. Second, investors and asset managers can evaluate their portfolios in light of the changing relationship as a result of possible protectionism initiatives. Finally, policymakers can apply the research methodology of this work in various industries while reevaluating post-COVID-19 international relations and trades policies at the firm, industry and country levels.Practical implicationsPolicymakers working on global connection can utilize the outcome of this research to explore the consequences of local and global policies on trade patterns, organizational performance as well as individuals' movements. Another implication of this study for policymakers is that it provides a powerful simulation and analytical tool to launch or combat the global ruptures, including trade wars and natural disasters stemming from natural events (e.g. climate change) and human-made events (e.g. wars, supply-chain interruptions, sanctions).Originality/valueTo the best of our knowledge, this is the first large-scale empirical study that measures the effect of supply chain structure across multiple (five) tiers of the global supply chain on firms' performance. The present study uses the original supply chain network data mined by the authors from financial publications.

Interference Management for K-Tier Networks Without CSIT Based on Reconfigurable Antennas

Interference Management for <i>K</i>-Tier Networks Without CSIT Based on Reconfigurable Antennas

Application of Cybertwin for Offloading in Mobile Multiaccess Edge Computing for 6G Networks

Multiaccess edge computing is an essential technology that academia and industry have recognized as fundamental for the future of the Internet of Things. Current research on the subject utilizes virtual machines as the intermediary between end devices and cloud servers. However, recently a new framework was proposed that utilizes Cybertwins instead of virtual machines for the same function. Such framework comes with a myriad of advantages but, most importantly, in this case, it includes a control plane capable of enabling cooperation between the Cybertwins. In this article, we present a mathematical model of the total service delay of a Cybertwin-based multiaccess edge computing system that includes user mobility, migration of virtual servers, multiple physical servers at different network tiers, fronthaul and backhaul communication, processing, and content request/caching. We also propose algorithms for guiding the operation of Cybertwins and the control plane in a multiaccess edge computing scenario. Finally, a performance analysis between Cybertwin and a virtual machine-based scheme is offered. Simulations show that Cybertwin brings significant improvement for the assumed scenario in the form of a faster overall service due to the higher cooperation. The models and simulations here were designed with the characteristics of future networks, beyond the current 5G, in mind, making them likely relevant for future networks, where multiaccess edge computing and the Internet of Things should play an even more important role.

A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.