Physical human mobility has played an important role in the design and operation of mobile networks. Physical mobility, however, differs from user identity (name) mobility in both traditional mobility management protocols such as Mobile-IP and in new architectures, such as XIA and MobilityFirst, that support identity mobility and location independence as first class objects. A multi-homed stationary user or a stationary user shifting among multiple devices attached to different networks will persistently keep his/her identity but will change access networks and the IP address to which his/her identity is associated. We perform a measurement study of such user transitioning among networks from a network-level point of view, characterizing the sequence of networks to which a user is attached and discuss insights and implications drawn from these measurements. We characterize network transitioning in terms of network residency time, degree of multi-homing, transition rates and more. We find that users typically spend time attached to a small number of access networks, and that a surprisingly large number of users access two networks contemporaneously. We develop and validate a parsimonious Markov chain model of canonical user transitioning among networks that can be used to provision network services and to analyze mobility protocols.

This paper presents a quantitative methodology and results comparing different approaches for {\it location-independent} communication. Our approach is empirical and is based on real Internet topologies, routing tables from real routers, and a measured workload of the mobility of devices and content across network addresses today. We measure the extent of network mobility exhibited by mobile devices with a home-brewn Android app deployed on hundreds of smartphones, and measure the network mobility of Internet content from distributed vantage points. We combine this measured data with our quantitative methodology to analyze the different cost-benefit tradeoffs struck by location-independent network architectures with respect to routing update cost, path stretch, and forwarding table size. We find that more than 20% of users change over 10 IP addresses a day, suggesting that mobility is the norm rather than the exception, so intrinsic and efficient network support for mobility is critical. We also find that with purely name-based routing approaches, each event involving the mobility of a device or popular content may result in an update at up to 14% of Internet routers; but, the fraction of impacted routers is much smaller for the long tail of unpopular content. These results suggest that recent proposals for pure name-based networking are suitable for highly aggregateable content that does not move frequently but may need to be augmented with addressing-assisted approaches to handle device mobility.

Search and rescue missions are entering a new era with the advent of small scale unmanned aerial vehicles (UAVs) with communication capabilities and embedded cameras. Yet, delivering high resolution images of the supervised surface to rescuers is time-critical. To help resolving this problem, we study how UAVs can take advantage of their controlled mobility to derive the optimum strategy for data transmission. Driven by real-world aerial experiments with both airplanes and quadrocopters equipped with 802.11n technology, we show that the UAV should not necessarily transmit as soon as a wireless link is established. Instead, it should wait until it reaches a suitable distance to the receiving UAV, only to transmit when the time to move to the new location and transmit is minimal. We then apply the principle of delayed gratification, where the UAV attempts to solve the tradeoff between postponing until it reaches this minimum and the impatience to deliver as much data as soon as possible, before any physical damage on-the-fly may occur. Our empirical-driven simulations demonstrate that the optimal distance of transmission greatly depends on the interplay of actual throughput, data size, UAV cruise speed, and failure rate, and that state-of-the-art UAVs can already benefit from our approach.

In many practical scenarios, nodes gathering at points of interest yield sizable connected components (clusters), which sometimes comprise the majority of nodes. While recent analysis of mobile networks focused on the process governing node encounters ("contacts"), this model is not particularly suitable for gathering behavior. In this paper, we propose a model of stochastic coalescence (merge) and fragmentation (split) of clusters. We implement this process as a Markov chain and derive analytically the exact stationary distribution of cluster size. Further, we prove that, as the number of nodes grows, the clustering behavior converges to a mean field, which is obtained as a closed-form expression. This expression translates the empirical merge and split rate of a scenario, a microscopic property, to an important macroscopic property - the cluster size distribution - with surprising accuracy. We validate all results with synthetic as well as real-world mobility traces from conference visitors and taxicabs with several thousand nodes.

Mobile wireless network research focuses on scenarios at the extremes of the network connectivity continuum where the probability of all nodes being connected is either close to unity, assuming connected paths between all nodes (mobile ad hoc networks), or it is close to zero, assuming no multi-hop paths exist at all (delay-tolerant networks). In this paper, we argue that a sizable fraction of networks lies between these extremes and is characterized by the existence of partial paths, i.e., multi-hop path segments that allow forwarding data closer to the destination even when no end-to-end path is available. A fundamental issue in such networks is dealing with disruptions of end-to-end paths. Under a stochastic model, we compare the performance of the established end-to-end retransmission (ignoring partial paths), against a forwarding mechanism that leverages partial paths to forward data closer to the destination even during disruption periods. Perhaps surprisingly, the alternative mechanism is not necessarily superior. However, under a stochastic monotonicity condition between current vs. future path length, which we demonstrate to hold in typical network models, we manage to prove superiority of the alternative mechanism in stochastic dominance terms. We believe that this study could serve as a foundation to design more efficient data transfer protocols for partially-connected networks, which could potentially help reducing the gap between applications that can be supported over disconnected networks and those requiring full connectivity.

Existing unicast routing protocols are not suited well for wireless mesh networks as in such networks, most traffic flows between a large number of mobile nodes and a few access points with Internet connectivity. In this paper, we propose HEAT, an anycast routing protocol for this type of communication that is designed to scale to the network size and to be robust to node mobility. HEAT relies on a temperature field to route data packets towards the Internet gateways, as follows. Every node is assigned a temperature value, and packets are routed along increasing temperature values until they reach any of the Internet gateways, which are modeled as heat sources. Our major contribution is a distributed protocol to establish such temperature fields. The distinguishing feature of our protocol is that it does not require flooding of control messages. Rather, every node in the network determines its temperature considering only the temperature of its direct neighbors, which renders our protocol particularly scalable to the network size. We analyze our approach and compare its performance with OLSR through simulations with Glomosim. We use realistic mobility patterns extracted from geographical data of large Swiss cities. Our results clearly show the benefit of HEAT versus OLSR in terms of scalability to the number of nodes and robustness to node mobility. The packet delivery ratio with HEAT is more than two times higher than OLSR in large mobile scenarios and we conclude that HEAT is a suitable routing protocol for city-wide wireless mesh networks.

End-to-end transport protocols perform poorly in mobile environments, primarily due to the frequent route breaks induced by node mobility. We present a framework for hop-by-hop transport protocols and propose a new reliable transport protocol: SaFT (store-and-forward transport). We evaluate its performance in mobile ad hoc networks vis-a-vis TCP. Overall, SaFT achieves up to 3 times faster delivery of messages in such networks. We conclude that store-and-forward protocols are suited well to provide reliable communication in mobile ad hoc networks