Towards optimising routing overhead in wireless multi-hop networks

Javaid, Nadeem; Bibi, A.; Mohammad, S. N.; Khan, Zahoor Ali; Alrajeh, Nabil

doi:10.1504/ijahuc.2015.069489

Cited by 4 publications

(13 citation statements)

References 18 publications

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“…Packet 0 is already ready to be processed for routing. There are several parameters such as IP precedence, DSCP, buffer size etc are exploited by RED based on which the packets are dropped before congestion occurs [24], [25]. We take the concepts of these techniques and exploit the ML techniques to automate the parametric setup of routing protocols of MANETs.…”

Section: B Random Early Detection Techniquementioning

confidence: 99%

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

2021

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Routing protocols in Mobile Ad Hoc Networks (MANETs) play a pivotal role in ensuring quality of service (QoS) and improving network performance. Selection of optimal routing protocol and suitable parameters for a given network scenario is a major task that ultimately affects the behavior of network. This work exploits machine learning (ML) techniques for the selection of adequate routing parameters and protocol by regression of parameters in given network scenario to ensure optimal performance. The network is trained based on parametric setup of expanding ring search mechanism (ERS) and random early detection (RED) technique to estimate network throughput, end to end (E2E) delay and packets delivery ratio (PDR) and is tested via wide-ranging simulations in varying network topologies. Both RED and ERS mechanisms are aimed to control link and node level congestion in the reactive routing protocols and our aim is to select the best suited parameters for given network topologies based on ERS and RED parametric setups and improve performance for ensuring QoS. ML algorithms are trained and tested for their performance in varying network topologies. We have exploited these models with best performance for ERS and RED based routing in given topological arrangements. The performance of the ML algorithms is evaluated on the basis of root mean squared error (RMSE) and mean absolute error (MAE) for regression settings. Prediction models with up to par RMSE and MAE out-turns are attained and exploited for selection of suitable ERS and RED parameters and routing protocols in order to ensure the QoS for given network scenario. Variants of standard routing protocols are devised based on their performance and the ML techniques are exploited for prediction of QoS parameters to decide on the optimal variant that attains significant improvement in performance. Results are shown to confirm that considerable improvement in QoS is attained. INDEX TERMS Ad hoc multi hop wireless networks, congestion control, expanding ring search, machine learning, mobile ad hoc networks, on demand routing protocols, random early detection, regression, quality of service.

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Section: B Random Early Detection Techniquementioning

confidence: 99%

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

2021

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“…Authors in [12] proposed a detailed framework for reducing routing overhead. The control overhead for route maintenance operation of chosen protocols: plain flooding of DSDV, Multi-Point Relays (MPRs) flooding in OLSR and Fish-eye scope routing of FSR, is modeled according to appropriate computed probabilities.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, d f of k N becomes zero. The routing control overhead of the above discussed traditional flooding in terms of CE, CE k N flooding , is computed using equation 1, taken from [12].…”

Section: Flooding-based Routing Strategymentioning

confidence: 99%

“…total . This amount is in terms of energy consumption for transmitting routing control packets (CE) and time spent for routing (CT ) and is computed using equation 3 [12].…”

Section: Flooding-based Routing Strategymentioning

confidence: 99%

“…Further, the addition of NRL in existing schemes show significant improvements in terms of throughput and delay. It is worth mentioning that this study has been conducted by inspiring from [8]- [11] and [12]. The comparison of the performance of the selected routing protocols is done by simulating them in NS-2 environment.…”

Section: Introductionmentioning

confidence: 99%

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On Modeling Optimizations and Enhancing Routing Protocols for Wireless Multihop Networks

Al-Zahrani

2020

IEEE Access

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The contribution of this paper is two fold. It first analyses the flooding strategies for the Wireless Multihop Networks (WMNs) then it enhances the reactive and proactive routing protocols. For analysis purpose, we select four widely used flooding techniques for routing: i. traditional flooding, ii. Time-To-Live based Expanding Ring Search (TTL-based ERS) flooding scheme, iii. TTL-based Scope Routing (SR) flooding and iv. Multi-Point Relays (MPR) flooding. These techniques play a vital role and act as a backbone for routing protocols. Therefore, we compare efficiency of these techniques for six widely used routing protocols: Ad-hoc On-demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), DYnamic MANET On-demand (DYMO), Fish-eye Scope Routing (FSR) and Optimized Link State Routing (OLSR). DSDV uses traditional flooding, AODV, DSR and DYMO use TTL-based ERS flooding, FSR uses TTL-based SR flooding and OLSR uses MPR flooding. This paper also presents mathematical models for flooding techniques and studies the affects of these techniques on their respective protocols in terms of energy and time consumption. This is done to measure the cost incurred by the routing protocols in the form of routing overhead and latencies. A novel contribution of this work is the enhancement in search set values and intervals of routing algorithms to improve the efficiency of selected existing protocols. A detailed comparison analysis of selected protocols with their default and enhanced routing algorithms in NS-2 is also a part of this work.INDEX TERMS Time-To-Live, expanding ring, destination sequenced distance vector, DYnamic MANET On-demand, ad-hoc on-demand distance vector, wireless multihop networks.

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An Efficient Routing Protocol Using an Improved Distance-Based Broadcasting and Fuzzy Logic System for VANET

Kandali¹,

Bennis²

2020

IJIES

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The high mobility and frequent changes in the network topology remain a great divergence between Vehicular Ad-hoc Network (VANET) and Mobile Ad-hoc Network (MANET). This makes existing routing protocols for MANET imperfect for a VANET environment. In order to ensure good data transmission between vehicles, it is mandatory to improve these protocols to this new environment. Ad-hoc On-demand Distance Vector (AODV) is one of the most proposed reactive protocols in the literature for MANET, but its direct application in VANET returns poor performance results. In this paper, we propose an Efficient Routing Protocol using an improved Distance-Based Broadcasting and Fuzzy Logic System (ERPFL), which is an extension of the AODV routing protocol. In the first, the proposed scheme uses an improved Distance-Based Broadcasting method in route discovery process. Each vehicle selects the most suitable neighbours to send a route request, using different mobility parameters such as distance, velocity and direction. Then, the destination vehicle selects the best route from the most reliable routes received, using the Fuzzy Logic System. Link Expiration Time and Link Reliability Model are considered input metrics for this system. The simulation results show that ERPFL saves network bandwidth resources which contributes to a higher data transmission ratio. In addition, our proposed protocol efficiently distributes data packets, decreases the hop count and reduces network routing time.

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Towards optimising routing overhead in wireless multi-hop networks

Cited by 4 publications

References 18 publications

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

On Modeling Optimizations and Enhancing Routing Protocols for Wireless Multihop Networks

An Efficient Routing Protocol Using an Improved Distance-Based Broadcasting and Fuzzy Logic System for VANET

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