Tier Network Research Articles

Distributed Ledger Technology Review and Decentralized Applications Development Guidelines

The Distributed Ledger Technology (DLT) provides an infrastructure for developing decentralized applications with no central authority for registering, sharing, and synchronizing transactions on digital assets. In the last years, it has drawn high interest from the academic community, technology developers, and startups mostly by the advent of its most popular type, blockchain technology. In this paper, we provide a comprehensive overview of DLT analyzing the challenges, provided solutions or alternatives, and their usage for developing decentralized applications. We define a three-tier based architecture for DLT applications to systematically classify the technology solutions described in over 100 papers and startup initiatives. Protocol and Network Tier contains solutions for digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scalability and Interoperability Tier solutions address the scalability and interoperability issues with a focus on blockchain technology, where they manifest most often, slowing down its large-scale adoption. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design and implementation of traditional systems.

Characterization of base station deployment distribution and coverage in heterogeneous networks

Considering different types of base stations (BSs) in future cellular networks are overlapping deployment with the status of dense, multi-tier and heterogeneous in general, how to optimize the real BS deployment becomes a complicated problem. Based on it, repulsive BS dataset and clustering BS dataset are statically characterized with various types of spatial point processes. It shows that the improvement of coverage probability between 1-tier and 2-tier network fluctuates as the signal-to-interference ratio (SIR) threshold increases for different datasets. The authors' proposed hybrid model fits well with repulsive BS dataset, while the log-Gaussian Cox point process (LGCP) and Cauchy model are reasonable models for clustering BS dataset. Besides, in order to dynamically analyze the coverage problem affected by adding new BSs, a cell boundary constructed by an irregular circle is introduced under an equal SIR constraint, and a BS placement scheme is proposed to place new BSs at the points of minimum interference. Numerical results show that the coverage probability may increase after adding BSs in the target area of heterogeneous network by using the proposed scheme. However, as the density of femto BS reaches a certain value, its coverage may remain unchanged even after adding more femto BSs.

Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment

The current agriculture systems compete to take advantage of industry advanced technologies, including the internet of things (IoT), cloud/fog/edge computing, artificial intelligence, and agricultural robots to monitor, track, analyze and process various functions and services in real-time. Additionally, these technologies can make the agricultural processes smarter and more cost-efficient by using automated systems and eliminating any human interventions, hence enhancing agricultural production to meet future expectations. Although the current agriculture systems that adopt the traditional cloud-based architecture have provided powerful computing infrastructure to distributed IoT sensors. However, the cost of energy consumption associated with transferring heterogeneous data over the multiple network tiers to process, analyze and store the sensor's information in the cloud has created a huge load on information and communication infrastructure. Besides, the energy consumed by cloud data centers has an environmental impact associated with using non-clean fuels, which usually release carbon emissions (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) to produce electricity. Thus, to tackle these issues, we propose a new integrated edge-fog-cloud architectural paradigm that promises to enhance the energy-efficient of smart agriculture systems and corresponding carbon emissions. This architecture allows data collection from several sensors to process and analyze the agriculture data that require real-time operation (e.g., weather temperature, soil moisture, soil acidity, irrigation, etc.) in several layers (edge, fog, and cloud). Thus, the real-time processing could be held by the edge and fog layers to reduce the load on the cloud layer, which will help to enhance the overall energy consumption and process the agriculture applications/services efficiently. Mathematical modeling is conducted using mixed-integer linear programming (MILP) for a smart agriculture environment, where the proposed architecture is implemented, and results are analyzed and compared to the traditional implementation. According to the results of thousands of agriculture sensors, the proposed architecture outperforms the traditional cloud-based architecture in terms of reducing the overall energy consumption by 36% and the carbon emissions by 43%. In addition to these achievements, the results show that our proposed architecture can reduce network traffic by up to 86%, which can reduce network congestion. Finally, we develop a heuristic algorithm to validate and mimic the presented approach, and it shows comparable results to the MILP model.

Joint Gateway Selection and Resource Allocation for Cross-Tier Communication in Space-Air-Ground Integrated IoT Networks

Leveraging unmanned aerial vehicles (UAVs) as relays, the space-air-ground integrated Internet of Things (SAG-IoT) networks can provide wireless access services to the connected devices with high data rate, high flexibility, and wide coverage. However, besides bringing lots of benefits, the SAG-IoT systems also face many challenges due to the high mobility, unreliable satellite links, and limited resources. Especially, when multiple UAVs act relays for cross-tier communication to transfer the collected data from ground network tier to the satellite tier, how to select an appropriate number of UAVs as gateways to improve system spectral efficiency (SE) is an important issue. To the best of our knowledge, it is an entirely new problem since most existing works utilize all UAVs as relay nodes and neglect the spectrum allocation. Toward this end, we present this paper to discuss the joint optimization problem of gateway selection, bandwidth allocation, and UAV deployment with the objective of maximizing the system SE. A Dinkelbach method based iterative algorithm is proposed as our solution by alternately adopting simulated annealing and successively convex programming technologies. Experimental results show that our designed joint optimization schemes are able to significantly improve the system SE and can make optimal decisions of gateway selection, bandwidth allocation, as well as UAV deployment.

A Remote Access Security Model based on Vulnerability Management

Information security threats exploit vulnerabilities in communication networks. Remote access vulnerabilities are evident from the point of communication initialization following the communication channel to data or resources being accessed. These threats differ depending on the type of device used to procure remote access. One kind of these remote access devices can be considered as safe as the organization probably issues it to provide for remote access. The other type is risky and unsafe, as they are beyond the organization’s control and monitoring. The myriad of devices is, however, a necessary evil, be it employees on public networks like cyber cafes, wireless networks, vendors support, or telecommuting. Virtual Private Network (VPN) securely connects a remote user or device to an internal or private network using the internet and other public networks. However, this conventional remote access security approach has several vulnerabilities, which can take advantage of encryption. The significant threats are malware, botnets, and Distributed Denial of Service (DDoS). Because of the nature of a VPN, encryption will prevent traditional security devices such as a firewall, Intrusion Detection System (IDS), and antivirus software from detecting compromised traffic. These vulnerabilities have been exploited over time by attackers using evasive techniques to avoid detection leading to costly security breaches and compromises. We highlight numerous shortcomings for several conventional approaches to remote access security. We then adopt network tiers to facilitate vulnerability management (VM) in remote access domains. We perform regular traffic simulation using Network Security Simulator (NeSSi2) to set bandwidth baseline and use this as a benchmark to investigate malware spreading capabilities and DDoS attacks by continuous flooding in remote access. Finally, we propose a novel approach to remote access security by passive learning of packet capture file features using machine learning and classification using a classifier model.

UAV-Assisted Networks Through a Tunable Dependent Model

In existing works of performance analysis of unmanned aerial vehicle (UAV) assisted networks, the location dependence between macro base stations (MBSs) and UAVs has not been considered. Considering UAV's particular agility, this letter proposes a tunable model for dependent deployment, and analyzes the performance of the setup via stochastic geometry. Specifically, to avoid the strong mutual interference between UAVs and MBSs, UAVs are only deployed in the macro cell boundaries, of which area fraction can be adjusted. In addition, we obtain a lower bound on the coverage probabilities (CPs) of MBS users and CP expression of UAV users. The results show that the CPs of UAV users in cell boundaries are significantly increased about 51% by the selective addition of UAVs, when signal to interference ratio (SIR) threshold equals 0dB, compared with the scenario of a single MBS tier network.

C-share

Integrating optical circuit switches in data-centers is an on-going research challenge. In recent years, state-of-the-art solutions introduce hybrid packet/circuit architectures for different optical circuit switch technologies, control techniques, and traffic re-routing methods. These solutions are based on separated packet and circuit planes that cannot utilize an optical circuit with flows that do not arrive from or delivered to switches directly connected to the circuit's end-points. Moreover, current SDN-based elephant flow re-routing methods require a forwarding rule for each flow, which raises scalability issues. In this paper, we present C-Share - a scalable SDN-based circuit sharing solution for data center networks. C-Share inherently enables elephant flows to share optical circuits by exploiting a flat top-of-rack tier network topology. C-Share is based on a scalable and decoupled SDN-based elephant flow re-routing method comprised of elephant flow detection, tagging and identification, which is utilized by using a prevalent network sampling method (e.g., sFlow). C-Share requires only a single OpenFlow rule for each optical circuit, and therefore significantly reduces the required OpenFlow rule entry footprint and setup rule rate. It also mitigates the OpenFlow outbound latency for subsequent elephant flows. We implement a proof-of-concept system for C-Share based on Mininet, and test the scalability of C-Share by using an event-driven simulation. Our results show a consistent increase in the mice/elephant flow separation in the network, which, in turn, improves both network throughput and flow completion time.

Robust Rate-Maximization Precoder Design for VFDM System

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique.

Simultaneous wireless information and power transfer in heterogeneous cellular networks with underlay D2D communication

Device to device (D2D) communication that provides high data rate proximity based direct communication between users, along with simultaneous wireless information and power transfer (SWIPT) that extracts energy from the received RF power, can achieve high energy and spectral efficiencies together with low latency communication. Heterogeneous networks, that employ efficient frequency reuse, provide high gains in the coverage and capacity of the cellular networks. SWIPT helps in converting the harmful interference, incurred by the frequency reuse of D2D tier underlaying the Hetnet, into energy that can be stored for the future. This work presents a resource allocation and power control scheme for SWIPT enabled underlay D2D networks, designed to improve the energy efficiency and the amount of energy harvested while ensuring the minimum required data rates for the users. Resource allocation is performed using a stable many-to-one matching game model inspired from Gale-Shapley algorithm. Two techniques of SWIPT, namely power splitting SWIPT and time splitting SWIPT, are considered and the resource allocation schemes are designed for both. Simulation results demonstrate the superior performance of the proposed algorithm compared to an existing work in single tier network with power splitting SWIPT. The work is extended to a typical 5G HetNet scenario, and extensive simulations are done to compare the performance of the two SWIPT architectures in the HetNet scenario.

Interference management for D2D‐enabled HetNets with imperfect channel estimation

Device-to-device (D2D) and heterogeneous networks (HetNets) are two promising technologies to be introduced in the upcoming 5G network, for improving spectral efficiency and decreasing latency. However, sharing the same spectrum resources among different tiers of HetNet raised the interference challenge. In this paper, cross-tier interference management for underlay D2D users is studied in a heterogeneous macro-small cell network assuming imperfect channel state information (CSI). The network consists of one macro-cell with a centered macro-base Station (MBS) and several small-cell base stations (SCBS). Two types cellular users are considered; macro-cell cellular user equipment (MUE) associated with the MBS, and small-cell cellular user equipment (SUE) linked to SCBS. While two types of D2D users are considered; macrocell D2D (MD) located outside the range of the small cells and cross-tier D2D user equipment (CD), where each user of the D2D pair belongs to different network tier. Closed form expressions for the D2D optimal power that aim to maximize the coverage probability and the signal-to-interference-noise ratio (SINR) of both links are derived, while guaranteeing the quality of service (QoS) requirements for the cellular user. The effect of channel estimation error on the performance of both D2D and cellular users is evaluated.

On Jamming and Black Hole Attacks in Heterogeneous Wireless Networks

The proliferation of radio-enabled equipment, including sensors, controllers, actuators, mobile handhelds, etc., into the today's heterogeneous wireless network (HWN) has led to the support of different vertical services on-top of the same physical infrastructure. Such an approach increases the potential for enhanced multiplexing gains due to the joint utilization of multiple network tiers and their resources. However, it also links the availability of new services to the robustness of the HWN infrastructure against malicious attacks. In this paper, we study the impact of two prominent denial of service (DoS) attacks, the jamming and the black hole attack, on network coverage of multi-tier HWNs. In particular, we consider a HWN model of multiple tiers, where the locations of nodes belonging to each tier are modeled by a homogeneous Poisson point process (PPP) with known intensity. Each tier consists of regular nodes, jammers (disrupting downlink communications by transmitting at constant power), and black holes (appearing as regular nodes but without forwarding received packets to their legitimate destination). The type of a node for a given network tier is determined by a prescribed probability, whereas jammer and black hole nodes are considered to act independently from each other. We further focus on the challenging scenario where end users can access a limited number of network tiers, while we also allow different tiers to potentially utilize different spectrum bands. Assuming that successful service reception from a node belonging to a given network tier requires a minimum received signal quality threshold to be attained, we derive exact expressions and performance bounds on the coverage probability of random multi-tier HWNs with joint jamming and black hole attacks, which depend on the capability of network nodes to detect and avoid association with malicious nodes (i.e., jammers or black holes). Detailed numerical results highlight the usefulness of the proposed analysis, providing valuable insights on system design and parameterization towards enhanced network robustness.

Handover Rate Characterization in 3D Ultra-Dense Heterogeneous Networks

Ultra-dense networks (UDNs) envision the massive deployment of heterogeneous base stations (BSs) to meet the desired traffic demands. Furthermore, UDNs are expected to support the diverse devices e.g., personal mobile devices and unmanned aerial vehicles. User mobility and the resulting excessive changes in user to BS associations in such highly dense networks may however nullify the capacity gains foreseen through BS densification. Thus, there exists a need to quantify the effect of user mobility in UDNs. In this paper, we consider a three-dimensional $N$ -tier downlink network and determine the association probabilities and inter/intra tier handover rates using tools from stochastic geometry. In particular, we incorporate user and BSs’ antenna heights into the mathematical analysis and study the impact of user height on the association and handover rate. The numerical trends show that the intra-tier handovers are dominant for the tiers with shortest relative elevation w.r.t. the user and this dominance is more prominent when there exists a high discrepancy among the tiers’ heights. However, biasing can be employed to balance the handover load among the network tiers.

A Novel Performance Tradeoff in Heterogeneous Networks: A Multi-Objective Approach

In this letter, we investigate the performance tradeoff between different tiers of heterogeneous networks (HetNets) consisting of a macrocell as the basic tier and small cells (e.g., picocells, femtocells, etc.) as the other tiers. Due to the interference among tiers in the HetNets, the performance of different cells are interactive. Maximizing the total performance in terms of data rate and energy efficiency (EE) of users in HetNets are both desirable system design objectives, however, it is expected that there is a non-trivial tradeoff between tiers due to the co-channel deployment. Thus, we formulate our resource allocation problem as a multiobjective optimization problem (MOOP) in which the performance of the macrocell and small cells are jointly maximized. Using the weighted Tchebycheff technique, we are able to transform the MOOP into a single objective optimization problem (SOOP). Then, an iterative algorithm is proposed to solve this SOOP which yields a locally optimal solution. Finally, our numerical results show this tradeoff among different tiers.

Joint Two-Tier Network Function Parallelization on Multicore Platform

As network function virtualization (NFV) is realized based on general-purpose processors for avoiding proprietary hardware, its benefits of flexibility and agility could be compromised by the increased packet latency and reduced throughput. An effective approach for improving the latency and throughput performance is to exploit new network function (NF) processing framework on general purpose processors. In this paper, we propose a new joint two-tier NF parallelization (TNP) framework, which can agilely and flexibly organize parallel NF processing to greatly improve the latency and throughput performance of service function chain (SFC) which is constituted by a set of NFs. In TNP, we jointly organize the parallelization of multiple NFs at the service tier and perform multicore mapping of individual NFs at the substrate network tier. We formulate the optimal TNP design problem as minimizing link bandwidth consumption subject to end-to-end latency and computing resource constraints. We solve the problem by decomposing it into two easier subproblems: 1) subproblem 1 (SP1) is to solve the optimal SFC parallelization graph design in conjunction with link mapping problem and 2) subproblem 2 (SP2) is to jointly solve computing resource allocation in conjunction with node mapping problem. The global optimal solution is accomplished by searching in a set of feasible regions in sequence. Numerical results demonstrate that our proposed TNP can significantly decrease service latency and improve network throughput compared with known single layer NF parallelization schemes. Moreover, the link bandwidth utilization and SFC request acceptance rate in the substrate network can also be greatly improved.

Distance based thresholds for 2-tier relay nodes selection in WSN

Relay selection technique approaches are increasingly used to improve the reliability of Wireless Sensor Networks (WSNs) communication, thus providing energy efficiency and reducing energy consumption. Due to the adoption of direct transmission from Cluster Heads (CHs) to the Base Station (BS), faster energy depletion may arise. Hence, the deployment of proper relay nodes techniques in WSN is a crucial task. This paper proposed, implemented and evaluated a new technique for the selection of relay nodes according to the nearest distance with Base Station (BS) in a 2- tier network. The selection was performed using K-Optimum that target the selection of the nearest number of relay nodes and, at the same time, ensure that all CHs are connected to at least one corresponding relay node based on K-Mean approach. Simulation results show that the distance based threshold for relay nodes selection implemented in Relay Access Protocol (RAP) performs better than the Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) protocols in terms of First Node Dead (FND), Last Node Dead (LND) and network lifetime.

Edge and Fog Computing in Healthcare – A Review

The architectural framework of Fog and edge computing reveals that the network components which lie between the cloud and devices computes application oriented operations. In this paper, an in-depth review of fog and mist computing in the area of health care informatics is analyzed, classified, and discussed various applications cited in the literature. For that purpose, applications are classified into different categories and a list of application-oriented tasks that can be handled by fog and edge computing are enlisted. It is further added that on which layer of the network system such fog and edge computing tasks can be computed and trade-offs with respect to requirements relevant to healthcare are provided. The review undertaken in this paper focuses on three important areas: firstly, the enormous amount of computing tasks of healthcare system can take mileage of these two computing principles; secondly, the limitation of wireless devices can be overcome by having higher network tiers which can execute tasks and aggregate the data; and thirdly, privacy concerns and dependability prevent computation tasks to completely move to the cloud. Another area which has been considered in the study is how Edge and Fog computing can make the security algorithms more efficient. The findings of the study provide evidence of the need for a logical and consistent approach towards fog and mist computing in healthcare system.

A Comparative Study of Resource Allocation Schemes for D2D Networks Underlay Cellular Networks

Device-to-Device (D2D) communication is a promising take over in cellular communication. Due to its advantages it gains significance in the industrial standardization along with academic researchers. The concept of two tier network can be evolved to enlighten the cellular communication. Making each cellular user as transmission relays it would form a massive D2D network resulting in increased offloading of cellular traffic. The scope of D2D also extends to mobile cloud computing, proximity applications, social network content distribution and so on. It has been proved to be the best solution for energy efficient, spectral efficient technology which meets the capacity demand. However, the D2D scheme have practical challenges which needs to be met in order to keep its place primary in the next generation wireless communication. One of the major challenges which has to be addressed and resolved is resource allocation. In this paper various resource allocation scheme for D2D networks in cellular network are discussed. Each of the resource allocation with its signal to interference ratio performance with the other and the sum-data rate of each system is compared. D2D assisted Cognitive radio networks (Ahmad et al. in IET Commun 12(10): 1207–1214, 2008) can be developed to utilise the spectrum opportunistically for next generation communication systems.

Inter-Network Localization Frameworks for Heterogeneous Networks With Multi-Connectivity

The ubiquity of many different networking options with diversified wireless radio access technologies creates an exciting opportunity to use heterogeneous networks (HetNets) for localization systems. Toward this motivation, in this paper, we propose two different inter-network localization frameworks based on maximum likelihood (ML) estimator for HetNets with multi-connectivity by considering the propagation characteristics of individual network tiers in a realistic manner. The first is inter-network noncooperative (INN) based on the weighted superposition of the location estimates of individual network tiers along with their reliability. The second is inter-network cooperative (INC) grounded on the joint utilization of raw received power measurements from all network tiers. We also derive Cramér-Rao bound (CRB) expressions for both frameworks as a theoretical performance benchmark. Furthermore, we obtain another performance criteria called as spatial quantization error bound to analytically determine the effect of grid search algorithm, which is used for solving the INN and INC ML cost functions, on localization performance. Extensive simulations validate our proposed localization frameworks.

Degrees of Freedom of 2-Tier Networks Without Channel State Information at the Transmitter

We characterize the degrees of freedom (DoF) of the two-tier heterogeneous networks without channel state information at the transmitters or cooperation among transmitters. We consider conventional transmitters while the users are equipped with a multimode receiver. In contrast to previous works focused on two-tier networks, the derived DoF consider the management of the intracell, the intercell, and intertier interference. The derived bounds define the DoF region for any two-tier network. Furthermore, these bounds are achievable by combining blind interference alignment schemes properly.

Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs

Different from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs’ performance by applying machine learning techniques and genetic algorithms. Using the $K$ -means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives’ dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to state-of-the-art algorithms at an acceptable computational complexity.