The capacity of ad hoc wireless networks is constrained by the mutual interference of concurrent transmissions between nodes. We study a model of an ad hoc network where nodes communicate in random source-destination pairs. These nodes are assumed to be mobile. We examine the per-session throughput for applications with loose delay constraints, such that the topology changes over the time-scale of packet delivery. Under this assumption, the per-user throughput can increase dramatically when nodes are mobile rather than fixed. This improvement can be achieved by exploiting a form of multiuser diversity via packet relaying.

The proliferation of online social networks, and the concomitant accumulation of user data, give rise to hotly debated issues of privacy, security, and control. One specific challenge is the sharing or public release of anonymized data without accidentally leaking personally identifiable information (PII). Unfortunately, it is often difficult to ascertain that sophisticated statistical techniques, potentially employing additional external data sources, are unable to break anonymity.In this paper, we consider an instance of this problem, where the object of interest is the structure of a social network, i.e., a graph describing users and their links. Recent work demonstrates that anonymizing node identities may not be sufficient to keep the network private: the availability of node and link data from another domain, which is correlated with the anonymized network, has been used to re-identify the anonymized nodes. This paper is about conditions under which such a de-anonymization process is possible.We attempt to shed light on the following question: can we assume that a sufficiently sparse network is inherently anonymous, in the sense that even with unlimited computational power, deanonymization is impossible? Our approach is to introduce a random graph model for a version of the de-anonymization problem, which is parameterized by the expected node degree and a similarity parameter that controls the correlation between two graphs over the same vertex set. We find simple conditions on these parameters delineating the boundary of privacy, and show that the mean node degree need only grow slightly faster than log n with network size n for nodes to be identifiable. Our results have policy implications for sharing of anonymized network information.

Graph matching is a generalization of the classic graph isomorphism problem. By using only their structures a graph-matching algorithm finds a map between the vertex sets of two similar graphs. This has applications in the deanonymization of social and information networks and, more generally, in the merging of structural data from different domains.One class of graph-matching algorithms starts with a known seed set of matched node pairs. Despite the success of these algorithms in practical applications, their performance has been observed to be very sensitive to the size of the seed set. The lack of a rigorous understanding of parameters and performance makes it difficult to design systems and predict their behavior.In this paper, we propose and analyze a very simple percolation -based graph matching algorithm that incrementally maps every pair of nodes (i, j) with at least r neighboring mapped pairs. The simplicity of this algorithm makes possible a rigorous analysis that relies on recent advances in bootstrap percolation theory for the G(n, p) random graph. We prove conditions on the model parameters in which percolation graph matching succeeds, and we establish a phase transition in the size of the seed set. We also confirm through experiments that the performance of percolation graph matching is surprisingly good, both for synthetic graphs and real social-network data.

Abstract-Mobile wireless networks frequently possess, at the same time, both dense and sparse regions of connectivity; for example, due to a heterogeneous node distribution or radio propagation environment. This paper is about modeling both the mobility and the formation of clusters in such networks, where nodes are concentrated in clusters of dense connectivity, interspersed with sparse connectivity. Uniformly dense and sparse networks have been extensively studied in the past, but not much attention has been devoted to clustered networks.We present a new mobility model for clustered networks, which is important for the design and evaluation of routing protocols. We refer to our model as Heterogeneous Random Walk (HRW). This model is simple, mathematically tractable, and it captures the phenomenon of emerging clusters, observed in real partitioned networks. We provide a closed-form expression for the stationary distribution of node position and we give a method for "perfect simulation".Moreover, we provide evidence, based on mobility traces, for the main macroscopic characteristics of clustered networks captured by the proposed mobility model. In particular, we show that in some scenarios, nodes have statistically very similar mobility patterns. Also, we discuss cluster dynamics and the relationship between node speed and node density.

Improving network lifetime is a fundamental challenge of wireless sensor networks. One possible solution consists in making use of mobile sinks. Whereas theoretical analysis shows that this approach does indeed benefit network lifetime, practical routing protocols that support sink mobility are still missing. In this paper, in line with our previous efforts, we investigate the approach that makes use of a mobile sink for balancing the traffic load and in turn improving network lifetime. We engineer a routing protocol, MobiRoute, that effectively supports sink mobility. Through intensive simulations in TOSSIM with a mobile sink and an implementation of MobiRoute, we prove the feasibility of the mobile sink approach by demonstrating the improved network lifetime in several deployment scenarios.I. INTRODUCTION Many proposals on using mobile sinks to improve the lifetime of wireless sensor networks (WSNs) have appeared recently [1,2,3,4,5,6,7,8,9]. However, in the research community there is a doubt that moving sinks is practical (e.g., [10]). One of the major concerns behind this doubt is that mobility inevitably incurs additional overhead in data communication protocols and the overhead can potentially offset the benefit brought by mobility. In this paper, we intend to dismiss the doubt.We focus on a scenario where all nodes are fixed and have limited energy reserves and where a mobile sink endowed with significantly more resources serves as the data collector. In this scenario, the sink mobility can increase network lifetime through two different methods, depending on the relationship between the sink moving speed and the tolerable delay of the data delivery.In the fast mobility regime, the speed produces tolerable data delivery delay. The WSNs may then take advantage of mobility capacity [11]. This mobile relay approach [1, 2, 3] uses the mobile sink to transport data with its mechanical movements. It trades data delivery latency for the reduction of node energy consumption. We refer to [3] and [4] for simulations and field studies in this regime. In the slow mobility regime, the sink mobility takes a discrete form: the movement trace consists of several anchor points between which the sink moves and at which it pauses. Consequently, the network cannot benefit from mobility capacity. However, it has recently been observed [5,6,7,8,9] that sink mobility can still improve network lifetime. The reason is that the typical many-to-one traffic pattern in WSNs imposes a heavy forwarding load on the nodes close to sinks. While no energy conserving protocol alleviates such a load, moving the sink (even very infrequently) can distribute over time the role of bottleneck nodes and thus even out the load. Unfortunately, theoretical analysis [5,6,7,8,9] may produce misleading results due to its simplified system model (an example is given in Section V: footnote 4); simulations involving a detailed protocol implementation are necessary to fully understand the benefit of using mobile sinks.We argue that the slow mobili...