A mobile ad hoc network (MANET) is a self-starting dynamic network, comprising of mobile nodes, where all the participating nodes are voluntarily transmitting the packets from one place to another place and assumed to be dynamic with more or less relative speed in an arbitrary direction [1–4]. Hence, it is highly difficult to ensure the long-term guaranteed path from one node to the other node. Typically, the MANET is employed for emergency scenarios like military operations, monitoring animal habitats, and disaster relief operation where there is a need for communication network immediately following some major event or some temporary requirement like a conference or seminar at a new place where no earlier network infrastructure exist and an alternative solution is needed [5–7].
The emergence of real-time applications and the widespread utilization of wireless and mobile devices have generated the need to provide quality of service (QoS) support in wireless and mobile networking environments. It is very important to determine the QoS of the network which is primarily depending upon the network. In MANET, there are several parameters which influence enhancing the QoS of the network such as throughput, end-to-end delay, packet delivery ratio, and jitter [8, 9]. The aforementioned parameters are improved by altering the algorithm, protocol, and mechanisms.
Typically, QoS refers to the ability of a network to provide improved service to selected network traffic over various underlying technologies . QoS routing requires finding not only a route from a source to a destination but also a route that satisfies the end-to-end QoS requirement. QoS is more difficult to guarantee in ad hoc networks than in most other types of networks, because the wireless bandwidth is shared among adjacent nodes and the network topology changes owing to the movement of nodes. Hence, in order to provide QoS in MANET, the extensive collaboration between nodes is essential to establish the route and to secure the resources [10, 11]. Primarily, QoS can be achieved by two ways: (a) over-provisioning and (b) traffic engineering. Over-provisioning employs the best-effort approach and simply increases the available resources. Alternatively, traffic engineering tries to exploit resources efficiently and to make the network QoS aware which includes additional service classes, admission control, and resource reservations .
QoS provisioning improves the end-to-end performance in heavily loaded networks through QoS aware routing, admission control, resource reservation, traffic analysis, and scheduling . The goal of QoS provisioning is to achieve more deterministic network behaviors, where in turn, the information carried by the network can be delivered accurately and network resources can be utilized properly. However, there still remains a significant challenge to provide QoS solutions and maintain end-to-end QoS with user mobility. QoS provisioning will lead to an increase in computational and communicational cost. The QoS provisioning approaches can be classified into two categories: hard QoS and soft QoS approaches. If QoS requirements of a connection are guaranteed to be met for the whole duration of the session, the QoS approach is termed as hard QoS approach. In MANETs, it is very challenging to provide hard QoS guarantees to user applications. In soft QoS, the Qos requirements are not guaranteed for the entire session . There are several challenges that are reported while providing Qos in MANETs such as hidden terminal problem, lack of central coordination, insecure medium, limited resource availability, dynamically varying network topology, error-prone shared radio channel, and imprecise state of information. In MANETs, one of the most crucial components of a system for QoS provisioning is to estimate the state of the network resources and thereby decide which application data can be processed. To estimate the available bandwidth in a heavily loaded wireless network is a non-trivial task due to the aforementioned factors of wireless networks .
In the literature, there are several non-linear programming methods such as genetic algorithm, fuzzy logic, and neural network which are employed to find the feasible routes in order to improve the QoS. The QoS of MANETs is enhanced using computational intelligent techniques , fuzzy multi-objective routing , flooding limited and multiconstrained multicast routing using genetic algorithm , multicast protocol (codepipe) [17, 18], optimize congestion and dilation , physarum optimization for long stating computational problems (Steiner tree problem) with low complexity and high parallelism , biology-based algorithm , interference-based topology control algorithm , spatial reusability aware routing , etc.
In MANET, there is no reliable mechanism to provide QoS; therefore, research in this field has received much attention from the last decade. Though there is no QoS mechanism for IEEE 802.11-based MANETs, it can provide some QoS level through service differentiation, due to the IEEE 802.11e amendment. However, no solution has been standardized for estimation of bandwidth which becomes necessary for guaranteed QoS. The estimation of available resources still represents one of the main issues for QoS enhancement. Hence, the bandwidth estimation must be accurate enough to assure the admission of right connections.
In this paper, a priority aware dynamic source routing (DSR) is proposed to enhance the QoS by estimating the available bandwidth in IEEE 802.11-based MANET. Five connections are considered, and their priority is assigned according to its data rates. The QoS parameters throughput, packet delivery ratio, and end-to-end delay are estimated with respect to the simulation time and total number of nodes in the network with and without mobility.
This paper is organized as follows: The reported bandwidth estimation techniques to enhance the QoS are presented in Section 2. The proposed priority aware DSR protocol is given in Section 3. The simulation results of priority aware DSR (PA-DSR) such as the effect of throughput, packet delivery ratio, and end-to-end delay with respect to the simulation time and nodes are discussed in Section 4. Finally, Section 5 concludes the paper.