Introduction
Wireless Ad Hoc networks are decentralized communication systems in which nodes dynamically establish connections without relying on fixed infrastructure. Their flexibility, rapid deployment capability, and robustness make them suitable for emergency response operations, military communications, and Internet of Things environments.
However, the performance of Ad Hoc networks significantly degrades when node density increases. As the number of nodes grows within a limited geographic area, the average node degree increases accordingly. A larger number of neighboring nodes results in a higher volume of routing control messages, which increases channel contention, packet collisions, and energy consumption.
Traditional routing protocols such as AODV, DSR, and OLSR were originally designed for networks with moderate density and therefore do not scale efficiently in highly dense environments. This motivates the development of new approaches capable of reducing routing overhead while preserving network connectivity.
Related work
Reactive routing protocols such as the Ad hoc On Demand Distance Vector (AODV) protocol and the Dynamic Source Routing (DSR) protocol establish routes only when data transmission is required. Although this strategy reduces periodic control traffic, route discovery relies on network wide flooding of Route Request (RREQ) packets, which may cause the broadcast storm problem in dense networks [1]–[3].
Proactive routing protocols, such as the Optimized Link State Routing (OLSR) protocol, attempt to reduce redundant retransmissions using Multi Point Relay (MPR) nodes. Despite these optimizations, periodic topology control messages still generate considerable overhead when the number of nodes increases [4].
Another research direction focuses on link quality aware routing metrics. Techniques based on Signal to Noise Ratio (SNR) or Received Signal Strength Indicator (RSSI) improve route reliability by avoiding unstable communication links [5]. However, these methods mainly focus on transmission quality and do not explicitly control routing graph density.
Topology control approaches attempt to construct sparse network subgraphs while preserving connectivity. Degree based techniques limit the number of active neighbors for each node in order to reduce interference and routing overhead [6]. Nevertheless, many degree based methods do not consider dynamic link quality, which may lead to unstable routing paths.
Therefore, an integrated method that simultaneously considers link reliability and node connectivity is required. The approach proposed in this paper addresses this issue by combining SNR and node degree into a unified routing eligibility metric.
Recent research has also focused on improving routing scalability using link-quality metrics and adaptive topology control mechanisms. For example, Pandey and Singh proposed a link-quality-aware routing approach for MANETs that improves route stability and reduces packet loss [8].
Other studies investigate topology-aware routing and adaptive control techniques to reduce routing overhead and improve network robustness in highly dynamic Ad-Hoc environments [9].
In addition, cross-layer approaches have been proposed to optimize interactions between routing, MAC, and physical layers, improving overall network efficiency and adaptability in mobile Ad-Hoc systems [10].
Proposed Method
The wireless Ad-Hoc network is represented as an undirected graph G(V,E), where V is the set of nodes and E is the set of bidirectional communication links. If the network is deployed over an area A, the node density is defined as ρ = N/A. For a transmission radius R, the expected node degree can be approximated as di ≈ ρπR².
The degree of node vi is defined as di = |Ni|, where Ni represents the set of neighboring nodes. In dense networks, nodes with very high connectivity may generate excessive routing overhead.
Normalized values are introduced to combine SNR and node degree: SNR* = SNRij / SNRmax and d* = di / dmax. Both normalized parameters lie in the range [0,1].
The routing eligibility function is defined as Fij = α·SNR* + β·(1 − d*), where α + β = 1. Parameter α controls sensitivity to link quality, while β controls topology sparsification.
A communication link is included in the reduced routing graph if Fij ≥ θ, where θ is a predefined threshold parameter. The resulting routing graph becomes G′(V,E′), where E′ ⊂ E.
Each node periodically performs neighbor discovery, measures SNR values, computes node degree, normalizes parameters, calculates the routing eligibility metric, and filters links according to threshold θ to build a reduced adjacency list.
Simulation Setup
The network area was 1000×1000 m with 50–200 nodes. The baseline routing protocol was AODV, while the modified protocol implementing the proposed method was denoted AODV-SD.
Simulation Results
Simulation results demonstrate that the proposed routing density reduction mechanism significantly decreases routing control overhead compared with the standard AODV protocol. In dense scenarios with 150–200 nodes, routing overhead was reduced by approximately 20–35%.
At the same time, the Packet Delivery Ratio remained stable with only minor degradation. The average node degree in the routing graph decreased by approximately 15–30%, confirming the effectiveness of the logical density reduction mechanism.
A slight increase in end to end delay was observed due to reduced path redundancy. However, the delay remained within acceptable limits for typical Ad Hoc applications.
Conclusion
This paper proposed a method for reducing routing overhead in dense Ad Hoc networks based on a combined analysis of Signal to Noise Ratio and node degree. The method constructs a sparsified routing graph by filtering communication links according to a routing eligibility metric. Simulation results confirmed that the proposed approach improves routing scalability while maintaining acceptable network performance indicators.
References
[1] C. E. Perkins and E. M. Royer, “Ad-hoc On-Demand Distance Vector Routing,”Proc. IEEE Workshop on Mobile Computing Systems and Applications, pp. 90–100, 1999.
[2] D. B. Johnson, D. A. Maltz, and J. Broch, “DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks,” in Ad Hoc Networking, Addison-Wesley, 2001.
[3] S. Ni, Y. Tseng, Y. Chen, and J. Sheu, “The Broadcast Storm Problem in a Mobile Ad Hoc Network,” Proc. ACM/IEEE MobiCom, pp. 151–162, 1999.
[4] T. Clausen and P. Jacquet, “Optimized Link State Routing Protocol (OLSR),” RFC 3626, Internet Engineering Task Force (IETF), 2003.
[5] R. Draves, J. Padhye, and B. Zill, “Routing in Multi-Radio, Multi-Hop Wireless Mesh Networks,” Proc. ACM MobiCom, pp. 114–128, 2004.
[6] P. Santi, “Topology Control in Wireless Ad Hoc and Sensor Networks,”
ACM Computing Surveys, vol. 37, no. 2, pp. 164–194, 2005.
[7] M. Gerla and J. T. Tsai, “Multicluster, Mobile, Multimedia Radio Network,”
Wireless Networks, vol. 1, no. 3, pp. 255–265, 1995
[8] P. Pandey and R. Singh, “Efficient Routing in Mobile Ad Hoc Networks Based on Awareness of Link Quality,”Proc. International Conference on Computing, Communication and Intelligent Systems (ICCCIS),pp. 245–249, 2021.
[9] A. Waqas et al.,“A Method to Reduce Route Discovery Cost of UAV Ad Hoc Networks,” Wireless Communications and Mobile Computing, 2023.
[10] V. Ivanov et al.,“Cross-Layer Methods for Ad Hoc Networks: Review and Research Directions,”Future Internet, vol. 16, no. 1, 2024.
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