Probabilistic route discovery for Wireless Mobile Ad Hoc Networks (MANETs)

Mobile wireless ad hoc networks (MANETs) have become of increasing interest in view of their promise to extend connectivity beyond traditional fixed infrastructure networks. In MANETs, the task of routing is distributed among network nodes which act as both end points and routers in a wireless multi-hop network environment. To discover a route to a specific destination node, existing on-demand routing protocols employ a broadcast scheme referred to as simple flooding whereby a route request packet (RREQ) originating from a source node is blindly disseminated to the rest of the network nodes. This can lead to excessive redundant retransmissions, causing high channel contention and packet collisions in the network, a phenomenon called a broadcast storm. To reduce the deleterious impact of flooding RREQ packets, a number of route discovery algorithms have been suggested over the past few years based on, for example, location, zoning or clustering. Most such approaches however involve considerably increased complexity requiring additional hardware or the maintenance of complex state information. This research argues that such requirements can be largely alleviated without sacrificing performance gains through the use of probabilistic broadcast methods, where an intermediate node rebroadcasts RREQ packets based on some suitable forwarding probability rather than in the traditional deterministic manner. Although several probabilistic broadcast algorithms have been suggested for MANETs in the past, most of these have focused on “pure” broadcast scenarios with relatively little investigation of the performance impact on specific applications such as route discovery. As a consequence, there has been so far very little study of the performance of probabilistic route discovery applied to the well-established MANET routing protocols. In an effort to fill this gap, the first part of this thesis evaluates the performance of the routing protocols Ad hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR) augmented with probabilistic route discovery, taking into account parameters such as network density, traffic density and nodal mobility. The results reveal encouraging benefits in overall routing control overhead but also show that network operating conditions have a critical impact on the optimality of the forwarding probabilities. In most existing probabilistic broadcast algorithms, including the one used here for preliminary investigations, each forwarding node is allowed to rebroadcast a received packet with a fixed forwarding probability regardless of its relative location with respect to the locations of the source and destination pairs. However, in a route discovery operation, if the location of the destination node is known, the dissemination of the RREQ packets can be directed towards this location. Motivated by this, the second part of the research proposes a probabilistic route discovery approach that aims to reduce further the routing overhead by limiting the dissemination of the RREQ packets towards the anticipated location of the destination. This approach combines elements of the fixed probabilistic and flooding-based route discovery approaches. The results indicate that in a relatively dense network, these combined effects can reduce the routing overhead very significantly when compared with that of the fixed probabilistic route discovery. Typically in a MANET there are regions of varying node density. Under such conditions, fixed probabilistic route discovery can suffer from a degree of inflexibility, since every node is assigned the same forwarding probability regardless of local conditions. Ideally, the forwarding probability should be high for a node located in a sparse region of the network while relatively lower for a node located in a denser region of the network. As a result, it can be helpful to identify and categorise mobile nodes in the various regions of the network and appropriately adjust their forwarding probabilities. To this end the research examines probabilistic route discovery methods that dynamically adjust the forwarding probability at a node, based on local node density, which is estimated using number of neighbours as a parameter. Results from this study return significantly superior performance measures compared with fixed probabilistic variants. Although the probabilistic route discovery methods suggested above can significantly reduce the routing control overhead without degrading the overall network throughput, there remains the problem of how to select efficiently forwarding probabilities that will optimize the performance of a broadcast under any given conditions. In an attempt to address this issue, the final part of this thesis proposes and evaluates the feasibility of a node estimating its own forwarding probability dynamically based on locally collected information. The technique examined involves each node piggybacking a list of its 1-hop neighbours in its transmitted RREQ packets. Based on this list, relay nodes can determine the number of neighbours that have been already covered by a broadcast and thus compute the forwarding probabilities most suited to individual circumstances

Neighbour coverage: a dynamic probabilistic route discovery for mobile ad hoc networks

Blind flooding is extensively use in ad hoc routing protocols for on-demand route discovery, where a mobile node blindly rebroadcasts received route request (RREQ) packets until a route to a particular destination is established. This can potentially lead to high channel contention, causing redundant retransmissions and thus excessive packet collisions in the network. Such a phenomenon induces what is known as broadcast storm problem, which has been shown to greatly increase the network communication overhead and end-to-end delay. In this paper, we show that the deleterious impact of such a problem can be reduced if measures are taken during the dissemination of RREQ packets. We propose a generic probabilistic method for route discovery, that is simple to implement and can significantly reduce the overhead associated with the dissemination of RREQs. Our analysis reveals that equipping AODV with probabilistic route discovery can result in significant reduction of routing control overhead while achieving good throughput

Geographic Centroid Routing for Vehicular Networks

A number of geolocation-based Delay Tolerant Networking (DTN) routing protocols have been shown to perform well in selected simulation and mobility scenarios. However, the suitability of these mechanisms for vehicular networks utilizing widely-available inexpensive Global Positioning System (GPS) hardware has not been evaluated. We propose a novel geolocation-based routing primitive (Centroid Routing) that is resilient to the measurement errors commonly present in low-cost GPS devices. Using this notion of Centroids, we construct two novel routing protocols and evaluate their performance with respect to positional errors as well as traditional DTN routing metrics. We show that they outperform existing approaches by a significant margin.Comment: 6 page

Self-Organized Routing For Wireless Micro-Sensor Networks

In this paper we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless micro-sensors (as found, for example, in security or environmental monitoring applications). In these networks, the battery life of individual sensors is typically limited by the power required to transmit their data to a receiver or sink. Thus effective network routing algorithms allow us to reduce this power and extend both the lifetime and the coverage of the sensor network as a whole. However, implementing such routing algorithms with a centralized controller is undesirable due to the physical distribution of the sensors, their limited localization ability and the dynamic nature of such networks (given that sensors may fail, move or be added at any time and the communication links between sensors are subject to noise and interference). Against this background, we present a distributed mechanism that enables individual sensors to follow locally selfish strategies, which, in turn, result in the self-organization of a routing network with desirable global properties. We show that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach

Improvement to efficient counter-based broadcast scheme through random assessment delay adaptation for MANETs

Flooding, the process in which each node retransmits every uniquely received packet exactly once is the simplest and most commonly used mechanism for broadcasting in mobile ad hoc networks (MANETs). Despite its simplicity, it can result in high redundant retransmission, contention and collision, a phenomenon collectively referred to as broadcast storm problem. To mitigate this problem, several broadcast schemes have been proposed which are commonly divided into two categories; deterministic schemes and probabilistic schemes. Probabilistic methods are quite promising because they can reduce the number of redundant rebroadcast without any control overhead. In this paper, we investigate the performance of our earlier proposed efficient counter-based broadcast scheme by adapting its random assessment delay (RAD) mechanism to network congestion. Simulation results revealed that this simple adaptation achieves superior performance in terms of saved rebroadcast, end-to-end delay and reachability

Una comparación de algoritmos basados en trayectoria granular para el problema de localización y ruteo con flota heterogénea (LRPH)

Indexación: Scopus.We consider the Location-Routing Problem with Heterogeneous Fleet (LRPH) in which the goal is to determine the depots to be opened, the customers to be assigned to each open depot, and the corresponding routes fulfilling the demand of the customers and by considering a heterogeneous fleet. We propose a comparison of granular approaches of Simulated Annealing (GSA), of Variable Neighborhood Search (GVNS) and of a probabilistic Tabu Search (pGTS) for the LRPH. Thus, the proposed approaches consider a subset of the search space in which non-favorable movements are discarded regarding a granularity factor. The proposed algorithms are experimentally compared for the solution of the LRPH, by taking into account the CPU time and the quality of the solutions obtained on the instances adapted from the literature. The computational results show that algorithm GSA is able to obtain high quality solutions within short CPU times, improving the results obtained by the other proposed approaches.https://revistas.unal.edu.co/index.php/dyna/article/view/55533/5896