text: We introduce and evaluate a very simple landmark-based network partition technique called Hierarchical Bipartition Routing (HBR) to support routing with delivery guarantee in wireless ad hoc sensor networks. It is a simple routing protocol that can easily be combined with any other greedy routing algorithm to obtain delivery guarantee. The efficiency of HBR increases if the network is sparse and contains obstacles. The space necessary to store the additional routing information at a node u is on average not larger than the size necessary to store the IDs of the neighbors of u. The amount of work to setup the complete data structure is on average proportional to flooding the entire network log(n) times, where n is the total number of sensor nodes. We evaluate the performance of HBR in combination with two simple energy-aware geographic greedy routing algorithms based on physical coordinates and virtual coordinates, respectively. Our simulations show that the difference between using HBR and a weighted shortest path to escape a dead-end is only a few percent in typical cases.
  @cite_30: Abstract The relative neighbourhood graph (RNG) of a set of n points on the plane is defined. The ability of the RNG to extract a perceptually meaningful structure from the set of points is briefly discussed and compared to that of two other graph structures: the minimal spanning tree (MST) and the Delaunay (Voronoi) triangulation (DT). It is shown that the RNG is a superset of the MST and a subset of the DT. Two algorithms for obtaining the RNG of n points on the plane are presented. One algorithm runs in 0(n2) time and the other runs in 0(n3) time but works also for the d-dimensional case. Finally, several open problems concerning the RNG in several areas such as geometric complexity, computational perception, and geometric probability, are outlined.
  @cite_11: We propose a novel localized routing protocol for wireless sensor networks (WSN) that is energy-efficient and guarantees delivery. We prove that it is constant factor of the optimum for dense networks. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path (SP) between s and each of its neighbors which are closer to the destination than itself. It then selects node x which minimizes the ratio of the cost of the SP to the progress towards the destination. It then sends the message to the first node on the SP from s to x: say node x'. Node x' restarts the same greedy routing process until the destination is reached or the routing fails. To recover from failure, our algorithm invokes face routing that guarantees delivery. This work is the first to optimize energy consumption of face routing. First, we build a connected dominating set from graph G, second we compute its Gabriel graph to obtain the planar graph G'. Face routing is applied on G' only to decide which edges to follow in the recovery process. On each edge, greedy routing is used. This two-phase (greedy-Face) end-to-end routing process (EtE) reiterates until the final destination is reached. Simulation results show that EtE outperforms several existing geographical routing on energy consumption metric.
  @cite_8: This paper introduces a novel geographic routing protocol called Energy-Efficient Geographic Routing (EEGR) for wireless sensor networks. In EEGR, both geographic information and transceiver power characteristics are employed to make forwarding decisions, thereby enabling an energy-aware localized routing strategy. We prove that EEGR is loop-free and derive the bounds on hop count for sensor- to-sink packet delivery. In particular, we analyze the energy dissipation under EEGR and present the approximated expected energy consumption for sensor-to-sink data delivery when nodes are uniformly deployed. Simulation results demonstrate that EEGR can provide near-optimal energy- efficient routing only based on local information.
  @cite_21: Geographic routing is one of the most widely-accepted techniques to route information in large-scale wireless sensor networks. It is based on a greedy forwarding strategy by which a sensor node selects as next hop relay the most promising neighbor (according to some metric) among those being closer to the destination than itself. This decision is based solely on the position of its neighbors and the destination. Given that sensor nodes are usually operated by batteries, energy-efficiency is a very important metric to be considered by the routing protocol. In this paper we present Locally-Optimal Source Routing (LOSR), a new localized and energy-efficient geographic routing algorithm for wireless sensor networks. Unlike existing energy-efficient geographic routing algorithms, in which current node routing the packet only considers nodes closer to destination than itself, LOSR uses all nodes in the neighborhood to compute a local energy-optimal path formed only by neighbors of the current node towards the selected next hop. Then, source routing is used to force data packets to follow that locally optimal path until next hop is reached. Our simulation results show that the proposed algorithm outperforms the best existing solution, over a variety of network densities and scenarios.
  @cite_0: An important technique for discovering routes between two nodes in an ad-hoc network involves applying the face routing algorithm on a planar spanner of the network. Face routing guarantees message delivery in networks that contains large holes, where greedy algorithms fail. Existing techniques for constructing a suitable planar subgraph involve local tests that eliminate crossings between existing links by deleting some links. They do not test whether the deleted links actually create some crossings and some of the links are deleted needlessly. As a result, some of the routes found in face routing will have an unnecessarily large number of hops from source to destination. We consider a new local test for preprocessing a wireless network that produces a planar subgraph. The test is relatively simple, requires low overhead and does not eliminate existing links unless it is needed to eliminate a crossing, thus reducing overhead associated with multiple hops.
  @cite_19: In a localized routing algorithm, each node currently holding a message makes forwarding decision solely based on the position information about itself, its neighbors and destination. In a unit graph, two nodes can communicate if and only if the distance between them is no more than the transmission radius, which is the same for each node. This paper proposes localized routing algorithms, aimed at minimizing total power for routing a message or maximizing the total number of routing tasks that a network can perform before a partition. The algorithms are combinations of known greedy power and or cost aware localized routing algorithms and an algorithm that guarantees delivery. A shortcut procedure is introduced in later algorithm to enhance its performance. Another improvement is to restrict the routing to nodes in a dominating set. These improvements require two-hop knowledge at each node. The efficiency of proposed algorithms is verified experimentally by comparing their power savings, and the number of routing tasks a network can perform before a node loses all its energy, with the corresponding shortest weighted path algorithms and localized algorithms that use fixed transmission power at each node. Significant energy savings are obtained, and feasibility of applying power and cost-aware localized schemes is demonstrated. Copyright © 2004 John Wiley & Sons, Ltd.
  @cite_16: Constructing a route connecting a source node to a destination node is one of the central problems in sensor networks and mobile computing. In this paper we consider the application of a data structure called ExtDCEL that can be very effective in implementing several location-based routing algorithms that include face routing, greedy most forward routing, and hybrid routing. The ExtDCEL data structure is very convenient for representing unit disk graphs as well as planar components. Many network properties can be computed locally when the graph is represented in ExtDCEL. In particular, we show how the popular hybrid greedy face routing algorithm can be implemented efficiently by using ExtDCEL.
  @cite_25: We consider routing problems in ad hoc wireless networks modeled as unit graphs in which nodes are points in the plane and two nodes can communicate if the distance between them is less than some fixed unit. We describe the first distributed algorithms for routing that do not require duplication of packets or memory at the nodes and yet guarantee that a packet is delivered to its destination. These algorithms can be extended to yield algorithms for broadcasting and geocasting that do not require packet duplication. A by product of our results is a simple distributed protocol for extracting a planar subgraph of a unit graph. We also present simulation results on the performance of our algorithms.
  @cite_20: We present a simple framework for designing network layer protocols for sensor networks including localized routing, broadcasting, area coverage, and so on. The framework is general enough and is applicable to a variety of problems, network assumptions, and optimality criteria. Our simple framework is based on optimizing the ratio of the cost of making certain decisions (e.g., selecting a forwarding neighbor for routing) to the progress made in doing so (e.g., reduction in distance to destination). We show how to apply this general guideline for the design of hop count, power awareness, maximal lifetime, beaconless and physical-layer-based routing, minimal energy broadcasting, sensor area coverage, and multicasting protocols. Moreover, we show that in the case of routing, the best known strictly localized position-based techniques are, in almost all cases, special cases of the described general cost-to-progress ratio paradigm
  @cite_17: In this paper, we address the problem of energy efficient localized routing in wireless ad hoc networks. Numerous energy aware routing protocols were proposed to seek the power efficiency of routes. Among them, several geographical localized routing protocols were proposed to help making smarter routing decision using only local information and reduce the routing overhead. However, most of the proposed localized routing methods cannot theoretically guarantee the power efficiency of their routes. In this paper, we give the first localized routing algorithm, called localized energy aware restricted neighborhood routing (LEARN), which can guarantee the power efficiency of its route asymptotically almost sure. Given destination node t, an intermediate node v will only select a certain neighbor v such that pi alpha, our LEARN routing protocol will find the route for any pair of nodes asymptotically almost sure. When the transmission range rn = radicbet alnl pin for some beta < pi alpha, the LEARN routing protocol will not be able to find the route for any pair of nodes asymptotically almost sure. We also conducted simulations to study the performance of LEARN and compare it with a typical localized routing protocol (GPSR) and a global ad hoc routing protocol (DSR)
summary: There are several attempts to obtain delivery guarantee for greedy routing algorithms. The authors of @cite_25 propose face routing , which guarantees delivery in two-dimensional unit disk graphs (UDG). Face routing is applied to a planar sub-network obtained by considering the Gabriel Graph @cite_25 @cite_16 , the Relative Neighborhood Graph @cite_30 , or the Morelia Graph @cite_0 . In @cite_19 a greedy-face-greedy (GFG) approach is considered, where greedy routing is based on COP as in @cite_20 and face routing is similar to the one in @cite_25 . Energy-aware routing is also proposed in LEARN @cite_17 , SPFSP @cite_21 , End-to-End (EtE) @cite_11 , and EEGR @cite_8 .