|Valmik Tilwari||Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia|
|MhD Nour Hindia||Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia|
|Kaharudin Dimyati||Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia|
|Mohamad Sofian Abu Talip|
In mobile ad hoc networks, limited energy resources and traffic congestion at the nodes are crucial issues due to the nodes being battery operated and flooding the network with packets, respectively. These issues degrade network routing performance in terms of quality of service. In this study, we proposed a contention window and residual battery-aware multipath routing scheme to enhance network performance. Our proposed routing scheme has successfully diverted the traffic load from a low energy node to a high energy node while also controlling congestion among intermediate nodes. A multi-criteria decision-making technique was also used for the selection criteria of an intermediate node in the optimal path, based on the mobility and window size contention of nodes. Eventually, the contention window and residual battery-aware multipath routing scheme has enhanced throughput, attenuated the packet loss ratio, and reduced the energy consumption in comparison to a conventional multipath optimized link state routing protocol routing scheme.
CRAM; Mobile ad hoc networks; Multi-criteria decision-making; Multipath optimized link state routing protocol; Quality of service
Rapid wireless technology development escalates upon the demands of its users. In 2019, people want everything to be controlled by their fingertips. Among the notable applications used in wireless technologies are mobile ad hoc networks (MANETs; Jabbar et al., 2017), device-to-device communication (Mumtaz et al., 2014), the Internet of things (Atzori et al., 2010), cognitive radio (Badoi et al., 2011), and heterogeneous networks (Peng et al., 2015). These technologies offer the biggest potential for reliable end user communication. Among them, MANETs provide prime solutions for user demand with self-organized and infrastructureless networks. In MANETs, user nodes collaborate with each other and acts as routers with end users building ad hoc networks for communication. To determine and maintain the best path for transferred data between source and destination nodes, optimal routing protocols for better quality of service (QoS) in the network need to be identified.
MANET routing protocols can be classified into three types, depending on their functionalities of instant reactive, proactive, and hybrid routing protocols. In reactive routing protocols, such as destination-sequenced distance-vectors (Hamid et al., 2015), and optimized link state routing protocol (OLSR; Yi & Parrein, 2017), source nodes initiate a route discovery process to transmit the data. By contrast, in proactive routing protocol (OLSR; Yi & Parrein, 2017), source nodes initiate a route discovery process to transmit the data. By contrast, in proactive routing protocols, such as dynamic source routing (Hui et al., 2016) and ad hoc on-demand distance vectors (Kabir, et al., 2015), every node always has network topology information in the form of table, owing to periodic transfer messages in the network. Whenever a source node needs to transmit packets, it will take routing information from the table to establish a network path. In hybrid routing protocols, such as zone routing protocol (Lin et al., 2017) and secure link state routing protocol (Sarkar et al., 2016), the routing decisions are made based on the geographical location of nodes to attain higher efficiency and scalability. However, if a destination node is in a given geographical area, it will use table-based routing, while destination nodes outside the geographical location use on-demand routing protocols.
Table-based routing protocols have one major drawback: every node in the network must exchange “HELLO” and “topology control” messages continuously with neighboring nodes. Such messaging increases the load burden of a network (i.e., traffic overhead). By contrast, on-demand routing protocol establishes routes only if a source node needs to transfer data, reducing the resultant load on the network. While research has been conducted on single path routing under a OLSR on-demand routing protocol (Sun et al., 2016), this protocol causes rapid energy depletion at the node due to high traffic congestion on a single node. That congestion degrades network performance and increases the possibility of link failure in the network, affecting packet loss and end-to-end delay (Li et al., 2017).
The multipath optimized link state routing protocol (MP-OLSR; Yi et al., 2011) resolves such issues by selecting multiple routes using multipath Dijkstra algorithms to establish connections between source and destination nodes. Moreover, the route selection process provides efficient communication and load balance among nodes by distributing packets to multiple paths. To solve the continuous exchange message flooding problem under the MP-OSLR protocol, the multipoint relay (MPR) concept has been introduced. MPR nodes are relay nodes that have at least two next-hop neighbor nodes. To mitigate network overhead, a source node only sends data packets to MPRs nodes. Energy efficient nodes are selected as the MPRs, so more reliable and robust route can be established by prolongs the lifetime of the route and network. However, the MP-OLSR routing scheme still faces challenges during the route selection process, due to rapid node depletion and traffic congestion on available paths. Based on these circumstances, this paper will focus on the selection of optimal routes from source to destination nodes in MANETs. We shall consider the status of the intermediate node during optimal route selection in terms of residual battery (RB) and contention window (CW) to improve the QoS. Moreover, the multi-criteria decision-making (MCDM) method will be used to determine the criteria of suitable nodes within an optimal route. Overall, the proposed routing scheme will be compared with the existing MP-OLSR routing scheme, the results will be expressed in the terms of the throughput, packets loss ratio, and energy consumption with various node speed.
The rest of this paper is organized as follows: section 2 illustrates the related works; section 3 describes the system model for optimal route selection; section 4 presents the simulation model results and discusses them further; and section 5 draws the conclusion.
This paper has identified the traffic congestion and quickly node exhaustion constraints. To enhance network performance with load balance among the nodes and increase the network lifetime, we have presented a CRAM scheme for MANETs. The proposed approach uses CW size, which depends upon the link quality and queue length at the nodes, to provide a better chance of channel access. To reduce the energy depletion of nodes, a drain rate concept was used to provide node energy efficiency and increase network life. Under the CRAM routing scheme, optimal route selection was based on the availability status of higher energy and CW size at a node. Overall, our results proved that the proposed approach provided better performance in terms of throughput, PLR, and energy consumption when compared to the conventional MP-OLSR scheme. The CRAM routing scheme is extremely applicable given a frequently changing network topology and the high speed of a wireless device, such as drone.
The authors would like to acknowledge EPSRC grant EP/P028764/1 (UM IF035-2017).
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