Evaluation of node weight-based congestion adaptive routing algorithm in MANET packet routing
Research Paper (postgraduate) 2017 21 Pages
II. RELATED WORK
III. SIMULATION FRAMEWORK
A. Energy Calculations
B. Packet Delivered Plots
C. Number of Dead Nodes Plots
V. DISCUSSIONS AND COMPARISONS
Evaluation Of Node Weight-Based Congestion Adaptive Routing Algorithm In MANET Packet Routing
Rebecca Nyasuguta Arika1, A. Mindila2, W. Cheruiyot3
Jomo Kenyatta University Of Agriculture And Technology – Nairobi , Kenya
Abstract – mobile ad-hoc networks are valuable in situations where communication support is required but there are no fixed infrastructures that are in existence and movement of communicating parties is permitted. This is a typical case in military where the personnel move randomly in the battle field but need to maintain constant communication to the commanding stations. Unfortunately, mobile ad-hoc networks exhibit unexpected behavior when transmitting multiple data streams under heavy traffic load, more so when these data streams are destined to common terminal. Therefore, congestion is one of the most limiting factors for efficient packet transmissions over wireless ad-hoc networks. This is because it introduces problems such as long delay, high overhead and low throughput over the communication channels. To surmount these issues, researchers have proposed many congestion aware and congestion adaptive routing protocols. One of such a congestion control algorithm is the one based on node weight for the computation of usable network routes. To achieve this objective, number of dead links, um of energy for packet transmission over a mobile ad hoc network, and the number of packets delivered over the network were used as performance metrics. The results obtained indicated that the node weight-based path calculation approach yields better performance compared to the existing congestion control algorithms.
Keyword: Evaluation, sum of energy, dead links, throughput
The primary issue in mobile wireless ad hoc networks is network congestion and traffic blocking. In these networks, congestion is prompted when there is limited availability of resources. Moreover, in mobile ad hoc networks, packet transmissions suffer from interference and fading. This can be attributed to the shared wireless channel among the communicating entities. According to Shrivastava and Tomar (2016), the dynamic topology experienced in mobile ad hoc networks can also lead to interference and fading. In their study, Valarmathi and Chandrasekaran (2010) point out those transmission errors are likely to lead to burdens on the network since the packets in question have to be retransmitted.
With the proliferation of multimedia communications, there is an increasing need for communication gadgets to support multimedia communication in mobile ad hoc networks. The challenge of this kind of communication, as Jain and Kokate (2011) point out, is that the bulky amount of real-time traffic is more often in form of bursts. Moreover, these transmissions are bandwidth intensive and hence susceptible to congestion. The flip side of congestion is that it can lead to packet losses and bandwidth degradation. On recovery from congestion, mobile ad hoc networks tend to and waste time and energy. In their study, Shrivastava et al., (2011) illustrated that it is not possible to get rid of congestion problem completely from the networks. However, it is feasible to bind the impact of congestion on network efficiency. This can be done by using suitable procedures and rules for traffic flow. To minimize congestion in mobile ad hoc networks, routing algorithms are used.
According to Wischik et al., (2015), these routing algorithms can be categorized as follows: proactive routing versus on-demand routing, or single-path routing versus multipath routing. Raiciu et l.,(2014) point out that in proactive protocols, paths between every two nodes are determined in advance even though no transmission is in demand. Consequently, this approach is not suitable for large networks. This is because a large number of unused routes will need to be maintained and the periodic updating may incur. Effectively, this will overwhelm processing and communication overhead. On the other hand, on-demand approaches are more efficient because a route is discovered only when network nodes need a transmission over this route, and this path is released upon the termination of the transmission.
The challenge of this approach is that when a route is disconnected due to failure or node mobility, which often occurs in MANETs, the delay and overhead due to new route establishment may be momentous. Ford et al., (2010) illustrate that in an effort to address this issue, multiple paths to the destination may be used, and this constitutes multipath routing protocols. Using this technique, an alternate route can be established quickly when the existing route is down. However, there is always a trade-off due to the multiplied overhead due to concurrent maintenance of such paths. Moreover, the utilization of multiple paths does not balance routing load in a manner superior to single-path unless very large number of routes are employed. This is in turn costly and therefore practically infeasible.
II. RELATED WORK
In a mobile ad hoc network, there are frequent and unpredictable topological changes due to the random mobility of the communicating nodes. This has the effect of rendering the task of routing a challenging one. The mobile ad hoc networks rely on the routing scheme to provide the expected Quality of Service. According to Al-Fares et al., (2010), round trip time measurements have been utilized to distribute load between routes in Multi-path Source Routing (MSR). Additionally, distributed multi-path DSR protocol (MP-DSR) has been developed to improve the quality of service with respect to end-to-end reliability. In this method, the routing protocol forwards outgoing packets along multiple paths that are subjected to an end-to-end reliability model.
On the part of congestion recovery, Klinkowski et al.,(2014) explain that Split Multi-path Routing (SMR), using several routes of maximally disjoint paths have been employed to minimize route recovery process and control message overhead [. The idea is to let the protocol employ a per-packet allocation scheme to allocate data packets into manifold paths of active sessions. This, as Miguel (2013) illustrate, effectively prevents nodes from being congested in heavily loaded traffic situations.
In their study, Haibo et al., (2015) illustrated that intelligent use of multi-path technique in Dynamic Source Routing (DSR) protocol can greatly condense the frequency of query floods. Moreover, Kaur and Singhai (2015) explain that a network queuing model in MSR that could incorporate cross-traffic among multiple paths has been developed. The load balancing was considered as an optimization problem and the results were confirmed with the existing model.
According to Klinkowski et al., (2014), the computation of the sum of energy level needed for packet transmission is crucial in the to determination of the battery level of every node during active data transmission. In their work, they assumed that the battery level of a wireless node reduced when the node initiated data transmission or when the node forwarded packets. The algorithm calculated the energy level of a node before forwarding data packet to the next node. Additionally, the queue length is yet another vital resource utilization parameter and is considered the best indicator of congestion. This is because when a data packet needs to wait in queue for longer time, the possibility is high that unexpected delay in transmission or dropping of packets may occur.
Kaur and Singhai (2015) discussed that for streaming multimedia applications, throughput alone cannot be regarded as performance indicator. They explained that traffic load may have a misleading effect on other parameters. As such, measurements of parameters including packet loss, end-to-end delay and jitter to further validate the network performance were considered crucial. Packet loss and delay are the most important measures for mobile ad hoc traffic. The present work utilized energy calculation, packets delivered, and number of dead links as performance indicators.
III. SIMULATION FRAMEWORK
A. Energy Calculations
The mobile ad hoc network nodes require energy to send the packets across the network to the nearest base station. As such, one of the codes developed included modules for energy computations. Figure 1 gives a snippet of the source code that was employed for energy calculations. The variables that are included in the calculation of the sum of energy include number of available paths, energy emitted, energy received from base station, aggregate energy for assembling the packets, energy for all paths, total energy for the whole MANET architecture, and the size of the packet to be transmitted.
The sum energy is then calculated as the total of energy for data aggregation and the energy for transferring packets from the source to the base station. That is:
Sum of energy = data aggregation energy + packet transfer energy
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Figure 1: Energy Calculations for the Selected Path Snippet
This figure illustrates the fact that there is some looping through the source code in that for each packet transferred in a MANET environment, the sum of energy for each of the available path through which packets may be sent has to be computed.
B. Packet Delivered Plots
One of the plots that were crucial for the evaluation of the developed algorithm was that of the number of packets delivered across the MANET environment. As Figure 2 demonstrates, the parameters that were employed as input variables included the clustering algorithm that was called via programming method calling technique, number of possible clusters, MANET model, and the mobile nodes model (the way the mobile nodes are distributed within the coverage area).