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_18: The Dynamic Source Routing protocol (DSR) is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes. DSR allows the network to be completely self-organizing and self-configuring, without the need for any existing network infrastructure or administration. The protocol is composed of the two mechanisms of Route Discovery and Route Maintenance, which work together to allow nodes to discover and maintain source routes to arbitrary destinations in the ad hoc network. The use of source routing allows packet routing to be trivially loop-free, avoids the need for up-to-date routing information in the intermediate nodes through which packets are forwarded, and allows nodes forwarding or overhearing packets to cache the routing information in them for their own future use. All aspects of the protocol operate entirely on-demand, allowing the routing packet overhead of DSR to scale automatically to only that needed to react to changes in the routes currently in use. We have evaluated the operation of DSR through detailed simulation on a variety of movement and communication patterns, and through implementation and significant experimentation in a physical outdoor ad hoc networking testbed we have constructed in Pittsburgh, and have demonstrated the excellent performance of the protocol. In this chapter, we describe the design of DSR and provide a summary of some of our simulation and testbed implementation results for the protocol.
  @cite_14: It was recently reported that all known face and combined greedy-face routing variants cannot guarantee message delivery in arbitrary undirected planar graphs. The purpose of this article is to clarify that this is not the truth in general. We show that specifically in relative neighborhood and Gabriel graphs recovery from a greedy routing failure is always possible without changing between any adjacent faces. Guaranteed delivery then follows from guaranteed recovery while traversing the very first face. In arbitrary graphs, however, a proper face selection mechanism is of importance since recovery from a greedy routing failure may require visiting a sequence of faces before greedy routing can be restarted again. A prominent approach is to visit a sequence of faces which are intersected by the line connecting the source and destination node. Whenever encountering an edge which is intersecting with this line, the critical part is to decide if face traversal has to change to the next adjacent one or not. Failures may occur from incorporating face routing procedures that force to change the traversed face at each intersection. Recently observed routing failures which were produced by the GPSR protocol in arbitrary planar graphs result from incorporating such a face routing variant. They cannot be constructed by the well known GFG algorithm which does not force changing the face anytime. Beside methods which visit the faces intersected by the source destination line, we discuss face routing variants which simply restart face routing whenever the next face has to be explored. We give the first complete and formal proofs that several proposed face routing, and combined greedyface routing schemes do guarantee delivery in specific graph classes or even any arbitrary planar graphs. We also discuss the reasons why other methods may fail to deliver a message or even end up in a loop.
  @cite_7: This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with known distances between neighbors but unknown geographic locations. We propose Hector, a hybrid energy efficient tree-based optimized routing protocol, based on two sets of virtual coordinates. One set is based on rooted tree coordinates, and the other is based on hop distances toward several landmarks. In our algorithm, the node currently holding the packet forwards it to its neighbor that optimizes ratio of power cost over distance progress with landmark coordinates, among nodes that reduce landmark coordinates and do not increase tree coordinates. If such a node does not exist then forwarding is made to the neighbor that reduces tree based distance and optimizes power cost over tree distance progress ratio. Our simulations show the superiority of our algorithm over existing alternatives while guaranteeing delivery, and only up to 30 additional power compared to centralized shortest weighted path algorithm.
  @cite_3: In multihop packet radio networks with randomly distributed terminals, the optimal transmission radii to maximize the expected progress of packets in desired directions are determined with a variety of transmission protocols and network configurations. It is shown that the FM capture phenomenon with slotted ALOHA greatly improves the expected progress over the system without capture due to the more limited area of possibly interfering terminals around the receiver. The (mini)slotted nonpersistent carrier-sense-multiple-access (CSMA) only slightly outperforms ALOHA, unlike the single-hop case (where a large improvement is available), because of a large area of "hidden" terminals and the long vulnerable period generated by them. As an example of an inhomogeneous terminal distribution, the effect of a gap in an otherwise randomly distributed terminal population on the expected progress of packets crossing the gap is considered. In this case, the disadvantage of using a large transmission radius is demonstrated.
  @cite_15: The Ad hoc On-Demand Distance Vector (AODV) routing protocol is intended for use by mobile nodes in an ad hoc network. It offers quick adaptation to dynamic link conditions, low processing and memory overhead, low network utilization, and determines unicast routes to destinations within the ad hoc network. It uses destination sequence numbers to ensure loop freedom at all times (even in the face of anomalous delivery of routing control messages), avoiding problems (such as "counting to infinity") associated with classical distance vector protocols.
  @cite_12: This document describes the Optimized Link State Routing (OLSR) protocol for mobile ad hoc networks. The protocol is an optimization of the classical link state algorithm tailored to the requirements of a mobile wireless LAN. The key concept used in the protocol is that of multipoint relays (MPRs). MPRs are selected nodes which forward broadcast messages during the flooding process. This technique substantially reduces the message overhead as compared to a classical flooding mechanism, where every node retransmits each message when it receives the first copy of the message. In OLSR, link state information is generated only by nodes elected as MPRs. Thus, a second optimization is achieved by minimizing the number of control messages flooded in the network. As a third optimization, an MPR node may chose to report only links between itself and its MPR selectors. Hence, as contrary to the classic link state algorithm, partial link state information is distributed in the network. This information is then used for route calculation. OLSR provides optimal routes (in terms of number of hops). The protocol is particularly suitable for large and dense networks as the technique of MPRs works well in this context.
  @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
summary: A routing protocol defines the rules for exchanging the information between nodes. In geographic routing protocols the decision to which neighbor the packet is sent is controlled by the position of the nodes and the distances between them. The position information to each node can be obtained either by devices such as GPS or Galileo (geographic coordinates) or by analyzing the network structure (virtual coordinates). Position awareness can often significantly improve the efficiency of routing. In @cite_7 it is mentioned that protocols using position information for routing like MFR @cite_3 , COP @cite_20 , and GFG @cite_14 are competitive alternatives to the classical routing protocols for wireless ad hoc networks as for example DSR @cite_18 , AODV @cite_15 , and OLSR @cite_12 ).