WiFi Offloading Research Articles

Wi-Fi offloading on mobile data communication in the office, the measurement study

Wi-Fi offloading on mobile data communication in the office, the measurement study

Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload

The amalgamation of heterogeneous generations of mobile cellular networks around the globe has resulted in diverse data speed experiences for end users. At present, there are no defined mechanisms in place for subscribers of a mobile network operator (MNO) to use the services of third-party WiFi providers. MNOs also have no standardized procedures to securely interact with each other, and allow their subscribers to use third-party services on a pay-as-you-go basis. This paper proposes a blockchain-enabled offloading framework that allows a subscriber of a mobile operator to temporarily use another MNO or WiFi provider’s higher-speed network. A smart contract is employed to allow diverse entities, such as MNOs, brokers and WiFi providers, to automatically execute mutual agreements, to enable the utilization of third-party infrastructure in a secure and controlled manner. The proposed framework is tested using Ethereum’s testnet on the Goerli network using Alchemy and Hardhat. The analysis of the results obtained shows that the proposed technique helps mobile operators to offer improved user experience in the form of average speed and latency. The experiments show that the average time taken to deliver a 500 MB file is reduced from 10.23 s to 0.91 s for the global average scenario, from 6.09 s to 0.50 s for 5G, from 13.50 s to 0.50 s for 4G-LTE, from 41.11 s to 0.49 s for 4G, and from 339.11 s to 0.49 s for the 3G scenario. The results also show that, with WiFi offloading, users from all cellular generations can enjoy a similar quality of services, because delivery time ranges from 0.49 s to 0.91 s for offloaded experiments whereas for the non-offloaded scenario it ranges from 6.09 s to 339.11 s.

Device-to-device Communications at the Terahertz Band: Open Challenges for Realistic Implementation

One of the key parameters that plays a major role in enabling the data rate requirements is spectrum or bandwidth, which is scarce and expensive. Therefore, spectrum management policies are required to optimize its usage to meet all the requirements and the promised data rate growth. Another strategy to deal with spectrum scarcity is to move toward higher frequency bands (terahertz bands), which are expected in the next 6G communication standard. It is therefore important to develop not only new techniques that enable efficient dynamic spectrum access and sharing at such bands, but also suitable channel models for the terahertz bands. Meanwhile, offloading mechanisms are very promising for cellular networks where a plethora of options have been proposed in the research arena in terms of device-to-device, licensed assisted access, or WiFi offloading, among others; but their behavior, when operated at high frequencies (terahertz band) remains unclear. Therefore, this article will tackle two technologies that will shape future networks: terahertz channel modeling/communications and offloading mechanisms.

Proposal and Simulation with NS3 of a New offloading Algorithm between LTE/LTE-Advanced and Wi-Fi

The field of mobile telecommunications is an integral part of our society. In the wake of IT, it is implemented and used in all areas of society. This deep anchoring, which mobile networks use today, poses significant problems for operators in terms of speed, capacity demands and customer satisfaction, particularly for the quality of services, but also in terms of profitability and cost of services investments. Reports from Ericsson and Cisco which are representative bases for calculating world total data traffic in cellular mobile networks predict that the number of devices connected to IP networks will be more than three times the global population by 2023. There will be 3.6 networked devices per capita by 2023, up from 2.4 networked devices per capita in 2018. There will be 29.3 billion networked devices by 2023, up from 18.4 billion in 2018. To answer these challenges, two choices are offered to the operators the evolution of the standards or the interconnection of several networks. In this order of idea, we carried out an 4G/4G+ -Wi-Fi offloading simulation algorithm. It was up to us to design an offloading algorithm and then to simulate through a dedicated environment in a scenario allowing to implement it. To achieve this, it was first necessary for us to understand the concept of mobile data offload, to know the types of mobile data offload and to choose one on which the present work will be based. Later we carried out an analysis of the existing data offload algorithms to be able to bring out the limits. From these limits we have designed and proposed a new traffic offloading algorithm with a different approach compared to the existing one studied in the literature. After all this, we have developed and simulated a scenario comprising a 3GPP network in this case the 4G + network, a non 3GPP network in this case the WiFi network, and we have implemented our algorithm via NS3 network simulation software. With this work, we have proposed a solution to overcome an investment deemed expensive to deploy 5G in Africa.

Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading

Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.

Does the improvement of public Wi-Fi technology undermine mobile network operators’ profits? Evidence from consumer preferences

Cellular data consumption by users is the main revenue source for mobile network operators (MNOs). On the one hand, technological development and expansion in Wi-Fi coverage can reduce cellular data usage. On the other hand, Wi-Fi offloading, which entails redirecting cellular traffic to a Wi-Fi network, can prevent network quality degradation as a result of explosive cellular traffic. Thus, the advancement and expansion of public Wi-Fi are both beneficial and detrimental for MNOs. Therefore, in this study, we analyze consumer preferences based on changes in the properties of Wi-Fi and cellular networks by employing a mixed logit model with stated preference data. The results indicate high consumer preference when the cellular and Wi-Fi networks are fast, Wi-Fi usability range is wide, and price is low. To analyze the impact of improving public Wi-Fi technology on MNOs, we conduct a scenario analysis. When the Wi-Fi service speed increases to 100 Mbps or more and its coverage expands to span an entire public transportation area, consumers reduce the amount of their monthly cell phone plan by 1347 Korean won (KRW) (1.15 USD) and 5141 KRW (4.40 USD), respectively. The results of this study can facilitate the determination of appropriate public Wi-Fi speeds, establish the quality of cellular networks by considering the effect on MNOs, and improve social welfare by expanding public Wi-Fi networks.

Revisiting WiFi offloading in the wild for V2I applications

This paper revisits the opportunities of using WiFi offloading for Vehicle to Internet (V2I) communication, and how this has changed over the last decade. With the rollouts of provider-managed WiFi networks that are more structured and operate under authenticated regimes, WiFi offloading, or use of available (roadside) WiFi networks for V2I data communication, has different opportunities and challenges. To study the current landscape,we develop a system (X-Fi), which efficiently selects, associates to, authenticates with, and performs WiFi offloading for V2I communication with these networks, and a tool (X-Perf), which illustrates opportunities of WiFi offloading available today in these networks, with measurements and experiments across four metro areas across three continents over 22 months. Our results indicate the feasibility of achieving 1 GB/hour application goodput, an order of magnitude higher than the number provided by open WiFi networks in the past, which can take a significant load away from alternative communication paths for V2I systems. Moreover, we provide several implications on transport protocols and WiFi deployments to shed light on the use of such WiFi networks for V2I communication.

Performance modeling of multipath mobile data offloading in cellular/WiFi networks with bandwidth uncertainty

Mobile data offloading is one of the most effective approaches for improving the Quality of Service (QoS) of mobile users. Through this method, the mobile data traffic load over a cellular network is mitigated by offloading some of the traffic to an alternative path; usually a WiFi network. Most previous studies on WiFi offloading have assumed that the data traffic is offloaded to a path with constant bandwidth. However, in real-world applications, it is generally impossible to perform data transmission at a constant rate through WiFi over the entire offloading process, Hence, one of the most challenging issues in analyzing the performance of mobile data offloading is that of evaluating the effects of bandwidth uncertainty on the actual transmission rate. Accordingly, the present study proposes an analytical model for predicting the performance of multipath data offloading over WiFi network with bandwidth uncertainty. The theoretical results are validated by means of numerical simulations. The effectiveness of the proposed model is then further investigated by comparing the QoS metrics of the data offloading process with those obtained in a previous study on mobile data offloading under the assumption of a constant bandwidth. Overall, as the WiFi connection times are highly larger than the WiFi disconnection times, our model removes estimated errors in the baseline model by 8.7% and 6.7% in deadline miss ratio, by 23.2% and 14.9% in average offloading volume, when we consider two and three varying WiFi bandwidth respectively. The results show that the proposed method provides mobile operators with an effective framework for maintaining the QoS level of mobile data offloading in real-world networks characterized by WiFi bandwidth uncertainty.

A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area.

Designing and analysis of a Wi-Fi data offloading strategy catering for the preference of mobile users

<p style='text-indent:20px;'>In recent years, offloading mobile traffic through Wi-Fi has emerged as a potential solution to lower down the communication cost for mobile users. Users hope to reduce the cost while keeping the delay in an acceptable range through Wi-Fi offloading. Also, different users have different sensitivities to the cost and the delay performance. How to make a proper cost-delay tradeoff according to the user's preference is the key issue in the design of the offloading strategy. To address this issue, we propose a preference-oriented offloading strategy for current commercial terminals, which transmit traffic only via one channel simultaneously. We model the strategy as a three-state M/MMSP/1 queueing system, of which the service process is a Markov modulated service process (MMSP), and obtain the structured solutions by establishing a hybrid embedded Markov chain. Our analysis shows that, given the user's preference, there exists an optimal deadline to maximize the utility, which is defined as the linear combination of the cost and the delay. We also provide a method to select the optimal deadline. Our simulation demonstrates that this strategy with the optimal deadline can achieve a good performance.</p>

Design and Evaluation of Wi-Fi Offloading Mechanism in Heterogeneous Networks

In recent years, WiFi offloading provides a potential solution for improving ad hoc network performance along with cellular network. This paper reviews the different offloading techniques that are implemented in various applications. In disaster management applications, the cellular network is not optimal for existing case studies because the lack of infrastructure. MANET Wi-Fi offloading (MWO) is one of the potential solutions for offloading cellular traffic. This word combines the cellular network with mobile ad hoc network by implementing the technique of Wi-Fi offloading. Based on the applications requirements the offloading techniques implemented into mobile-to-mobile (M-M), mobile-to-cellular (M-C), mobile-to-AP (M-AP). It serves more reliability, congestion eliminated, increasing data rate, and high network performance. The authors also identified the issue while implementing the offloading techniques in network. Finally, this paper achieved the better performance results compared to existing approaches implemented in disaster management.

An Intelligent Data Uploading Selection Mechanism for Offloading Uplink Traffic of Cellular Networks.

Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or the reduced uploading cost. In this paper, we propose an Intelligent Data Uploading Selection Mechanism (IDUSM) to realize a trade-off between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The mechanism first helps the source participant choose an appropriate data uploading manner based on the proposed probability prediction model, and then optimizes its performance objective for the chosen data uploading manner. In IDUSM, our proposed probability prediction model precisely predicts a participant’s mobility from spatial and temporal aspects, and we decrease data redundancy produced in the Wi-Fi offloading process to reduce waste of participants’ limited resources (e.g., storage, battery). Simulation results show that the offloading efficiency of our proposed IDUSM is , and the value is the highest among the other three Wi-Fi offloading mechanisms. Meanwhile, the offloading ratio and uploading cost of IDUSM are respectively 52.1% and . Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost.

Congestion-aware WiFi offload algorithm for 5G heterogeneous wireless networks

First, the key technologies of 5G heterogeneous wireless networks are introduced. Based on the 5G heterogeneous network fusion architecture, heterogeneous network technologies are used to achieve the integration between different access networks, so that they can cooperate with each other, coordinate interference, and improve spectrum resources. Utilization and transmission efficiency; At the same time, an enhanced ADNSF congestion-aware WiFi(WIreless FIdelity) offloading system suitable for 5G networks is proposed. The congestion-aware WiFi offloading system can obtain network status information in real time. Analyzed the shortcomings of 3GPP’s existing congestion-aware WiFi offload strategy, combined with the user-centric network characteristics of 5G, proposed a congestion-aware WiFi offload algorithm, and modeled the access strategy as the effective capacity In the non-cooperative game model, a distributed algorithm and a dynamic value algorithm are designed respectively to maximize network utility. Simulation shows that the proposed distributed algorithm doubles the total effective capacity of the network compared to the device full activation scheme; compared with the distributed algorithm, the proposed dynamic value algorithm improves the total effective capacity of the network. This algorithm can effectively improve user satisfaction and achieve fine management of congested WiFi offloads

An efficient SDN‐based LTE‐WiFi spectrum aggregation system for heterogeneous 5G networks

SummaryNowadays, the continuously increasing demand for high data traffic and providing different quality of services (QoS) to the customer are very challenging tasks for all network operators. In the last few years, mobile data traffic is increased to a significant extent and half of the traffic is provided through WiFi technology which is known as WiFi offloading. To overcome the increasing traffic demand, WiFi offloading is the best option to reduce the burden of cellular networks. So, by aggregating existing indoor WiFi technology to the cellular network increases the network capacity and provides better QoS to customers. In this article, we propose and implement the LTE‐WiFi aggregation system where eNodeB is responsible for the aggregation of the WiFi access point without modifying the core network. Furthermore, the proposed system is integrated with the mobile‐CORD (M‐CORD) platform which leverages software defined networking (SDN), network function virtualization (NFV), and cloud technologies for providing a 5G environment. M‐CORD platform has three main modules: service orchestrator (XOS), SDN controller ONOS, and OpenStack. One of the important features of M‐CORD is to provide virtualized core network functions that enable the users to automatically customize, monitor, and control the resources of the network. Due to ONOS controller support, we can easily scale up the network instances by giving the configurations to service orchestrator XOS of the M‐CORD. The implementation of the proposed system is based on the OpenAirInterface (OAI) platform which provides open sources implementation of core and access networks. The aggregation of both LTE and WiFi technologies is done at the PDCP layer in a very tight coupling way. Moreover, we test our proposed system with three kinds of policies for UDP and TCP traffic: LTE only, WiFi only, and LTE‐WiFi aggregated. The experimental results show that our proposed LTE‐WiFi aggregated system gives better performance and provides high bandwidth as compared to the LTE network.

DRAIM: A Novel Delay-Constraint and Reverse Auction-Based Incentive Mechanism for WiFi Offloading

Offloading cellular traffic through WiFi Access Points (APs) has been a promising way to relieve the overload of cellular networks. However, data offloading process consumes a lot of resources (e.g., energy, bandwidth, etc.). Given that the owners of APs are rational and selfish, they will not participate in the data offloading process without receiving the proper reward. Hence, there is an urgent need to develop an effective incentive mechanism to stimulate APs to take part in the data offloading process. This paper proposes a novel Delay-constraint and Reverse Auction-based Incentive Mechanism, named DRAIM. In DRAIM, we model the reverse auction-based incentive problem as a nonlinear integer problem from the business perspective, aiming to maximize the revenue of the Mobile Network Operator (MNO), and jointly consider the delay constraint of different applications in the optimization problem. Then, two low-complexity methods: Greedy Winner Selection Method (GWSM), and Dynamic Programming Winner Selection Method (DPWSM) are proposed to solve the optimization problem. Furthermore, an innovative standard Vickrey-Clarke-Groves scheme-based payment rule is proposed to guarantee the individual rationality and truthfulness properties of DPWSM. At last, extensive simulation results show that the proposed DPWSM is superior to the proposed GWSM and the Random Winner Selection Method in terms of the MNO’s utility and traffic load under different scenarios.

Controlling WiFi Direct Group Formation for Non-Critical Applications in C-V2X Network

The fifth-generation (5G) networks are expected to meet various communication requirements for vehicles. C-V2X, introduced in LTE V2X in Release 14, is designed to provide ultra-high reliability and ultra-low latency performance required by the most demanding V2X applications. In the literature, research interests are primarily focused on safety-critical applications in a dynamic environment. Therefore, in most communication models, both safety and non-safety critical applications operate through the same radio access technology. This is the case of both C-V2X Direct Communication and IEEE 802.11p. However, in an urban environment characterized by high traffic density, the availability of resources can be problematic. In that case, it would be best to propose new communication strategies because different use cases will have different sets of requirements. In this paper, we propose to increase the capacity of C-V2X Direct Communication by introducing WiFi Direct as a second connection alternative. Indeed, several works have shown that WiFi offloading can alleviate the congestion of cellular networks. Thus, an SDN-based P2P Group Formation is proposed by extending OpenFlow to manage the WiFi Direct control plane. This solution also allows establishing multi-hop communication, something that is not possible in the standard version of WiFi Direct. The performance evaluation of the P2P Group Formation procedure is proposed via simulations in an urban environment. The results show that our proposed procedure performs better compared to those proposed in the literature. To demonstrate the implementation feasibility of the proposed solution in real hardware, we also performed prototyping.

Machine learning-Based traffic offloading in fog networks

Wi-Fi offloading in fog networks is believed to be one of the best ways to solve the significant data increase in cellular networks, since nodes located close by are used as relays for offloading traffic and computations. This alarming growth has affected these networks and has put its mark on their performance. Some operators might try upgrading the wide area networks, but in most scenarios, this is not the most cost-effective and optimal solution. These operators would benefit more from intelligent offloading solutions. Therefore, we can use Wi-Fi networks to send some of the packets, thus releasing cellular networks and decongesting traffic. This paper deals with offering a complete offloading solution and presents various profiles aimed at different purposes: saving the battery, getting the maximum data rate, or balancing the two, as well as offering a simulator that reproduces the behavior of the devices in an environment as close to reality as possible. Through extensive analysis, we show that the proposed solutions are able to improve certain metrics based on user requirements.

A Study of the Effects on QoS in WiFi-Cellular Offloading Scenarios

Nowadays the mobile data usage has been significantly increased by an unprecedented amount with the wide spread of smart devices, which is known as the explosion of data traffic. The rapid growth in mobile data traffic leads to a deficiency of cellular network capacity. To solve this problem, readily available Wi-Fi networks are used to offload the data traffic from cellular networks. The Wi-Fi offloading must ensure guaranteed throughput and delay performance for the users. However, if the user doesn’t meet any Wi-Fi network during the download period, the quality of experience gets degraded. Quality of experience can be improved with the help of various techniques such as resource allocation, scheduling, and handoff schemes. To know the effect of the offloading process, some key parameters are identified in this paper and the effect of offloading on these parameters is studied. Here, in this paper a study of various parameters like download time, number of users, data size on the throughput, delay and packet loss is done in the cellular network -WiFi offloading scenarios. This study highlights the need for an efficient QoS mechanism in future heterogeneous networks. It can be considered as a research aspect in upcoming integrated networks.

Depiction Optimization of Overdue Wi-Fi Offloading in Heterogeneous Systems

Depiction Optimization of Overdue Wi-Fi Offloading in Heterogeneous Systems

User Preference Aware Task Coordination and Proactive Bandwidth Allocation in a FiWi-Based Human–Agent–Robot Teamwork Ecosystem

Cooperative human–agent–robot teamwork (HART) provides enormous opportunities for present-day human users to orchestrate their real-time tasks in a coordinated fashion. However, given human users’ different preferences for real-time HART task execution, e.g., lower delay and monetary cost, the selection of proper task coordination services has emerged as an important research problem by taking dynamically changing cloud agent/robot resources, network bandwidth utilization, as well as delay-sensitive and delay-tolerant HART task properties into account. To cope with these challenges, in this paper, we explore the synergy between caching, computation, and communications for achieving cost-effective HART task execution. To exploit the locality of different HART-centric tasks and local/non-local cloud agent/robot resources for different HART-centric task execution, we consider integrated fiber-wireless (FiWi) enhanced networks with computation task offloading as well as fiber backhaul sharing and WiFi offloading capabilities. More precisely, to minimize task execution delay and monetary cost, we propose a user preference aware HART task coordination framework that selects the appropriate dedicated or non-dedicated robot and cloud agent for given caching and computing HART task execution requirements. Further, to cope with varying bandwidth resources, we propose a proactive bandwidth allocation policy for the execution of both delay-sensitive and delay-tolerant HART tasks execution across FiWi enhanced network infrastructures. We evaluate the performance of our proposed preference aware task offloading scheme and compare it to various baseline schemes in terms of different key performance indicators, including the task execution time and monetary cost saving ratio, communication to computation ratio, and offloading gain overhead ratio. Our findings indicate that the proposed delay cost saving policy exhibits a 27% higher task execution time saving ratio and a 48% lower monetary cost saving ratio than the proposed monetary cost saving policy in a typical scenario.