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.

Abstract-Mobile wireless ad hoc and sensor networks can be permanently partitioned in many interesting scenarios. This implies that instantaneous end-to-end routes do not exist. Nevertheless, when nodes are mobile, it is possible to forward messages to their destinations through mobility.In these many interesting settings we observe that spatial node distributions are very heterogeneous and possess concentration points of high node density. The locations of these concentration points and the flow of nodes between them tend to be stable over time. This motivated us to propose a novel mobility model, where nodes move randomly between stable islands of connectivity, where they are likely to encounter other nodes, while connectivity is very limited outside these islands.Our goal is to exploit such a stable topology of concentration points by developing algorithms that allow nodes to collaborate in order to discover this topology and to use it for efficient mobility forwarding. We achieve this without any external signals to nodes, such as geographic positions or fixed beacons; instead, we rely only on the evolution of the set of neighbors of each node.We propose an algorithm for this collaborative graph discovery problem and show that the inferred topology can greatly improve the efficiency of mobility forwarding. Using the proposed mobility model we show through simulations that our approach achieves end-to-end delays comparable to those of epidemic approaches and requires a significantly lower transmission overhead.

Web tracking has been extensively studied over the last decade. To detect tracking, previous studies and user tools rely on filter lists. However, it has been shown that filter lists miss trackers. In this paper, we propose an alternative method to detect trackers inspired by analyzing behavior of invisible pixels. By crawling 84,658 webpages from 8,744 domains, we detect that third-party invisible pixels are widely deployed: they are present on more than 94.51% of domains and constitute 35.66% of all third-party images. We propose a fine-grained behavioral classification of tracking based on the analysis of invisible pixels. We use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 4, 216, 454 third-party requests. We demonstrate that two popular methods to detect tracking, based on EasyList&EasyPrivacy and on Disconnect lists respectively miss 25.22% and 30.34% of the trackers that we detect. Moreover, we find that if we combine all three lists, 379, 245 requests originated from 8,744 domains still track users on 68.70% of websites.

According to the type of connectivity, we can distinguish three types of mobile ad-hoc networks: dense, sparse and clustered networks. This paper is about modeling the mobility in clustered networks, where the nodes are concentrated into clusters of the dense connectivity, and in between there exists the sparse connectivity. The dense and sparse networks are extensively studied and modeled, not much attention is paid to the clustered networks.In the sparse and clustered networks, an inherently important aspect is a mobility model, both for the design and evaluation of routing protocols. We propose a new mobility model, called Heterogeneous Random Walk (HRW), for the clustered networks. This model is simple, mathematically tractable and most importantly it captures the phenomenon of emerging clusters, observed in real partitioned networks, in an elegant way. We provide a closed-form expression for the stationary distribution of node position whose movement is governed by the HRW model and we give a recipe for the "perfect simulation".Moreover, based on the real mobility trace we provide strong evidence for the main macroscopic characteristics of clustered networks captured by the HRW mobility model. For the very first time in the literature we show evidence for the correlation between the spatial speed distribution and the cluster formation. We also present the analysis of real cluster dynamics caused by nodes mobility.

Abstract. Last Encounter Routing (LER) algorithms for mobile ad hoc networks rely only on encounter histories at every node to raute packets, and therefore do not need control traffic to track topology changes due to node mobility. LER exploits the fact that past information about a node's mobility helps to locate that node in the future. As we have pointed out in earlier work [1], the performance of LER algorithms depends on the mobility processes of nodes. In this paper, we ask whether LER can work under the random waypoint (RWP) mobility model. This question is important for several reasons. First, as shown in [1], a good performance for the RWP model is harder to achieve than for another prominent mobility model, the random walk. This is because the RWP model has a much shorter relaxation time, i.e. , a time-horizon over which past information is still useful. Also, the RWP model has a much less favorable ratio of number of encounters between nodes and the traveled distance. Second, in centrast to the random walk, the RWP model is predictable. This provides us with an opportunity to exploit additional information collected in an encounter (such as speed, direction, etc.) to improve routing. We formally define the RWP model, and compute the optimal predictors for several observation sets, i.e., observed parameters of node mobility. We develop a new LER algorithm tuned to the RWP model called GREASE-RWP, and present simulation results that dernarrstrate that an efficient and scalable LER for the RWP model is possible.