Performance of LTE network for VoIP users

With the arrival of LTE standard, it is expected that the mobile voice services paradigm will shift from the circuit switched to fully packet switched mode supporting the VoIP services. VoIP services took quite a bit of time before they were accepted as the main stream telephony service in the fixed networks. To provide VoIP services over the LTE networks with appropriate QoS, it is necessary to analyse the performance of such services and optimise the network parameters. This paper analyses the performance of VoIP services on the LTE network using the FD and the SMP packet scheduling techniques. This work identifies and analyses the features of above LTE packet scheduling techniques to enhance the QoS of VoIP services. An OPNET-based simulation model is used to analyse the performance of VoIP services on the LTE network by incorporating G.711 and G.723 speech coders. The work also studied the performance of VoIP services in variable transmission channel conditions

An enhanced block validation framework with efficient consensus for secure consortium blockchains

Consortium blockchains have attracted considerable interest from academia and industry due to their low-cost installation and maintenance. However, typical consortium blockchains can be easily attacked by colluding block validators because of the limited number of miners in the systems. To address this problem, in this paper, we propose a novel block validation framework to enhance blockchain security. In the framework, the block validations are assisted and implemented by various lightweight nodes, e.g., edge devices, in addition to the typical blockchain miners. This improves the blockchain security but can cause an increased block validation delay and, thereby, reduced blockchain throughput. To tackle this challenge, we propose an effective method to select lightweight nodes based on their computing powers to maximize the blockchain throughput, and prove the uniqueness of the optimal nodes selection strategy. Security analysis and simulation results from the deployed consortium blockchain platform show that the proposed framework achieves higher throughput and security than the existing consortium blockchain models

Throughput-efficient blockchain for Internet-of-Vehicles

Internet-of-Vehicle (IoV) is empowering smart vehicles with data collection and sharing capabilities, and blockchains have been introduced to manage the IoV data due to many advantages, including decentralization, security, reliability, and scalability. Nevertheless, existing IoV blockchain models suffer from poor security against collusion attacks instigated by malicious blockchain miners typically represented by roadside units (RSUs). To address this problem, additional block verifiers, e.g., vehicles, can be recruited during block verification, which enhances security but also can lead to the reduced throughput. Therefore, in this paper, we propose a resource management scheme for IoV blockchains to enhance the system security while maximizing the throughput by optimizing contributed computing resources from RSUs and recruited vehicles. We show that the optimal strategies of RSUs and vehicles can be found through the Karush-Kuhn-Tucker (KKT) conditions and verify (using simulations) that our scheme achieves the higher throughput with enhanced security compared to the existing IoV blockchains

Lagrange coded federated learning (L-CoFL) model for Internet of Vehicles

In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches

QoS-Oriented Mode, Spectrum, and Power Allocation for D2D Communication Underlaying LTE-A Network

This paper investigates the problem of resource allocation for device-to-device (D2D) communication in a ThirdGeneration Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) network. The users in the network can operate either in a traditional cellular mode, communicating with each other via the evolved NodeB (eNB), or in a D2D mode, communicating with each other without traversing the eNB. In the considered model, the D2D users and cellular users share the same radio resources. Particularly, each resource block (RB) within the available bandwidth can be occupied by one cellular and several D2D users. Hence, the problem of interference management is crucial for effective performance of such a network. The twofold aim of the proposed algorithm is to 1) mitigate the interference between cellular and D2D users and 2) improve the overall user-perceived quality of service (QoS). To control the interference, for each user, we define a certain target interference level and constrain the interference from the other users to stay below this level. The corresponding optimization problem maximizes the QoS of the users by minimizing the size of the buffers of user equipments (UEs). The performance of the algorithm has been evaluated by using the OPNET-based simulations. The algorithm shows improved performance in terms of mean packet end-to-end delay and loss for UEs when compared to other relevant schemes

Effective resource block allocation procedure for quality of service provisioning in a single-operator heterogeneous LTE-A network

We consider the problem of resource block (RB) allocation in the integrated pico/macrocell Long Term Evolution - Advanced (LTE-A) network. It is assumed that the network is controlled by a single service provider (SP) and all the operation of the picocells is coordinated with a macro-network. To improve the quality of service (QoS) for end-to-end applications, we take into account the individual traffic demands of the users and allocate the RBs to minimize the sum of user utilities which are expressed in terms of the size of their queues. The formulated RB allocation problem belongs to the family of the multiple knapsack problems (MKPs) and, therefore, it is non-deterministic polynomial time (NP) hard in the strong sense. To reduce the complexity of this problem, we propose a simple heuristic technique to find the suitable (but not necessarily optimal) solution. The proposed RB allocation procedure requires only two additional signalling steps (necessary to maintain the coordination among different cells) and, therefore, its impact of the control signalling overhead is neglectable. It was shown (using OPNET-based simulations) that the proposed technique has low complexity, fast solution time, and shows improved performance when compared to other relevant schemes. (C) 2016 Elsevier B.V. All rights reserved

Dynamic Buffer Status-Based Control for LTE-A Network With Underlay D2D Communication

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier

Joint Bandwidth and Power Allocation for LTE-Based Cognitive Radio Network Based on Buffer Occupancy

We investigate the problem of resource allocation in a cognitive long-term evolution (LTE) network, where the available bandwidth resources are shared among the primary (licensed) users (PUs) and secondary (unlicensed) users (SUs). Under such spectrum sharing conditions, the transmission of the SUs should have minimal impact on quality of service (QoS) and operating conditions of the PUs. To achieve this goal, we propose to assign the network resources based on the buffer sizes of the PUs and SUs in the uplink (UL) and downlink (DL) directions. To ensure that the QoS requirements of the PUs are satisfied, we enforce some upper bound on the size of their buffers considering two network usage scenarios. In the first scenario, PUs pay full price for accessing the spectrum and get full QoS protection; the SUs access the network for free and are served on a best-effort basis. In the second scenario, PUs pay less in exchange for sharing the bandwidth and get the reduced QoS guarantees; SUs pay some price for their access without any QoS guarantees. Performance of the algorithms proposed in the paper is evaluated using simulations in OPNET environment. The algorithms show superior performance when compared with other relevant techniques