Abstract—3G networks are currently facing severe traffic overload problems caused by excessive demands of mobile users. Offloading part of the 3G traffic through other forms of networks, such as Delay Tolerant Networks (DTNs), WiFi hotspots, and Femtocells, is a promising solution. However, since these networks can only provide intermittent and opportunistic connectivity to mobile users, utilizing them for 3G traffic offloading may result in a non-negligible 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 3G 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 take DTN as a case study to illustrate how to predict the offloading potential of the users by using stochastic analysis. Extensive tracedriven simulations verify the efficiency of our incentive framework for 3G traffic offloading. I.

Despite the popularity of home networks, they face a number of systemic problems: (i) Broadband networks are expensive to deploy; and it is not clear how the cost can be shared by several service providers; (ii) Home networks are getting harder to manage as we connect more devices, use new applications, and rely on them for entertainment, communication and work—it is common for home networks to be poorly managed, insecure or just plain broken; and (iii) It is not clear how home networks will steadily improve, after they have been deployed, to provide steadily better service to home users. In this paper we propose slicing home networks as a way to overcome these problems. As a mechanism, slicing allows multiple service providers to share a common infrastructure; and supports many policies and business models for cost sharing. We propose four requirements for slicing home networks: bandwidth and traffic isolation between slices, independent control of each slice, and the ability to modify and improve the behavior of a slice. We explore how these requirements allow cost-sharing, out-sourced management of home networks, and the ability to customize a slice to provide higher-quality service. Finally, we describe an initial prototype that we are deploying in homes.

Abstract: The recent availability of human mobility traces has driven a new wave of research – on human movement – with straightforward applications in wireless/cellular network algorithmic problems. However, all of the studies isolate movement from the environment that surrounds people, i.e. the points of interest that they visit. In this paper we revisit the human mobility problem with new assumptions. We believe that human movement is not independent of the surrounding locations; most of the time people travel with specific goals in mind, visit specific points of interest, and frequently revisit favorite places. Points of interest are also differently spread. We study the correlation between people’s trajectories and the differently spread points of interest nearby. More specifically, by analyzing GPS mobility traces of a large number of users located across two distinct geographical locations, we find that: (i) users do not particularly visit only locations that are close to them but the functional aspect of the location matters as well, (ii) although users in different parts of the globe exhibit different time-of-day behavior, we also find that there is a striking correlation in the frequency of visits to the basic points-of-interest categories that we define. Key-words: Human movement; Mobility traces; GPS; Points of interest

draft-mglt-mif-security-requirements-00.txt Current Radio Access Network (RAN) infrastructure will not be able to deal with the next future traffic increase. As such traffic is being offloaded on alternate networks like WLAN. Contrary to RAN, WLAN MAY not be trusted networks, so the End User has to secure offloaded communications. Current offload architectures consist in tunneling the End User traffic to a Security Gateway. Alternatively, ISPs MAY provide End-to-End security and connect directly the End User to the Server. Because WLAN network are not managed by ISPs, WLAN Access Points MAY not be reliable making End User willing to benefit from multiple connections. This draft presents the Security Requirements for an offloaded End User with multiple interfaces. From the Security Requirements, the draft explains why IPsec is the most appropriated security protocol, and points the Multihoming feature current IKEv2 Extension MOBIKE are lacking. Status of this Memo This Internet-Draft is submitted in full conformance with the

We present procedures that exploit mobility prediction and prefetching to enhance offloading of traffic from mobile networks to WiFi hotspots, for both delay tolerant and delay sensitive traffic. We evaluate the procedures in terms of the percentage of offloaded traffic, the data transfer delay, and the cache size used for prefetching. The evaluation considers empirical measurements and shows how various parameters influence the performance of the procedures, and their robustness to time and throughput estimation errors.

Recently, there has been a tremendous increase in mobile data usage with the wide-spread proliferation of smartphone like devices. However, this increased demand from users has caused severe traffic overloading in cellular networks. Offloading the traffic through several other devices (femtocells, WiFi access points) have been considered to be immediate remedy for such a problem. Thus, in this paper, we study the deployment of WiFi access points (AP) in a metropolitan area for efficient offloading of mobile data traffic. We analyze a large scale real user mobility traces and propose a deployment algorithm based on the density of user data request frequency. In simulations, we present offloading ratio that our algorithm can accomplish with different number of APs. The results demonstrate that our algorithm can achieve close to optimal offloading ratio that is higher than offloading ratios that existing algorithms can achieve with the same number of APs.