A wormhole attack places two radio transceivers connected by a high capacity link and retransmits wireless signals from one antenna at the other. This creates a set of shortcut paths in the network, and may attract a lot of traffic to the wormhole link. The link thus gains control of a large fraction of network traffic which opens the door for more dangerous attacks afterwards. In this paper we introduce a wormhole detection and removal algorithm based on local connectivity tests.The basic idea is that the neighborhood of a wormhole contains two sets of nodes corresponding to two sides of the wormhole. The distance between these two sets is small when using paths that pass through the wormhole link, but is large when only regular network paths are considered. Thus we remove a small neighborhood that will contain potential wormhole links and check if a slightly larger neighborhood falls apart to multiple connected components. To accommodate spatial and temporal unpredictability of wireless communication links we abstract the network connectivity as an arbitrary graph so that the method does not assume any idealistic models (such as unit disk graph model). The algorithm uses purely local connectivity information, handles multiple wormhole attacks and generalizes to wireless networks deployed in 3D. It does not suffer from typical limitations in previous work such as the requirements of special hardware, communication models, synchronization, node density etc. In simulations, our method is seen to beat the state of the art solutions, in particular for cases where previous solutions experience poor performance.

Abstract-In this paper we propose an algorithm to construct a "space filling" curve for a sensor network with holes. Mathematically, for a given multi-hole domain R, we generate a path P that is provably aperiodic (i.e., any point is covered at most a constant number of times) and dense (i.e., any point of R is arbitrarily close to P). In a discrete setting as in a sensor network, the path visits the nodes with progressive density, which can adapt to the budget of the path length. Given a higher budget, the path covers the network with higher density. With a lower budget the path becomes proportional sparser. We show how this density-adaptive space filling curve can be useful for applications such as serial data fusion, motion planning for data mules, sensor node indexing, and double ruling type in-network data storage and retrieval. We show by simulation results the superior performance of using our algorithm vs standard space filling curves and random walks.

We consider the problem of deployment of cameras inside a complex indoor setting for surveillance applications. We formulate the problem of the minimum guarding network that places a minimum number of cameras satisfying both visual coverage of the domain and wireless network connectivity. We prove that finding the minimum guarding network in both the geometric setting and discrete setting is NP-hard. We also give a 2-approximation algorithm to the geometric minimum guarding network. Motivated by the connection of this problem with the watchman tour problem and the art gallery problem, we develop two algorithms that generate satisfactory results in a prototype testbed and in our simulations.

Load balanced routing in a network, i.e., minimizing the maximum traffic load any node carries for unsplittable flows, is a well known NP-hard problem. Finding practical algorithms remains a long standing challenge. In this paper we propose greedy routing using virtual coordinates that achieves both small path stretch ratio (compared to shortest path) and small load balancing ratio (compared to optimal load balanced routing), in a large scale wireless sensor network deployed densely inside a geometric domain with complex shape. We first provide a greedy routing scheme on a disk with a stretch ratio of at most 2, and under which the maximum load is a factor 4 √ 2 smaller than the maximum load under shortest path routing. This is the first simple routing scheme with a small stretch that has been proven to outperform shortest path routing in terms of load balancing. Then we transform a network of arbitrary shape to a disk by an area preserving map φ. We show that both the path length and the maximum traffic load in the original network only increases by an additional factor of d 2 , where d is the maximum length stretch of φ. Combined with the result on a disk we again achieve both bounded stretch and bounded load balancing ratio. Our simulation results evaluated the practical performance on both quality measures.

Abstract-In this paper we address the problem of scalable and load balanced routing for wireless sensor networks. Motivated by the analog of the continuous setting that geodesic routing on a sphere gives perfect load balancing, we embed sensor nodes on a convex polyhedron in 3D and use greedy routing to deliver messages between any pair of nodes with guaranteed success. This embedding is known to exist by the Koebe-Andreev-Thurston Theorem for any 3-connected planar graphs. In our paper we use discrete Ricci flow to develop a distributed algorithm to compute this embedding. Further, such an embedding is not unique and differs from one another by a Möbius transformation. We employ an optimization routine to look for the Möbius transformation such that the nodes are spread on the polyhedron as uniformly as possible. We evaluated the load balancing property of this greedy routing scheme and showed favorable comparison with previous schemes.

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Small-world experiments in which packages reach addressees unknown to the original sender through a forwarding chain confirm that acquaintance networks have short paths, a property that was later also discovered in many other networks. They further show that people can find these paths by passing the package on to the acquaintance most socially proximate to the target. This has led researchers to conjecture that perhaps also in many other networks some proximity-based algorithm can be used to find short paths, provided that nodes are given appropriate coordinates. Although potential applications are numerous, ranging from decentralized search to recommendation-based trust to disease control, this conjecture has remained largely unverified. In this paper we apply algorithmic methods to embed nodes in some latent space and employ greedy routing to deliver packages. Using these methods we empirically investigate the navigability of five real-world complex networks from diverse contexts and of varying topology. In each network, we deliver a majority of packages in fewer than six hops.

We consider the problem of deployment of cameras inside a complex indoor setting for surveillance applications. We formulate the problem of the minimum guarding network that places a minimum number of cameras satisfying both visual coverage of the domain and wireless network connectivity. We prove that finding the minimum guarding network in both the geometric setting and discrete setting is NP-hard. We also give a 2-approximation algorithm to the geometric minimum guarding network. Motivated by the connection of this problem with the watchman tour problem and the art gallery problem, we develop two algorithms that generate satisfactory results in a prototype testbed and in our simulations.