1. Introduction
Geographic routing protocols for ad-hoc networks are becoming more and more popular within the research community. One of the reasons for their growing popularity is the fact that in regions in which there is not much or no communication infrastructure, or the existing infrastructure is expensive and not convenient to use, wireless nodes may still be able to communicate and send packets because of the nature of ad-hoc networks. This kind of system is suitable for VANET [1]
and MANET.
Wireless Ad- Hoc networks do not require communication infrastructure. A wireless ad hoc network is a decentralized type of wireless network [2]. Ad hoc is a Latin word and it means "for this purpose" [3]. Routers are needed in wired networks and access points are needed in infrastructural wireless networks, but not in a Wireless Ad- Hoc network. Each node contributes in routing by forwarding data for other nodes, so the determination of which nodes forward data is made dynamically using some kind of routing protocol.
One kind of Wireless Ad- Hoc network is mobile ad hoc network (MANET). Nodes in MANET can move independently of each other, but all of the nodes can be used in forwarding data. While moving, nodes create new links with the nodes that are within their transmission range frequently.
Geographic routing is a kind of routing principle that relies on geographic position information.
Main idea of geographic routing is that nodes are using geographical position to choose next hop node. In geographic routing we assume that nodes are aware of their own position. Position can be obtained with some sort of position navigation system (e.g. GPS). Before making forwarding decision, geographic location of the destination node and neighboring nodes is necessary. The destination of a packet is the final node to which the packet is traversing. Neighboring nodes are the nodes that are within the limits of radio transmission range of the node that is making the routing decision. Local topology is a local chart that each node creates and it consists of nodes that are in the nodes vicinity.
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One of the most popular geographic routing protocols is Greedy perimeter stateless routing (GPSR) [4].In GPSR the routing decision is made using merely local topology, which means there is no global topology. This kind of characteristics makes geographic routing completely stateless.
Each node keeps track of its neighboring nodes by maintaining a neighbor table [4]. Greedy forwarding of data represents the method of deciding to which node to transmit data. GPSR operates in greedy forwarding manner where a node transmits data to a neighboring node that is geographically closest to the destination. However GPSR can be stuck in local minima situation (Figure 1), when there is no any neighboring node closer to the destination than the node that has data packets to transmit. In this situation GPSR recovers by routing around the perimeter of the void where greedy forwarding is impossible (Figure 1).
Figure 1: Local Minima Problem (left) and Node x’s void with respect to destination D. (right)[4]
It uses the right hand rule for traversing a graph which is depicted in Figure 2.
Figure 2: The right-hand rule (interior of the triangle). x receives a packet from y, and forwards it to its first neighbor counterclockwise about itself.[4]
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Local topology needs to be maintained with frequent beacons updates generated by nodes in the network. Beacon packets contain node’s position information and its identifier. Nodes broadcast beacon packets. This can be done periodically or the mechanism can be made entirely reactive.
Periodic beaconing is a simple beaconing algorithm where periodically, each node transmits a beacon message. Reactive beaconing is a sort of on demand beaconing where nodes ask for beacons with a broadcast “neighbor request” only when they have data packets to send.
Periodic beaconing yields numerous problems and it can generate a large number of beacon messages which leads to high beacon overhead. Every node in the network sends position updates even to the nodes that are still or not transmitting data. This can worsen routing performance and produce numerous beacon packets, resulting in high update cost in high-mobility networks. Also, nodes that are highly mobile will not maintain accurate network topology due to the rapid changing of network topology.
Reactive beaconing procedure starts before each data transmission [5]; as a result, for networks that have a lot of source destination pairs, many unnecessary beacon messages are broadcasted. Furthermore, increased numbers of beacons lead to higher energy consumption at each node as nodes consume additional energy for transmission and reception of packets; as well it increases the probability of collision at the medium access control (MAC) layer. Packet delivery ratio, overall routing performance, beacon overhead and end-to-end delay are worsening by these problems. Having all these mentioned above it is useful to adapt the beaconing mechanism somehow.
We propose an adaptive beaconing mechanism called An Adaptive Energy-efficient Beaconing Mechanism for Geographic Routing (AEE). In further reading, when referring to our
beaconing mechanism we will be using the abbreviation AEE. Our beaconing mechanism adapts the beacon interval to the value ΔT based on the function of mobility of nodes and the number of data flows that are passing through that node. Nodes that are not extremely populari.e., nodes that are not frequently transmitting data packets and the nodes that are not very mobile will have long
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beacon interval. This makes higher mobile and popular nodes generate more frequent beacon updates than the nodes that are mostly static and non-popular. This lowers the beacon overhead and energy consumption.
We run a series of simulation to evaluate our strategy and to determine what sort of impact it makes on packet delivery fraction, beacon overhead, end to end delay, energy consumption, number of packet collisions in MAC layer, hop count and other network metrics.
The rest of the thesis is organized as follows: In Section 2, we discuss some related work. A thorough explanation of the AEE mechanism is given in Section 3. In Section 4 we present the results of our simulations showing performance improvements succeeded by a conclusion of our thesis in Section 5.
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