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34
AdHoc Networks Beyond Unit Disk Graphs
, 2003
"... In this paper we study a model for adhoc networks close enough to reality as to represent existing networks, being at the same time concise enough to promote strong theoretical results. The Quasi Unit Disk Graph model contains all edges shorter than a parameter d between 0 and 1 and no edges longer ..."
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Cited by 101 (10 self)
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In this paper we study a model for adhoc networks close enough to reality as to represent existing networks, being at the same time concise enough to promote strong theoretical results. The Quasi Unit Disk Graph model contains all edges shorter than a parameter d between 0 and 1 and no edges longer than 1. We show that  in comparison to the cost known on Unit Disk Graphs  the complexity results in this model contain the additional factor 1/d². We prove that in Quasi Unit Disk Graphs flooding is an asymptotically messageoptimal routing technique, provide a geometric routing algorithm being more efficient above all in dense networks, and show that classic geometric routing is possible with the same performance guarantees as for Unit Disk Graphs if d 1/ # 2.
R.: The Complexity of Connectivity in Wireless Networks
 In: Proc. of the 25 th Annual Joint Conf. of the IEEE Computer and Communications Societies (INFOCOM
, 2006
"... Abstract — We define and study the scheduling complexity in wireless networks, which expresses the theoretically achievable efficiency of MAC layer protocols. Given a set of communication requests in arbitrary networks, the scheduling complexity describes the amount of time required to successfully ..."
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Cited by 69 (12 self)
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Abstract — We define and study the scheduling complexity in wireless networks, which expresses the theoretically achievable efficiency of MAC layer protocols. Given a set of communication requests in arbitrary networks, the scheduling complexity describes the amount of time required to successfully schedule all requests. The most basic and important network structure in wireless networks being connectivity, we study the scheduling complexity of connectivity, i.e., the minimal amount of time required until a connected structure can be scheduled. In this paper, we prove that the scheduling complexity of connectivity grows only polylogarithmically in the number of nodes. Specifically, we present a novel scheduling algorithm that successfully schedules a strongly connected set of links in time O(log 4 n) even in arbitrary worstcase networks. On the other hand, we prove that standard MAC layer or scheduling protocols can perform much worse. Particularly, any protocol that either employs uniform or linear (a node’s transmit power is proportional to the minimum power required to reach its intended receiver) power assignment has a Ω(n) scheduling complexity in the worst case, even for simple communication requests. In contrast, our polylogarithmic scheduling algorithm allows many concurrent transmission by using an explicitly formulated nonlinear power assignment scheme. Our results show that even in largescale worstcase networks, there is no theoretical scalability problem when it comes to scheduling transmission requests, thus giving an interesting complement to the more pessimistic bounds for the capacity in wireless networks. All results are based on the physical model of communication, which takes into account that the signaltonoise plus interference ratio (SINR) at a receiver must be above a certain threshold if the transmission is to be received correctly. I.
Improving spatial reuse through tuning transmit power, carrier sense threshold, and data rate in multihop wireless networks
 In Proc. of ACM MobiCom
, 2006
"... The importance of spatial reuse in wireless adhoc networks has been long recognized as a key to improving the network capacity. One can increase the level of spatial reuse by either reducing the transmit power or increasing the carrier sense threshold (thereby reducing the carrier sense range). On ..."
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Cited by 65 (5 self)
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The importance of spatial reuse in wireless adhoc networks has been long recognized as a key to improving the network capacity. One can increase the level of spatial reuse by either reducing the transmit power or increasing the carrier sense threshold (thereby reducing the carrier sense range). On the other hand, as the transmit power decreases or the carrier sense threshold increases, the SINR decreases as a result of the smaller received signal or the increased interference level. Consequently, the data rate sustained by each transmission may decrease. This leads naturally to the following questions: (1) How can the tradeoff between the increased level of spatial reuse and the decreased data rate each node can sustain be quantified? In other words, is there an optimal range of transmit power/carrier sense threshold in which the network capacity is maximized? (2) What is the relation between
Arbitrary Throughput Versus Complexity Tradeoffs in Wireless Networks using Graph Partitioning
, 2007
"... Several policies have recently been proposed for attaining the maximum throughput region, or a guaranteed fraction thereof, through dynamic link scheduling. Among these policies, the ones that attain the maximum throughput region require a computation time which is linear in the network size, and t ..."
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Cited by 22 (6 self)
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Several policies have recently been proposed for attaining the maximum throughput region, or a guaranteed fraction thereof, through dynamic link scheduling. Among these policies, the ones that attain the maximum throughput region require a computation time which is linear in the network size, and the ones that require constant or logarithmic computation time attain only certain fractions of the maximum throughput region. In contrast, in this paper we propose policies that can attain any desirable fraction of the maximum throughput region using a computation time that is largely independent of the network size. First, using a combination of graph partitioning techniques and lyapunov arguments, we propose a simple policy for tree topologies under the primary interference model that requires each link to exchange only 1 bit information with its adjacent links and approximates the maximum throughput region using a computation time that depends only on the maximum degree of nodes and the approximation factor. Then we develop a framework for attaining arbitrary close approximations for the maximum throughput region in arbitrary networks, and use this framework to obtain any desired tradeoff between throughput guarantees and computation times for a large class of networks and interference models. Specifically, given any ɛ> 0, the maximum throughput region can be approximated in these networks within a factor of 1 − ɛ using a computation time that depends only on the maximum node degree and ɛ.
Proximity Structures for Geometric Graphs
 International Journal of Computational Geometry and Applications
, 2003
"... In this paper we study proximity structures like Delaunay triangulations based on geometric graphs, i.e. graphs which are subgraphs of the complete geometric graph. Given an arbitrary geometric graph G, we define several restricted Voronoi diagrams, restricted Delaunay triangulations, relative n ..."
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Cited by 7 (1 self)
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In this paper we study proximity structures like Delaunay triangulations based on geometric graphs, i.e. graphs which are subgraphs of the complete geometric graph. Given an arbitrary geometric graph G, we define several restricted Voronoi diagrams, restricted Delaunay triangulations, relative neighborhood graphs, Gabriel graphs and then study their complexities when G is a general geometric graph or G is some special graph derived from the application area of wireless networks. Besides being of fundamental interest these structures have applications in topology control for wireless networks.
Towards an optimal positioning of multiple mobile sinks in wsns for buildings
 Int J On Advances in Intelligent Systems
"... The need for wireless sensor networks is rapidly growing in a wide range of applications specially for buildings automation. In such networks, a large number of sensors with limited energy supply are in charge of relaying the sensed data hop by hop to the nearest sink. The sensors closest to the sin ..."
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Cited by 5 (4 self)
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The need for wireless sensor networks is rapidly growing in a wide range of applications specially for buildings automation. In such networks, a large number of sensors with limited energy supply are in charge of relaying the sensed data hop by hop to the nearest sink. The sensors closest to the sinks deplete their energy much faster than distant nodes because they carry heavy traffic which causes prematurely the end of the network lifetime. Employing mobile sinks can alleviate this problem by distributing the high traffic load among the sensors and increase the network lifetime. In this work, we aim to find the best way to relocate sinks inside buildings by determining their optimal locations and the duration of their sojourn time. Therefore, we propose an Integer Linear Program for multiple mobile sinks which directly maximizes the network lifetime instead of minimizing the energy consumption or maximizing the residual energy, which is what was done in previous solutions. We evaluated the performance of our approach by simulation and compared it with others schemes. The results show that our solution extends significantly the network lifetime and balances notably the energy consumption among the nodes.
Routing and broadcasting in hybrid ad hoc and sensor networks
 In Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless and PeertoPeer Networks, Jie
, 2006
"... Hybrid ad hoc networks consist of two kinds of nodes, regular nodes and nodes with additional capabilities. For example, multihop cellular and wireless Internet networks consist of static or mobile nodes and access points to a fixed infrastructure. Each node may access fixed infrastructure either d ..."
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Cited by 4 (2 self)
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Hybrid ad hoc networks consist of two kinds of nodes, regular nodes and nodes with additional capabilities. For example, multihop cellular and wireless Internet networks consist of static or mobile nodes and access points to a fixed infrastructure. Each node may access fixed infrastructure either directly or via other nodes in multihop fashion. Another example is heterogeneous sensor networks, which consists of regular tiny sensors, and special nodes capable of communicating between themselves and to monitoring station using their own backbone network. In this paper, we propose some protocols for broadcasting and routing in hybrid ad hoc networks. Hybrid blind flooding uses backbone of access nodes to spread the message, otherwise blind flooding is applied. Component neighbor elimination based flooding applies neighbor elimination based broadcasting separately within each component, consisting of all nodes with the same closest access point. In adaptive flooding, each node additionally estimates whether each of its neighbor from a different component already received the packet via its own access point in the neighbor elimination process. Multipoint relaying, and dominating set based broadcasting are generalized from existing ad hoc network protocols, utilizing the capabilities of access points. These broadcasting protocols can be applied for route discovery in proactive or reactive routing protocols for hybrid ad hoc networks. In particular, broadcasting from multiple sinks in sensor networks can be used to set up routes from each sensor to its closest sink. Hybrid routing protocol for hybrid ad hoc networks applies proactive routing to maintain the link to the closest access point, and reactive routing to find route between two ad hoc nodes. Access points cooperate to reduce the hop count of later route discovery. 1
Topology construction and maintenance in wireless sensor networks
 in: Handbook of Sensor Networks: Algorithms and Architectures
"... Energy efficiency and network capacity are two of the most important issues in wireless sensor networks. Topologycontrol algorithms have been proposed to maintain network connectivity while reducing energy consumption and improving network capacity. Several studies have also been performed to inves ..."
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Cited by 3 (0 self)
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Energy efficiency and network capacity are two of the most important issues in wireless sensor networks. Topologycontrol algorithms have been proposed to maintain network connectivity while reducing energy consumption and improving network capacity. Several studies have also been performed to investigate critical conditions on several network parameters in order to ensure network kconnectivity (in the asymptotic sense). In this chapter, several problems (and their corresponding solutions) related to topology construction, maintenance, and connectivity in wireless sensor networks are discussed. Specifically, topics discussed include (1) various communication models and generation of random network topologies; (2) neighbor discovery and maintenance; (3) basic connectivity properties of wireless sensor networks (with the random unit graph model as the underlying model); (4) localized topology construction algorithms, along with their associated geometric structures in both homogeneous and heterogeneous networks; and (5) how to enhance fault tolerance in topology construction and connectivity. 10.1
Distributed Localization for Anisotropic Sensor Networks
"... In this paper, we address the issue of localization in anisotropic sensor networks. Anisotropic networks are differentiated from isotropic networks in that they possess properties that vary according to the direction of measurement. Anisotropic characteristics result from various factors such as th ..."
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Cited by 3 (0 self)
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In this paper, we address the issue of localization in anisotropic sensor networks. Anisotropic networks are differentiated from isotropic networks in that they possess properties that vary according to the direction of measurement. Anisotropic characteristics result from various factors such as the geographic shape of the region (nonconvex region), the different node densities, the irregular radio patterns, and the anisotropic terrain conditions. In order to characterize anisotropic features, we devise a linear mapping method that transforms proximity measurements between sensor nodes into a geographic distance embedding space by using the truncated singular value decomposition (SVD) pseudoinverse technique. This transformation retains as much topological information as possible and reduces the effect of measurement noises on the estimates of geographic distances. We show via simulation that the proposed localization method outperforms DVhop, DVdistance, and MDSmap, and makes robust and accurate estimates of sensor locations in both isotropic and anisotropic sensor networks.
Relay deployment and power control for lifetime elongation in sensor networks
 in IEEE ICC
, 2006
"... Abstract — In a sensor network, usually a large number of sensors transport data messages to a limited number of sinks. Due to this multipointtopoint communications pattern in general homogeneous sensor networks, the closer a sensor to the sink, the quicker it will deplete its battery. This unbala ..."
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Cited by 3 (1 self)
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Abstract — In a sensor network, usually a large number of sensors transport data messages to a limited number of sinks. Due to this multipointtopoint communications pattern in general homogeneous sensor networks, the closer a sensor to the sink, the quicker it will deplete its battery. This unbalanced energy depletion phenomenon has become the bottleneck problem to elongate the lifetime of sensor networks. In this paper, we consider the effects of joint relay node deployment and transmission power control on network lifetime. Contrary to the intuition the relay nodes considered are even simpler devices than the sensor nodes with limited capabilities. We show that the network lifetime can be extended significantly with the addition of relay nodes to the network. In addition, for the same expected network lifetime goal, the number of relay nodes required can be reduced by employing efficient transmission power control while leaving the network connectivity level unchanged. The solution suggests that it is sufficient to deploy relay nodes only with a specific probabilistic distribution rather than the specifying the exact places. Furthermore, the solution does not require any change on the protocols (such as routing) used in the network. I.