Routing protocols for Mobile Ad Hoc Networks (MANETs) have been extensively studied for more than fifteen years. Position-based routing protocols route packets towards the destination using greedy forwarding (i.e., an intermediate node forwards packets to a neighbor that is closer to the destination than itself). Different position-based protocols use different strategies to pick the neighbor to forward the packet. If a node has no neighbor that is closer to the destination than itself, greedy forwarding fails. In this case, we say there is void (no neighboring nodes) in the direction of the destination. Different position-based routing protocols use different methods for dealing with voids. In this paper, we use a simple backtracking technique to deal with voids and design a position-based routing protocol called "Greedy Routing Protocol with Backtracking (GRB)". We compare the performance of our protocol with the well known Greedy Perimeter Stateless Routing (GPSR) routing and the Ad-Hoc On-demand Distance Vector (AODV) routing protocol as well as the Dynamic Source Routing (DSR) protocol. Our protocol needs much less routing-control packets than those needed by DSR, AODV, and GPSR. Simulation results also show that our protocol has a higher packet-delivery ratio, lower end-to-end delay, and less hop count on average than AODV.
presented Geo-DFR zhoublyzg_Baban_2008 which incorporates directional forwarding in routing (DFR) @cite_15 . Routing is done mainly using greedy forwarding. However, in case of dead ends (voids), DFR is used. Geo-DFR improves DFR to solve the dead end problem and avoids face routing. The authors use Fisheye State Routing protocol (FSR) @cite_20 which is the protocol that is "hosting" Geo-DFR. Scalability of Geo-DFR is affected by different factors. First, maintaining three tables in each node increases overheard especially when a node has many neighbors. But this limitation is local, since the number of records in two of these tables depends on the density of the neighboring nodes.
@cite_15 @cite_20
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In this paper, we present a novel packet forwarding scheme for wireless ad hoc networks --- “Direction” Forwarding (DFR). Popular routing protocols such as DSDV and AODV use “predecessor” based forwarding, namely, the packet is forwarded to the predecessor on the shortest path from the destination, as advertised during the last update. Predecessor forwarding may fail in large scale networks where the routing update rate must be reduced by the need to maintain link O H below reasonable levels. However, if nodes are mobile, routing table entries may become “stale” very rapidly. DFR is designed to overcome the “stale” routing table entry problem. When the routing update arrives, the node remembers not only the predecessor delivering the update, but also the update “direction” of arrival. When a packet must be forwarded to destination, it is first forwarded to the node ID found in the routing table. If the node has moved and ID forwarding fails, the packet is “direction” forwarded to the “most promising” node in the indicated direction. At first glance, DFR seems to combine the features of table based routing and geo-routing. However, direction forwarding differs from geo-routing in that the direction is learned from the routing updates, instead of being computed from the destination coordinates. Thus, DFR does not require destination coordinates, a global coordinate system, or a Geo Location Server. In the paper we show the application of DFR to a scalable routing scheme, LANMAR. Through simulation experiments we show that DFR substantially enhances LANMAR performance in large, mobile network scenarios. This paper presents a novel routing protocol for wireless ad hoc networks-fisheye state routing (FSR). FSR introduces the notion of multi-level fisheye scope to reduce routing update overhead in large networks. Nodes exchange link state entries with their neighbors with a frequency which depends on distance to destination. From link state entries, nodes construct the topology map of the entire network and compute optimal routes. Simulation experiments show that FSR is a simple, efficient and scalable routing solution in a mobile, ad hoc environment. Geographic forwarding in wireless sensor networks (WSN) has long suffered from the problem of bypassing "dead ends," i.e., those areas in the network where no node can be found in the direction of the data collection point (the sink). Solutions have been proposed to this problem, that rely on geometric techniques leading to the planarization of the network topology graph. In this paper, a novel method alternative to planarization is proposed, termed ALBA-R, that successfully routes packets to the sink transparently to dead ends. ALBA-R combines nodal duty cycles (awake asleep schedules), channel access and geographic routing in a cross-layer fashion. Dead ends are dealt with by enhancing geographic routing with a mechanism that is distributed, localized and capable of routing packets around connectivity holes. An extensive set of simulations is provided, that demonstrates that ALBA-R is scalable, generates negligible overhead, and outperforms similar solutions with respect to all the metrics of interest investigated, especially in sparse topologies, notoriously the toughest benchmark for geographic routing protocols. 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.