Connectivity and capacity are two fundamental properties of wireless multi-hop networks. The scalability of these properties has been a primary concern for which asymptotic analysis is a useful tool. Three related but logically distinct network models are often considered in asymptotic analyses, viz. the dense network model, the extended network model and the infinite network model, which consider respectively a network deployed in a fixed finite area with a sufficiently large node density, a network deployed in a sufficiently large area with a fixed node density, and a network deployed in @math with a sufficiently large node density. The infinite network model originated from continuum percolation theory and asymptotic results obtained from the infinite network model have often been applied to the dense and extended networks. In this paper, through two case studies related to network connectivity on the expected number of isolated nodes and on the vanishing of components of finite order k>1 respectively, we demonstrate some subtle but important differences between the infinite network model and the dense and extended network models. Therefore extra scrutiny has to be used in order for the results obtained from the infinite network model to be applicable to the dense and extended network models. Asymptotic results are also obtained on the expected number of isolated nodes, the vanishingly small impact of the boundary effect on the number of isolated nodes and the vanishing of components of finite order k>1 in the dense and extended network models using a generic random connection model.
 We analyze various critical transmitting sensing ranges for connectivity and coverage in three-dimensional sensor networks. As in other large-scale complex systems, many global parameters of sensor networks undergo phase transitions. For a given property of the network, there is a critical threshold, corresponding to the minimum amount of the communication effort or power expenditure by individual nodes, above (respectively, below) which the property exists with high (respectively, a low) probability. For sensor networks, properties of interest include simple and multiple degrees of connectivity coverage. First, we investigate the network topology according to the region of deployment, the number of deployed sensors, and their transmitting sensing ranges. More specifically, we consider the following problems: assume that n nodes, each capable of sensing events within a radius of r, are randomly and uniformly distributed in a 3-dimensional region R of volume V, how large must the sensing range R sub SENSE be to ensure a given degree of coverage of the region to monitor? For a given transmission range R sub TRANS , what is the minimum (respectively, maximum) degree of the network? What is then the typical hop diameter of the underlying network? Next, we show how these results affect algorithmic aspects of the network by designing specific distributed protocols for sensor networks. Unlike wired networks, wireless networks do not come with links. Rather, links have to be fashioned out of the ether by nodes choosing neighbors to connect to. Moreover the location of the nodes may be random.The question that we resolve is: How many neighbors should each node be connected to in order that the overall network is connected in a multi-hop fashion? We show that in a network with n randomly placed nodes, each node should be connected to Θ(log n) nearest neighbors. If each node is connected to less than 0.074 log n nearest neighbors then the network is asymptotically disconnected with probability one as n increases, while if each node is connected to more than 5.1774 log n nearest neighbors then the network is asymptotically connected with probability approaching one as n increases. It appears that the critical constant may be close to one, but that remains an open problem.These results should be contrasted with some works in the 1970s and 1980s which suggested that the "magic number" of nearest neighbors should be six or eight. For n points uniformly randomly distributed on the unit cube in d dimensions, with d≥2, let ρn (respectively, σn) denote the minimum r at which the graph, obtained by adding an edge between each pair of points distant at most r apart, is k-connected (respectively, has minimum degree k). Then P[ρn=σn]1 as n∞. ©1999 John Wiley & Sons, Inc. Random Struct. Alg., 15, 145–164, 1999 Let P be a Poisson process of intensity one in a square Sn of area n. For a fixed integer k, join every point of P to its k nearest neighbours, creating an undirected random geometric graph Gn,k. We prove that there exists a critical constant ccrit such that for c ccrit, Gn,⌊clog n⌋ is connected with probability tending to 1 as n → ∞. This answers a question posed by the authors in [1]. Let P be a Poisson process of intensity one in a square Sn of area n. For a fixed integer k, we join every point of P to its k nearest neighbours, creating an undirected random geometric graph GSn,k = Gn,k in which every vertex has degree at least k. The connectivity of these graphs was studied by the present authors in [1]. It is not hard to see that Gn,k becomes connected around k = �(log n), and we proved in [1] that if k(n) ≤ 0.3043log n then the probability that Gn,k(n) is connected tends to zero as n → ∞, while if k(n) ≥ 0.5139log n then the probability that Gn,k(n) is connected tends to one as n → ∞. However, we were unable to prove the natural conjecture that there exists a critical constant ccrit such that for c ccrit, P(Gn,⌊clog n⌋ is connected) → 1 as n → ∞. In this paper we prove this conjecture.  A model of a packet radio network in which transmitters with range R are distributed according to a two-dimensional Poisson point process with density D is examined. To ensure network connectivity, it is shown that pi R sup 2 D, the expected number of nearest neighbors of a transmitter, must grow logarithmically with the area of the network. For an infinite area there exists an infinite connected component with nonzero probability if pi R sup 2 D>N sub 0 , for some critical value N sub 0 . It is shown that 2.195 > A range assignment to the nodes in a wireless ad hoc network induces a topology in which there is an edge between two nodes if and only if both of them are within each other's transmission range. The critical transmission radius for k-connectivity is the smallest r such that if all nodes have the transmission radius r,the induce topology is k-connected. The critical neighbor number for k-connectivity is the smallest integer l such that if every node sets its transmission radius equal to the distance between itself an its l-th nearest neighbor, the induce topology is k-connecte. In this paper, we study the asymptotic critical transmission radius for k-connectivity an asymptotic critical neighbor number for k-connectivity in a wireless ad hoc network whose nodes are uniformly an independently distribute in a unit-area square or disk. We provide a precise asymptotic distribution of the critical transmission radius for k-connectivity and an improve asymptotic almost sure upper bound on the critical neighbor number for k-connectivity. 