Optimized Link State Routing Protocol (OLSR)

Network Working GroupNetwork Working GroupThis 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

Game theoretic approach in routing protocols for wireless mobile ad hoc networks

Mobile Ad hoc Networks (MANETs) are becoming popular as a means of providing communication among a group of people. Because of self-configuring and self-organizing characteristics, MANETs can be deployed quickly. There is no infrastructure defined in the network, therefore all of the participating nodes relay packets for other nodes and perform routing if necessary. Because of the limitations in wireless transmission range, communication links could be multi-hop. Routing protocol is the most important element of MANET. Routing protocols for MANET can broadly be classified as proactive routing protocol and reactive routing protocol. In proactive routing protocols like Destination Sequence Distance Vector (DSDV), mobile nodes periodically exchange routing information among themselves. Hence proactive routing protocols generate high overhead messages in the network. On the other hand, reactive routing protocols like Ad hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) work on-demand. Hence reactive routing protocols generate fewer number of overhead messages in the network compared to proactive routing protocols. But reactive routing protocols use a global search mechanism called flooding during the route discovery process. By flooding mechanism a source node can discover multiple routes to a destination. Flooding generates a large number of overhead packets in the network and is the root cause of scaling problem of reactive routing protocols. Hierarchical Dynamic Source Routing (HDSR) protocol has been proposed in this dissertation to solve that scaling problem. The DSR protocol has been modified and optimized to implement HDSR protocol. HDSR protocol reduces the flooding problem of reactive routing protocols by introducing hierarchy among nodes. Two game theoretic models, Forwarding Dilemma Game (FDG) and Forwarding Game Routing Protocol (FGRP), is proposed to minimize the \u27flooding\u27 effect by restricting nodes that should participate in route discovery process based on their status. Both FDG and FGRP protocols reduce overhead packet and improve network performances in terms of delay packet delivery ratio and throughput. Both protocols were implemented in AODV and the resulting protocol outperformed AODV in our NS-2 simulations. A thorough connectivity analysis was also performed for FDG and FGRP to ensure that these protocols do not introduce disconnectivity. Surprisingly, both FDG and FGRP showed better connectivity compared to AODV in moderate to high node density networks

An analysis of the lifetime of OLSR networks

The Optimized Link State Routing (OLSR) protocol is a well-known route discovery protocol for ad-hoc networks. OLSR optimizes the flooding of link state information through the network using multipoint relays (MPRs). Only nodes selected as MPRs are responsible for forwarding control traffic. Many research papers aim to optimize the selection of MPRs with a specific purpose in mind: e.g., to minimize their number, to keep paths with high Quality of Service or to maximize the network lifetime (the time until the first node runs out of energy). In such analyzes often the effects of the network structure on the MPR selection are not taken into account. In this paper we show that the structure of the network can have a large impact on the MPR selection. In highly regular structures (such as grids) there is even no variation in the MPR sets that result from various MPR selection mechanisms. Furthermore, we study the influence of the network structure on the network lifetime problem in a setting where at regular intervals messages are broadcasted using MPRs. We introduce the ’maximum forcedness ratio’, as a key parameter of the network to describe how much variation there is in the lifetime results of various MPR selection heuristics. Although we focus our attention to OLSR, being a widely implemented protocol, on a more abstract level our results describe the structure of connected sets dominating the 2-hop neighborhood of a node

Performance Comparison of the RPL and LOADng Routing Protocols in a Home Automation Scenario

RPL, the routing protocol proposed by IETF for IPv6/6LoWPAN Low Power and Lossy Networks has significant complexity. Another protocol called LOADng, a lightweight variant of AODV, emerges as an alternative solution. In this paper, we compare the performance of the two protocols in a Home Automation scenario with heterogenous traffic patterns including a mix of multipoint-to-point and point-to-multipoint routes in realistic dense non-uniform network topologies. We use Contiki OS and Cooja simulator to evaluate the behavior of the ContikiRPL implementation and a basic non-optimized implementation of LOADng. Unlike previous studies, our results show that RPL provides shorter delays, less control overhead, and requires less memory than LOADng. Nevertheless, enhancing LOADng with more efficient flooding and a better route storage algorithm may improve its performance