We present a novel geographical routing scheme for spontaneous wireless mesh networks. Greedy geographical routing has many advantages, but suffers from packet losses occurring at the border of voids. In this paper, we propose a flexible greedy routing scheme that can be adapted to any variant of geographical routing and works for any connectivity graph, not necessarily Unit Disk Graphs. The idea is to reactively detect voids, backtrack packets, and propagate information on blocked sectors to reduce packet loss. We also propose an extrapolating algorithm to reduce the latency of void discovery and to limit route stretch. Performance evaluation via simulation shows that our modified greedy routing avoids most of packet losses.
Geographic information can largely reduce complexity of routing in spontaneous mesh networks. The most simple and widely used protocol is greedy geographic routing @cite_4 @cite_1 @cite_6 @cite_12 : when a node receives a packet, it uses the following forwarding rule: is usually defined with respect to the distance towards the destination. Since improvement is not negative, there is no routing loops. Moreover, routing is scalable, because all routing decisions are local.
@cite_1 @cite_4 @cite_12 @cite_6
2140531391 2140525529 2083299955 2126003739 2165689920 2152948207 1974718755 2100013559 2083086638 2963380201 2104645107 2152809170 2141661023 1989581936 2133937062 2101963262 2110348561 2020668600 2036935881 2165424349
Routing in large-scale mobile ad hoc networks is challenging because all the nodes are potentially moving. Geographic routing can partially alleviate this problem, as nodes can make local routing decisions based solely on the destinations' geographic coordinates. However, geographic routing still requires an efficient location service, i.e., a distributed database recording the location of every destination node. Devising efficient, scalable, and robust location services has received considerable attention in recent years. The main purpose of this paper is to show that node mobility can be exploited to disseminate destination location information without incurring any communication overhead. We achieve this by letting each node maintain a local database of the time and location of its last encounter with every other node in the network. This database is consulted by packets to obtain estimates of their destination's current location. As a packet travels towards its destination, it is able to successively refine an estimate of the destination's precise location, because node mobility has "diffused" estimates of that location. We define and analyze a very simple algorithm called EASE (exponential age search) and show that in a model where N nodes perform independent random walks on a square lattice, the length of the routes computed by EASE are on the same order as the distance between the source and destination even for very large N. Therefore, without exchanging any explicit location information, the length of EASE routes are within a constant factor of routes obtained with perfect information. We discuss refinements of the EASE algorithm and evaluate it through extensive simulations. We discuss general conditions such that the mobility diffusion effect leads to efficient routes without an explicit location service. In practical settings, where these conditions may not always be met, we believe that the mobility diffusion effect can complement existing location services and enhance their robustness and scalability. Routing in large-scale mobile ad hoc networks is challenging because all the nodes are potentially moving. Geographic routing can partially alleviate this problem, as nodes can make local routing decisions based solely on the destinations' geographic coordinates. However, geographic routing still requires an efficient location service, i.e., a distributed database recording the location of every destination node. Devising efficient, scalable, and robust location services has received considerable attention in recent years. The main purpose of this paper is to show that node mobility can be exploited to disseminate destination location information without incurring any communication overhead. We achieve this by letting each node maintain a local database of the time and location of its last encounter with every other node in the network. This database is consulted by packets to obtain estimates of their destination's current location. As a packet travels towards its destination, it is able to successively refine an estimate of the destination's precise location, because node mobility has "diffused" estimates of that location. We define and analyze a very simple algorithm called EASE (Exponential Age Search) and show that in a model where Theta(n) nodes perform independent random walks on a square lattice of size n, the length of the routes computed by EASE are of the same order as the distance between the source and destination even for very large n. Therefore, without disseminating any explicit location information, the length of EASE routes are within a constant factor of routes obtained with perfect information. We discuss refinements of the EASE algorithm and evaluate it through extensive simulations. We discuss general conditions such that the mobility diffusion effect leads to efficient routes without an explicit location service. In practical settings, where these conditions may not always be met, we believe that the mobility diffusion effect can complement existing location services and enhance their robustness and scalability We begin by asking whether geographic routing can scale when nodes are mobile; that is, can the overhead involved in tracking node locations be accommodated within the transport capacity of large-scale mobile ad hoc networks (MANETs)? We answer this question in the affirmative by proposing an efficient position publish protocol which fits within the transport capacity and a routing protocol that operates with imperfect information of the destination's location. The routing protocol guarantees, with high probability, routes whose lengths are within a constant “stretch” factor of the shortest path from source to destination. The key idea underlying the scalability of the publish protocol is for each potential destination node to send location updates (with frequency decaying with distance) only to a subset of network nodes, structured as annular regions around it (the natural approach of updating circular regions in distance-dependent fashion does not scale). The routing protocol must then account for the fact that the source and or relay nodes may not have estimates of the destination's location (or may have stale estimates). Spatial and temporal scaling of protocol parameters are chosen so as to guarantee scalability, route reliability and route stretch. Since ad hoc and sensor networks can be composed of a very large number of devices, the scalability of network protocols is a major design concern. Furthermore, network protocols must be designed to prolong the battery lifetime of the devices. However, most existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. In a geographical routing algorithm, data packets are forwarded by a node to its neighbor based on their respective positions. The neighborhood of each node is constituted by the nodes that lie within a certain radio range. Thus, from the perspective of a node forwarding a packet, the next hop depends on the width of the neighborhood it perceives. The analytical framework proposed in this paper allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. A wider topology knowledge may improve the energy efficiency of the routing tasks but increases the cost of topology information due to signaling packets needed to acquire this information. The problem of determining the optimal topology knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is shown that the problem is intrinsically localized, i.e., a limited topology knowledge is sufficient to make energy efficient forwarding decisions. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover, a new forwarding scheme, partial topology knowledge forwarding (PTKF), is introduced, and shown to outperform other existing schemes in typical application scenarios. A probe-based distributed protocol for knowledge range adjustment (PRADA) is finally introduced that allows each node to efficiently select online its topology knowledge range. PRADA is shown to rapidly converge to a near-optimal solution. 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. We propose a scalable and reliable point-to-point routing algorithm for ad hoc wireless networks and sensor-nets. Our algorithm assigns to each node of the network a virtual coordinate in the hyperbolic plane, and performs greedy geographic routing with respect to these virtual coordinates. Unlike other proposed greedy routing algorithms based on virtual coordinates, our embedding guarantees that the greedy algorithm is always successful in finding a route to the destination, if such a route exists. We describe a distributed algorithm for computing each node's virtual coordinates in the hyperbolic plane, and for greedily routing packets to a destination point in the hyperbolic plane. (This destination may be the address of another node of the network, or it may be an address associated to a piece of content in a Distributed Hash Table. In the latter case we prove that the greedy routing strategy makes a consistent choice of the node responsible for the address, irrespective of the source address of the request.) We evaluate the resulting algorithm in terms of both path stretch and node congestion. Greedy geographic routing is attractive for large multi-hop wireless networks because of its simple and distributed operation. However, it may easily result in dead ends or hotspots when routing in a network with obstacles (regions without sufficient connectivity to forward messages). In this paper, we propose a distributed routing algorithm that combines greedy geographic routing with two non-Euclidian distance metrics, chosen so as to provide load balanced routing around obstacles and hotspots. The first metric, Local Shortest Path, is used to achieve high probability of progress, while the second metric, Weighted Distance Gain, is used to select a desirable node among those that provide progress. The proposed Load Balanced Local Shortest Path (LBLSP) routing algorithm provides loop freedom, guarantees delivery when a path exists, is able to efficiently route around obstacles, and provides good load balancing. 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. We show that complex (scale-free) network topologies naturally emerge from hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient greedy forwarding in these networks. Greedy forwarding is topology-oblivious. Nevertheless, greedy packets find their destinations with 100 probability following almost optimal shortest paths. This remarkable efficiency sustains even in highly dynamic networks. Our findings suggest that forwarding information through complex networks, such as the Internet, is possible without the overhead of existing routing protocols, and may also find practical applications in overlay networks for tasks such as application-level routing, information sharing, and data distribution. We show that complex (scale-free) network topologies naturally emerge from hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient greedy forwarding in these networks. Greedy forwarding is topology-oblivious. Nevertheless, greedy packets find their destinations with 100 probability following almost optimal shortest paths. This remarkable efficiency sustains even in highly dynamic networks. Our findings suggest that forwarding information through complex networks, such as the Internet, is possible without the overhead of existing routing protocols, and may also find practical applications in overlay networks for tasks such as application-level routing, information sharing, and data distribution. Geographic routing can provide efficient routing at a fixed overhead. However, the performance of geographic routing is impacted by physical voids, and localization errors. Accordingly, virtual coordinate systems (VCS) were proposed as an alternative approach that is resilient to localization errors and that naturally routes around physical voids. However, VCS also faces virtual anomalies. Moreover, there are no effective complementary routing algorithm that can be used to traverse voids. Most existing solutions use variants of flooding or blind searching when a void is encountered. In this paper, we make the observation that increasing the number of dimensions in virtual coordinate systems cannot eliminate all virtual anomalies since some portions of the network may be 1-connected to the rest of the network. As a result, we conjecture that a delivery guaranteed protocol must be one-dimensional and propose a spanning-path virtual coordinate system that has this property. We develop a delivery guaranteed routing algorithm on top of this system that can be used on its own, or as a complementary algorithm to traverse voids. With this approach, and for the first time, we demonstrate a stateless and delivery guaranteed geometric routing algorithm on VCS. When used in conjunction with our previously proposed aligned virtual coordinate system (AVCS), it out-performs not only all geometric routing protocols on VCS, but also geographic routing with accurate location information. For many years, scalable routing for wireless communication systems was a compelling but elusive goal. Recently, several routing algorithms that exploit geographic information (e.g. GPSR) have been proposed to achieve this goal. These algorithms refer to nodes by their location, not address, and use those coordinates to route greedily, when possible, towards the destination. However, there are many situations where location information is not available at the nodes, and so geographic methods cannot be used. In this paper we define a scalable coordinate-based routing algorithm that does not rely on location information, and thus can be used in a wide variety of ad hoc and sensornet environments. Existing position-based unicast routing algorithms which forward packets in the geographic direction of the destination require that the forwarding node knows the positions of all neighbors in its transmission range. This information on direct neighbors is gained by observing beacon messages each node sends out periodically. Due to mobility, the information that a node receives about its neighbors becomes outdated, leading either to a significant decrease in the packet delivery rate or to a steep increase in load on the wireless channel as node mobility increases. In this paper, we propose a mechanism to perform position-based unicast forwarding without the help of beacons. In our contention-based forwarding scheme (CBF) the next hop is selected through a distributed contention process based on the actual positions of all current neighbors. For the contention process, CBF makes use of biased timers. To avoid packet duplication, the first node that is selected suppresses the selection of further nodes. We propose three suppression strategies which vary with respect to forwarding efficiency and suppression characteristics. We analyze the behavior of CBF with all three suppression strategies and compare it to an existing greedy position-based routing approach by means of simulation with ns-2. Our results show that CBF significantly reduces the load on the wireless channel required to achieve a specific delivery rate compared to the load a beacon-based greedy forwarding strategy generates. Routing in ad hoc networks is a complicated task because of many reasons. The nodes are low-memory, low-powered, and they cannot maintain routing tables large enough for well-known routing protocols. Because of that, greedy forwarding at intermediate nodes is desirable in ad hoc networks. Also, for traffic engineering, multi-path capabilities are important. So, it is desirable to define routes at the source like in source based routing (SBR) while performing greedy forwarding at intermediate nodes. We investigate trajectory-based routing (TBR) which was proposed as a middle-ground between SBR and greedy forwarding techniques. In TBR, source encodes trajectory to be traversed and embeds it into each packet. Upon the arrival of each packet, intermediate nodes decode the trajectory and employ greedy forwarding techniques such that the packet follows its trajectory as much as possible. In this paper, we address various issues regarding implementation of TBR. We also provide techniques to efficiently forward packets along a trajectory defined as a parametric curve. We use the well-known Bezier parametric curve for encoding trajectories into packets at source. Based on this trajectory encoding, we develop and evaluate various greedy forwarding algorithms 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) We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions. GPSR makes greedy forwarding decisions using only information about a router's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's scalability on densely deployed wireless networks. Geographic forwarding has been widely studied as a routing strategy for large wireless networks, mainly due to the low complexity of the routing algorithm, scalability of the routing information with network size and fast convergence times of routes. On a planar network with no holes, Gupta and Kumar (2000) have shown that a uniform traffic demand of ominus(1 radicn log n) is achievable. However, in a network with routing holes (regions on the plane which do not have active nodes), geographic routing schemes such as GPSR or GOAFR could cause the throughput capacity to significantly drop due to concentration of traffic on the face of the holes. Similarly, geographic schemes could fail to support non-uniform traffic patterns due to spatial congestion (traffic concentration) caused by greedy "straight-line" routing. In this paper, we first propose a randomized geographic routing scheme that can achieve a throughput capacity of ominus(1 radicn) (within a poly-logarithmic factor) even in networks with routing holes. Thus, we show that our scheme is throughput optimal (up to a poly-logarithmic factor) while preserving the inherent advantages of geographic routing. We also show that the routing delay incurred by our scheme is within a poly-logarithmic factor of the optimal throughput-delay trade-off curve. Next, we construct a geographic forwarding based routing scheme that can support wide variations in the traffic requirements (as much as ominus(1) rates for some nodes, while supporting ominus(1 radicn) for others). We finally show that the above two schemes can be combined to support non-uniform traffic demands in networks with holes. Geographic routing is a family of routing algorithms that uses geographic point locations as addresses for the purposes of routing. Such routing algorithms have proven to be both simple to implement and heuristically effective when applied to wireless sensor networks. Greedy routing is a natural abstraction of this model in which nodes are assigned virtual coordinates in a metric space, and these coordinates are used to perform point-to-point routing. Here we resolve a conjecture of Papadimitriou and Ratajczak that every 3-connected planar graph admits a greedy embedding into the Euclidean plane. This immediately implies that all 3-connected graphs that exclude K3.3 as a minor admit a greedy embedding into the Euclidean plane. Additionally, we provide the first non-trivial examples of graphs that admit no such embedding. These structural results provide efficiently verifiable certificates that a graph admits a greedy embedding or that a graph admits no greedy embedding into the Euclidean plane. 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. 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.