Cellular Traffic Offloading Research Articles

Cost-effective live video streaming for internet of connected vehicles using heterogeneous networks

Smart vehicles of Internet of Connected Vehicles (IoCV) create Ad-Hoc networks and are persistently connected to the Internet through different network access technologies like Wifi, cellular, and dedicated short range communication (DSRC). A vehicle may prefer one network access technology over another based on availability, coverage area, and network access cost. The choice also depends on application requirements, like delay-tolerant or delay-sensitive, and application objectives, like maximizing data rate, maximizing cellular traffic offloading, etc. Cellular traffic offloading using Wifi access points and vehicle-to-vehicle (V2V) collaborations are very challenging due to the intermittent short coverage areas of Wifi access points and highly dynamic connectivity of V2V Ad-Hoc network. Most existing works on traffic offloading have considered delay-tolerant or on-demand services, whereas delay-constrained live video streaming services are mostly ignored. This work is focused on live video streaming services over IoCVs, where the non-triviality of the network selection task is demonstrated by formulating the task as an integer linear programming problem (ILPP). Accordingly, a distributed algorithm is proposed, which greedily maximizes cellular traffic offloading while significantly reducing the control packet overheads. Compared to existing works, the proposed work provides the best service effectiveness with a balanced performance on traffic offloading, service efficiency, and user satisfaction.

Energy efficient target set selection and buffer management for D2D mobile data offloading

Data offloading offers a significant solution to the problem of explosive rise in mobile data traffic. A naive approach would be to utilize the infrastructure (cellular tower, WiFi, femtocell) or other mobile devices to offload data. However, increasing the number of a cellular towers, WiFi, or femtocells is costlier deal for data delivery. Recently, device-to-device (D2D) paradigm of data communication has emerged out as one of the most promising solutions to deal with cost effective cellular traffic offloading. D2D communication provides a direct communication link between closely located mobile users. Another significant feature of D2D is its content centric nature, which makes it useful in data offloading. In this paper, we have addressed the issue of data offloading in mobile devices and proposed a hybrid model of D2D communication with ad-hoc nature. The paper also considers the issues like memory constraints of the devices, pruning of replicated messages and energy efficiency to increase the lifetime of the battery. Considering all the constraints and trade off, we have modeled our problem into optimal target selection problem and distributed community detection problem, both of which are NP-hard. We propose a clustering algorithm to optimize the cooperative mobile nodes. The proposed algorithm uses the betweenness centrality and k-means for optimizing target set section. Our proposed algorithm requires less time in terms of computational complexity with limited space. We compare it with the community-based approach in terms of load transferred for varying target set sizes for validation. The simulation results of the suggested algorithm may reduce the energy requirements up to 16.7% and is able to accommodate 80% more traffic as compared to the community-based algorithm.

Deep Learning Empowered Traffic Offloading in Intelligent Software Defined Cellular V2X Networks

The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the `Pareto optimal' to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks.

A Multi-Unit Profit Competitive Mechanism for Cellular Traffic Offloading

Cellular traffic offloading is nowadays an important problem in mobile networking. We model it as a procurement problem where each agent sells multi-units of a homogeneous item with privately known capacity and unit cost, and the auctioneer's demand valuation function is symmetric submodular. Based on the framework of random sampling and profit extraction, we aim to design a prior-free mechanism which guarantees a profit competitive to the omniscient single-price auction. However, the symmetric submodular demand valuation function and 2-parameter setting present new challenges. By adopting the highest feasible clear price, we successfully design a truthful profit extractor, and then we propose a mechanism which is proved to be truthful, individually rational and constant-factor competitive in a fixed market.

Analytical models of floating content in a vehicular urban environment

Among the many proposed opportunistic content sharing schemes, Floating Content (FC) is of special interest for the vehicular environment, not only for cellular traffic offloading, but also as a natural communication paradigm for location-based context-aware vehicular applications. Previously published results on the performance of vehicular FC have mostly focused on the conditions under which content persists over time in a given region of space, without addressing other important aspects of vehicular FC performance, such as the effectiveness with which content is replicated and made available, and the system conditions that enable good FC performance. This work presents a first analytical model of FC performance in vehicular networks in urban settings. It is based on a new synthetic mobility model (called District Mobility Model - DMM), and it does not require a detailed knowledge of the road grid geometry. We validate our model extensively, by comparison against numerical simulations based on real-world traces, and we prove our model accuracy under a variety of mobility patterns and traffic conditions. Our analytical and simulation results provide evidence of the effectiveness of theFC paradigm in realistic urban settings over a wide range of traffic conditions.

Cellular Traffic Offloading via Link Prediction in Opportunistic Networks

With the emergence of affordable smart mobile devices (such as smartphones and tablets) running innovative applications have severely overloaded the cellular network. To cope with this issue, there have been many efforts to offload the traffic from the cellular network to other complement networks, for instance, Wi-Fi and device-to-device (D2D) communications. In this paper, we formulate the traffic offloading issue as a link prediction problem in opportunistic D2D network, which is targeted to alleviate the overburdened cellular network traffic and reduce the delay time. Considering the complexity of realistic networks, we employ three indexes of link prediction: common neighbors, Katz, and LRW index. To measure the performance of our proposed algorithm, we analyze it is offloading traffic capacity along with delay minimization among users in different networks. It is demonstrated that our proposed link prediction solution can efficiently offload up to 80% of the cellular traffic.

Joint mode selection and power allocation for better traffic offloading in D2D communications

Device-to-Device (D2D) technology underlying the cellular network is an attractive solution for future generation network to increase cellular traffic offloading. When two devices are close enough, D2D communication allows a direct communication between them without the mediation of the cellular base station. However, since D2D communication shares the same uplink cellular network radio resource, D2D communication mode selection remains challenging to limit the mutual interference between D2D and cellular links. In this paper, a new D2D mode selection scheme based on a new criterion is proposed aiming at maximizing the cellular traffic offload while respecting the quality of service of cellular users. More precisely, we introduce a noise rise parameter which takes into account the cellular link distance, the D2D link distance, and user’s required throughput. The D2D links are admitted under the constraint of a certain noise rise threshold. To further enhance the achieved throughput of D2D users while respecting the overall interference level in the network, a new power control algorithm is also proposed. Simulation results show that our proposed mode selection scheme provides a tradeoff between traffic offloading and the overall radio capacity in the network. They also prove that the D2D communication mode is more attractive at the cell edge and when D2D users are close enough to each other. In addition, we show the effectiveness of our proposed power control scheme as compared to both static and Rate-Adaptive Water Filling power allocation schemes.

An Incentive Mechanism Integrating Joint Power, Channel and Link Management for Social-Aware D2D Content Sharing and Proactive Caching

In this paper, a downlink cellular traffic offloading framework with social-aware device-to-device (D2D) content sharing and proactive caching is studied. In the considered system model, each user equipment (UE) is intelligent to determine which content(s) to request/cache and to share according to its own preference. As the central controller, the base station (BS) can establish cellular transmissions and/or incentivize D2D communications for content dissemination (including proactive caching). By taking into account wireless features, social characteristics, and device intelligence, we formulate a welfare maximization problem integrating power control, channel allocation, link scheduling, and reward design. To solve this complicated problem, we propose a novel mechanism which consists of a newly developed optimization approach, called basis transformation method , for the joint resource management, and a specially devised pricing scheme for the reward determination. Theoretical and simulation results examine the desired properties of our proposed mechanism, and demonstrate its superiority in improving social welfare, network capacity, and utility of the BS.

IEEE 802.11ah: A Long Range 802.11 WLAN at Sub 1 GHz

IEEE 802.11ah is an emerging Wireless LAN (WLAN) standard that defines a WLAN system operating at sub 1 GHz license-exempt bands. Thanks to the favorable propagation characteristics of the low frequency spectra, 802.11ah can provide much improved transmission range compared with the conventional 802.11 WLANs operating at 2.4 GHz and 5 GHz bands. 802.11ah can be used for various purposes including large scale sensor networks, extended range hotspot, and outdoor Wi-Fi for cellular traffic offloading, whereas the available bandwidth is relatively narrow. In this paper, we give a technical overview of 802.11ah Physical (PHY) layer and Medium Access Control (MAC) layer. For the 802.11ah PHY, which is designed based on the down-clocked operation of IEEE 802.11ac’s PHY layer, we describe its channelization and transmission modes. Besides, 802.11ah MAC layer has adopted some enhancements to fulfill the expected system requirements.These enhancements include the improvement of power saving features, support of large number of stations, efficient medium access mechanisms and throughput enhancements by greater compactness of various frame formats. Through the numerical analysis, we evaluate the transmission range for indoor and outdoor environments and the theoretical throughput with newly defined channel access mechanisms.

Cellular traffic offloading utilizing set-cover based caching in mobile social networks

To cope with the explosive data demands, offloading cellular traffic through mobile social networks (MSNs) has become a promising approach to alleviate traffic load. Indeed, the repeated data transmission results in a great deal of unnecessary traffic. Existing solutions generally adopt proactive caching and achieve traffic shifting by exploiting opportunistic contacts. The key challenge to maximize the offloading utility needs leveraging the trade-off between the offloaded traffic and the users’ delay requirement. Since current caching scheme rarely address this challenge, in this paper, we first quantitatively interpret the offloading revenues on the cellular operator side associated with the scale of caching users, then develop a centralized caching protocol to maximize the offloading revenues, which includes the selective algorithm of caching location based on set-cover, the cached-data dissemination strategy based on multi-path routing and the cache replacement policy based on data popularity. The experimental results on real-world mobility traces show that the proposed caching protocol outperforms existing schemes in offloading scenario.

Green Cooperative Device–to–Device Communication: a Social–Aware Perspective

As the mobile operators strive to accommodate the increasing load and capacity requirements, direct communication among users’ devices, namely, device-to-device (D2D) communication, emerges as an advantageous solution for cellular traffic offloading. However, the formation and the operation of D2D networks are challenging, as D2D peers must satisfy various network-related constraints. Although existing approaches address the D2D communication technical challenges, they usually neglect the fact that devices are used by humans possibly reluctant to communicate with unknown users, introducing an additional constraint. Following the proliferation of social networks and cutting edge mobile devices, social ties among users can promote D2D cooperation. In D2D cooperative communication, multiple devices in close proximity attempt to access the wireless medium. Their interactions at medium access level are affected by the users’ social features, as socially connected users are more likely to engage in D2D cooperation. Moreover, the energy consumption of power-constrained mobile devices affects the effectiveness of D2D cooperative communication, stressing the need for incorporating energy awareness in D2D networking. In this paper, we outline the challenges that appear in D2D cooperative networking and medium access control (MAC) design under the influence of social characteristics and the energy consumption concerns that arise in modern D2D networking scenarios. Considering the users’ social ties, we present an energy efficient social-aware cooperative D2D MAC protocol as a paradigm of social information inclusion in the green D2D MAC design. Last, we discuss the practical issues of the adoption of social awareness in green D2D cooperation, which may affect the D2D performance in realistic scenarios. Our simulation results demonstrate the effectiveness of exploiting the existence of users’ social connections in D2D cooperation, highlighting the need for green social-aware D2D cooperative schemes.

Maximized Cellular Traffic Offloading via Device-to-Device Content Sharing

In next-generation LTE-advanced cellular networks, device-to-device (D2D) communication has emerged as an effective way to offload cellular traffic and improve system performance. Conventionally, a device exclusively relies on cellular communication to retrieve the content it desires. With D2D communication, however, if the same piece of content is available in the vicinity of the device, the content can be directly retrieved from one of its neighbouring devices. Naturally, the key problem becomes how to maximize content sharing via D2D communication. Existing works on content sharing are mainly concerned with a multi-hop communication setting, while works on D2D communication have primarily focused on the communication aspects, including interference avoidance and energy efficiency. In this paper, we study the problem of maximizing cellular traffic offloading with D2D communication, by selectively caching popular content locally, and by exploring maximal matching for sender-receiver pairs. Specifically, we consider an interference-aware communication model and formulate selective caching as a Knapsack problem, and sender-receiver matching as a maximum weighted matching problem in a bipartite graph. We propose decentralized algorithms to solve both problems, and our simulation results demonstrate that our algorithms are effective in maximizing cellular traffic offloading.

Cellular traffic offloading via opportunistic networking with reinforcement learning

The widespread diffusion of mobile phones is triggering an exponential growth of mobile data traffic that is likely to cause, in the near future, considerable traffic overload issues even in last-generation cellular networks. Offloading part of the traffic to other networks is considered a very promising approach and, in particular, in this paper we consider offloading through opportunistic networks of users’ devices. However, the performance of this solution strongly depends on the pattern of encounters between mobile nodes, which should therefore be taken into account when designing offloading control algorithms. In this paper we propose an adaptive offloading solution based on the Reinforcement Learning framework and we evaluate and compare the performance of two well known learning algorithms: Actor–Critic and Q-Learning. More precisely, in our solution the controller of the dissemination process, once trained, is able to select a proper number of content replicas to be injected in the opportunistic network to guarantee the timely delivery of contents to all interested users. We show that our system based on Reinforcement Learning is able to automatically learn a very efficient strategy to reduce the traffic on the cellular network, without relying on any additional context information about the opportunistic network. Our solution achieves higher level of offloading with respect to other state-of-the-art approaches, in a range of different mobility settings. Moreover, we show that a more refined learning solution, based on the Actor–Critic algorithm, is significantly more efficient than a simpler solution based on Q-Learning.

Cellular Traffic Offloading through Opportunistic Communications Based on Human Mobility

The rapid increase of smart mobile devices and mobile applications has led to explosive growth of data traffic in cellular network. Offloading data traffic becomes one of the most urgent technical problems. Recent work has proposed to exploit opportunistic communications to offload cellular traffic for mobile data dissemination services, especially for accepting large delayed data. The basic idea is to deliver the data to only part of subscribers (called target-nodes) via the cellular network, and allow target-nodes to disseminate the data through opportunistic communications. Human mobility shows temporal and spatial characteristics and predictability, which can be used as effective guidance efficient opportunistic communication. Therefore, based on the regularity of human mobility we propose NodeRank algorithm which uses the encounter characteristics between nodes to choose target nodes. Different from the existing work which only using encounter frequency, NodeRank algorithm combined the contact time and inter-contact time meanwhile to ensure integrity and availability of message delivery. The simulation results based on real-world mobility traces show the performance advantages of NodeRank in offloading efficiency and network redundant copies.

MobiCache: Cellular traffic offloading leveraging cooperative caching in mobile social networks

Offloading cellular traffic through mobile social networks has arisen as a promising way for relieving cellular networks. Prior studies mainly focused on caching data in a number of pre-selected helpers. However, such a strategy would fail when mobile users enter and leave the target area over time. In this paper, we examine the research decisions and design tradeoffs that arise when offloading cellular traffic in such a dynamic area of interest, referred to as a MobiArea, and we design an offloading framework, MobiCache, for maximizing cellular operators’ revenues and minimizing the overhead imposed on mobile devices. On the user side, we propose a content floating-based cooperative caching strategy that caches data in geographical floating circles, instead of selected helpers in previous studies, to cope with the dynamics. A geographical routing scheme is designed for delivering data and queries towards floating circles. We also develop a cache replacement scheme to improve caching cost-effectiveness inside floating circles. On the operator side, query history and feedback are maintained for cellular operators to optimize framework parameters that maximize their revenues. Extensive trace-driven simulations show that, compared with a state-of-the-art scheme, MobiCache offloads up to 52% more traffic with 15% shorter delay and 6% less forwarding cost.

An Incentive Framework for Cellular Traffic Offloading

Cellular networks (e.g., 3G) are currently facing severe traffic overload problems caused by excessive traffic demands. Offloading part of the cellular traffic through other forms of networks, such as Delay Tolerant Networks (DTNs) and WiFi hotspots, is a promising solution. However, since these networks can only provide intermittent connectivity to mobile users, utilizing them for cellular traffic offloading may result in a nonnegligible delay. As the delay increases, the users' satisfaction decreases. In this paper, we investigate the tradeoff between the amount of traffic being offloaded and the users' satisfaction. We provide a novel incentive framework to motivate users to leverage their delay tolerance for cellular traffic offloading. To minimize the incentive cost given an offloading target, users with high delay tolerance and large offloading potential should be prioritized for traffic offloading. To effectively capture the dynamic characteristics of users' delay tolerance, our incentive framework is based on reverse auction to let users proactively express their delay tolerance by submitting bids. We further illustrate how to predict the offloading potential of the users by using stochastic analysis for both DTN and WiFi cases. Extensive trace-driven simulations verify the efficiency of our incentive framework for cellular traffic offloading.

IEEE 802.11ah: A Long Range 802.11 WLAN at Sub 1 GHz

IEEE 802.11ah is an emerging Wireless LAN (WLAN) standard that defines a WLAN system operating at sub 1 GHz license-exempt bands. Thanks to the favorable propagation characteristics of the low frequency spectra, 802.11ah can provide much improved transmission range compared with the conventional 802.11 WLANs operating at 2.4 GHz and 5 GHz bands. 802.11ah can be used for various purposes including large scale sensor networks, extended range hotspot, and outdoor Wi-Fi for cellular traffic offloading, whereas the available bandwidth is relatively narrow.
 In this paper, we give a technical overview of 802.11ah Physical (PHY) layer and Medium Access Control (MAC) layer. For the 802.11ah PHY, which is designed based on the down-clocked operation of IEEE 802.11ac’s PHY layer, we describe its channelization and transmission modes. Besides, 802.11ah MAC layer has adopted some enhancements to fulfill the expected system requirements. These enhancements include the improvement of power saving features, support of large number of stations, efficient medium access mechanisms and throughput enhancements by greater compactness of various frame formats. Through the numerical analysis, we evaluate the transmission range for indoor and outdoor environments and the theoretical throughput with newly defined channel access mechanisms.