Within the rapidly expanding Internet of Vehicles (IoV) landscape, the demand for network services to accommodate data-intensive applications has become increasingly paramount. However, IoV faces significant challenges, including high latency, limited coverage in remote areas, network congestion, and privacy concerns in content popularity prediction. To address these challenges, we introduce the Scalable Optimisation for Networked Aerial-vehicles (SONA) scheme, which reduces latency through advanced caching techniques while enhancing coverage and ensuring required data rates using Unmanned Aerial Vehicles (UAVs). UAVs are deployed to augment coverage in areas lacking RoadSide Unit (RSU) support and to assist in scenarios where RSUs are overwhelmed, ensuring continuous data rate provision. Our scheme introduces a novel mathematical optimisation model and machine learning algorithms: Federated Learning (FL) to collaboratively predict content popularity without exposing user data, Reinforcement Learning (RL) to dynamically optimise UAV placement and energy-efficient deployment, and blockchain to enable secure, decentralized coordination between RSUs and UAVs for real-time decision-making. Simulation results demonstrate the effectiveness of our approach, achieving an average delay of 8 ms, an average cache hit rate of 88.43%, and satisfying desired data rate requirements in 84.85% of scenarios, significantly improving IoV performance.

Industry 4.0 applications require communication networks with deterministic and bounded latency. Ethernet, augmented with the IEEE Time-Sensitive Networking (TSN) standards, can meet these requirements. One of the TSN standards, Time Aware Shaper (TAS) is ideal for periodic time-sensitive traffic in wired TSN networks. However, it relies on network-wide synchronization to a common reference time. Existing solutions for developing TAS schedules assume perfect synchronization or consider a worst-case error. This study highlights the need for using network-derived time synchronization parameters and errors to optimize TAS schedules. It raises the importance of accurate time synchronization information and proposes a novel methodology for efficient band-width utilization while ensuring deadline requirements are met for time-sensitive traffic in realistic networks. The effectiveness of the proposed framework is validated by simulating a TSN network in OMNeT++ and comparing the devised TAS schedules with schedules which use existing assumptions.

To attract more users, mobile network operators may offer both chargeable/metered and free services as parts of their product bundles. However, an extra cost of providing the free services and their substitution effect on metered services in the bundles may reduce their profitability. Therefore, the mobile network operators must decide on including free services in their offered bundles based on the profitability. In this paper, we develop an analytical framework composed of leader-follower game models to obtain the Stackelberg equilibrium strategy of the mobile network operators and analyze several important properties of the equilibrium. The proposed framework takes into account the congestion effect resulting from incorporating free services in the bundles. The game models are general and capable of analyzing different setting of bundles. Extensive performance evaluations show that the inclusion of free services can improve the profit of the mobile network operators when (i) the users' interest for the free services is above a certain value, (ii) the free services add small cost to the bundle, and (iii) the users' demand of free services is less dependent on that of metered services in the bundle.

Exploiting Content-Centric Networking (CCN) caching capabilities, in which contents are cached on intermediate nodes can be beneficial in IoT, as it can decrease the latency, reduce required transmission hops, limit traffic load on content producer and improves availability. This article presents a new scheme for caching the contents in IoT environments. In the proposed method, devices are grouped into clusters in which the cluster heads act as the cache controller. In addition, we consider a global SDN/cache controller which is responsible for orchestrating cache decisions in the whole IoT network. This central management of the caching system improves the efficiency of resource usage in the IoT network. In the proposed scheme caching, decision is made in two steps: 1) determining the candidate contents and 2) determining the candidate nodes. In each step, several metrics are taken into account, and the Analytical Hierarchy Process (AHP) is used as a Multi-Criteria Decision Making (MCDM) approach. Simulation results demonstrate that our proposed caching method can achieve up to 40% more cache hit and at the same time reduce the content retrieval time.

Connectivity is a fundamental requirement for vehicular ad hoc networks (VANETs) to secure reliable information dissemination. Connectivity is not guaranteed in the case of traffic sparsity and low market penetration of networked vehicles. Therefore, it is essential to examine the connectivity condition before deploying VANETs. The probabilistic distribution of intervehicle spacing plays a crucial role in the study of connectivity. It is quite often in previous studies to assume a priori distribution. This paper has studied this issue analytically and proved a general result as follows. A Poisson vehicle flow of volume λ enters a road stretch over the period [0, ∞), with the speed of each vehicle sampled from a common probability distribution of the density function fV (v); then, in the steady state, the number of vehicles within any road section [x1, x2] at any time instant t > 0 is Poisson distributed with the parameter λ(x2- x1) f∞01/V f(v)dv. This theoretical result is also con1 0 firmed with extensive simulation studies.

Zero-rating is the concept of not counting data usage of end-users for accessing the basic functions of some websites via mobile network operators (MNOs). Recently, some zero-rating platforms (e.g. Internet.org) are formed allowing the websites present their contents as zero-rated. In current setup of zero-rating platforms, no monetary reward is transferred to partner MNOs. Therefore, MNOs face a question of whether to cooperate with zero-rating platforms or not. In this paper, a variant of Stackelberg game is used to model the interaction between an MNO and its end-users toward providing their access to zero-rating services (i.e. partnership of the MNO and zero-rating platforms). We propose a bundle pricing scheme for the MNO and study the effects of including zero-rating services in the bundles. The proposed model assists the MNO to make decision on including zero-rating services in its bundles as well as appropriate pricing scheme. Numerical studies show that depending on the end- users' preferences, the MNO may increase its profit by including zero-rating services in its bundles.

Optical Packet Loss Rate (OPLR) is a critical performance measure for Optical Packet Switched (OPS) networks. In this paper, we use a common Artificial Neural Network (ANN), that is multilayer perceptron (MLP), as a fast OPLR estimator for bufferless slotted OPS networks. Based on the proposed estimator, an algorithm called OPLRC is proposed to control Optical Flow's Packet Loss Rates (OFPLRs) which are OPLRs for optical packets transmitted from different ingress switches. OPLRC is a greedy algorithm which uses estimated OFPLRs of the trained MLP to tag some optical packets at ingress switches as eligible for drop at core switches in case of contention. This will control OFPLRs of un-tagged optical packets at the specified limits while giving some chance for tagged optical packets to reach their destinations. The accuracy of the proposed estimator is analyzed using numerical methods and extensive simulations. Results show that OPLRC can manage OFPLRs of untagged optical packets with an acceptable accuracy.

Abstract This paper introduces a new vehicle localization approach for global positioning system‐challenged road segments (e.g., tunnels), which takes advantage of roadside units (RSUs) and in‐vehicle inertial navigation system (INS). In the proposed approach, namely RSU/INS‐aided localization system (RIALS), vehicles only need one RSU in their transmission range for an accurate positioning. The beacons received from the RSU along with the information provided by the INS system are used for establishing and maintaining particular locus circles. After linearization, the system of locus circle equations is solved using the linear least square estimation technique, and the estimated vehicle's position is obtained. In the presence of speed variations and existing ranging and INS estimation errors, the proposed RIALS adaptively sets the sufficient number of required locus circles, aiming at keeping the localization error below a given threshold. We study the effects of ranging and INS estimation errors on localization accuracy of RIALS from analytical and simulation perspectives. Results of extensive simulations show that the localization error is more sensitive to the ranging error than the INS error. Moreover, the network traffic overhead of the proposed method is considerably lower than other competitive localization approaches. Copyright © 2015 John Wiley & Sons, Ltd.

In this study, an efficient handover algorithm based on the received signal strength (RSS) prediction is presented for two‐tier macro–femtocell networks in which, because of the fading effects of channel and short coverage range of femtocells, ping‐pong handovers may take place. In the proposed approach, first each mobile station (MS) uses the recursive least square algorithm for predicting the RSS from the candidate base stations (BSs) including both femtocell and macrocell BSs. Then, according to the predicted RSS values, several future values of signal‐to‐interference plus noise ratio (SINR) are calculated. Afterwards, the candidate list of BSs is pruned according to the estimated future SINR values and the predicted RSS of each BS. Finally, the target BS which yields the highest throughput, is opted for handover. Through extensive simulations, the effects of speed of MSs and the density of femtocell BSs on the outage probability (OP), throughput and the ping‐pong rate of MSs are studied. The results show that the proposed handover algorithm outperforms the previous ones and improves the throughput of MS while it reduces the OP and the number of ping‐pong handovers.

Data communication in Vehicular Ad hoc NETworks (VANETs) tends to be an important challenge mainly due to intermittent connectivity and MAC layer collisions. Such issues hinder reliable and in-order reception of data packets and hence lead to poor performance of some applications like file transferring. In this paper we propose a file exchange scheme based on Fountain coding aiming at improving throughput of the network. In the proposed approach there is no need of in-order reception of packets, thus, vehicles can make benefit of opportunistic delivery of data. As a result we use UDP in contrast to classical use of TCP while reliability is still preserved. Results of extensive simulations show that the proposed approach increases the number of successful file exchanges as well as the average throughput of each vehicle. It also facilitate resumable downloads. Therefore it can be a good candidate for comfort applications in VANETs which mainly rely on file (or any sort of bulk data) exchange.