A Novel Hybrid VANET Routing Protocol with Dynamic Power Management for Performance Enhancement

1. Introduction

Vehicular Ad-hoc Networks (VANETs), a specialised subclass of Mobile Ad-hoc Networks (MANETs), constitute the fundamental communication infrastructure for contemporary Intelligent Transportation Systems (ITS). These networks are integral to a diverse array of applications, encompassing safety-critical functions such as collision avoidance and emergency alert dissemination, as well as efficiency-oriented services like dynamic traffic flow optimisation. Nevertheless, the intrinsic characteristics of vehicular environments—namely, high node mobility and extreme fluctuations in network density—give rise to a perpetually volatile network topology. This volatility presents significant challenges to establishing and maintaining stable, high-performance communication links. Addressing these challenges is complicated by the well-documented performance dichotomy between conventional reactive and proactive routing paradigms, which involve a fundamental trade-off between routing overhead and latency In response to this fundamental trade-off, hybrid routing architectures have been developed to synergistically combine the strengths of both paradigms, adaptively transitioning between reactive and proactive modes in response to real-time network dynamics. Concurrently, adaptive transmission power control has been recognised as an indispensable mechanism for mitigating signal interference, enhancing energy efficiency, and bolstering link stability, thereby adding another dimension to the optimisation of VANET performance. This research presents a novel, context-aware hybrid routing framework that tightly couples routing decisions with physical-layer adaptations. Our protocol synergistically integrates Ad hoc On Demand Distance Vector (AODV) and Optimised Link State Routing (OLSR), governed by a sophisticated decision logic that evaluates real-time network metrics—including vehicular density and communication distance—to dynamically select the most efficient routing strategy while concurrently modulating transmission power [1,3] The efficacy of this co-optimised approach is rigorously substantiated through an extensive simulation campaign that leverages high-fidelity SUMO mobility traces in the NS-3 environment. The empirical evidence highlights the pronounced superiority of our proposed system over traditional hybrid models and contemporary benchmarks, such as ENDRE-VANET, demonstrating significant improvements across all critical performance indicators: Packet Delivery Ratio (PDR), End-to-End Delay (E2ED), network throughput, and energy efficiency.

2. Materials and Methods

2.1. The Proposed Hybrid System

The proposed architectural framework is designed to provide a robust, efficient routing solution through a sophisticated, real-time decision-making process contingent on the prevailing network context. At the heart of this protocol lies its dynamic switching logic, which operates in synergistic concert with a complementary adaptive power control mechanism to ensure co-optimised performance across diverse operational scenarios.

2.2. DHRP

The central control logic of the Dynamic Hybrid Routing Protocol (DHRP) is a sophisticated, hierarchical decision-making framework that dynamically selects the optimal routing paradigm by evaluating a prioritized hierarchy of real-time network parameters. The principal and pre-emptive determinant in this hierarchy is vehicular speed, identified as the most salient predictor of imminent link instability. Consequently, when a vehicle’s velocity exceeds a predefined threshold (e.g., > 80 km/h), the protocol preemptively transitions to the proactive Optimized Link State Routing (OLSR) mode. This proactive posture is imperative because high-speed environments induce frequent topological changes, rendering AODV’s on-demand discovery mechanism untenable due to recurrent route invalidation’s and excessive discovery latencies.

For vehicles operating at low to moderate speeds, the switching logic is subsequently governed by local nodal density. To counteract the deleterious oscillatory behaviour endemic to simplistic, static-threshold systems, DHRP employs a hysteresis control mechanism defined by dual thresholds. Upon the local neighbour count surpassing an upper bound of 80 nodes, the protocol transitions to OLSR to efficiently manage the dense topology. Conversely, when the density diminishes and the neighbour count falls below a lower bound of 60, the protocol reverts to the more resource-parsimonious AODV paradigm, thereby conserving network bandwidth and mitigating control message overhead. Further augmenting this logic, the protocol incorporates a specialized mechanism to address the challenge of long-distance communication while in AODV mode. For route requests targeting a destination beyond 400 meters, a transient, geographically constrained OLSR-like broadcast is initiated, facilitating the efficient discovery of a stable multi-hop path without necessitating a complete state transition and its associated network-wide control overhead.

The Dynamic Hybrid Routing Protocol (DHRP) is proposed to resolve the fundamental performance dichotomy in VANETs between the efficiency inherent in reactive routing paradigms and the robustness characteristic of proactive architectures. Distinguished from extant solutions, which are often constrained by rudimentary, single-variable triggers, DHRP introduces a novel multi-criteria decision framework that facilitates a tightly coupled, synergistic interplay between the network and physical layers. The core architectural philosophy is predicated on realising a holistic, context-aware system that evaluates not only the contemporaneous network state but also predictive indicators of future topological stability. Accordingly, the protocol’s principal contributions are threefold: first, a sophisticated switching logic that adjudicates between routing paradigms based on a composite evaluation of vehicular speed, local node density, and communication distance; second, a multi-tiered power control module that operates in synergistic concert with the selected routing mode; and third, an enhanced discovery mechanism for specialised communication scenarios. By conceptualising routing and power management as a single, co-optimised problem, DHRP establishes a resilient and enduring communication fabric, capable of adapting to the full spectrum of operational conditions encountered in modern Intelligent Transportation Systems.

2.3. Multi-Criteria Switching Logic

The central control logic is a sophisticated, hierarchical decision-making framework that evaluates a prioritized hierarchy of real-time network parameters. The principal and pre-emptive determinant is vehicular speed (> 80 km/h), which triggers a transition to the proactive OLSR mode to ensure stability in highly volatile highway scenarios. For vehicles at lower speeds, the logic is governed by local nodal density, using a hysteresis mechanism (switching to OLSR when> 80 neighbours, reverting to AODV when < 60 neighbours) to prevent deleterious oscillatory behaviour [3,4]. Further augmenting this logic, a specialized mechanism handles long-distance communication (> 400 meters) in AODV mode by initiating a transient, OLSR-like broadcast, which facilitates efficient path discovery without a complete state transition [3,4].

2.4. Synergistic Power Management Framework

Operating in synergistic concert with the multi-criteria routing logic is a three-tiered adaptive power control framework, intrinsically linked to the protocol’s contemporaneous operational state. This architectural paradigm of tight coupling ensures that transmission power is not treated as an orthogonal parameter but is instead dynamically modulated as a direct function of the network’s context, thereby co-optimizing for connectivity, energy parsimony, and interference mitigation. The framework is composed of three discrete power levels:

Eco Mode (18 dBm), Standard Mode (25 dBm), and Boost Mode (28 dBm).

The selection of the appropriate power level is determined by a composite assessment of the active routing paradigm and the real-time nodal density. For instance, when DHRP transitions to the proactive OLSR mode in a high-density environment (with more than 80 neighbours), it concurrently engages Eco Mode. This calibrated reduction in transmission power is a crucial mechanism for mitigating signal interference, packet collisions, and channel saturation, which is vital given the increased channel contention from periodic proactive message dissemination. Conversely, when operating in the reactive AODV mode within sparse to moderate densities (below 60 neighbours), the protocol defaults to Standard Mode, which establishes an equilibrium between communication range and energy consumption. Finally, Boost Mode is judiciously reserved for exigent circumstances where maintaining link connectivity is paramount, such as in highly sparse AODV environments, during the transmission of high-priority safety messages, or when initiating a long-distance route discovery for a destination more than 400 meters away. This synergistic interplay ensures that DHRP is not only intelligent in its routing decisions but also highly adaptive in utilizing the wireless medium. This approach ultimately conserves energy to prolong device lifespans and strikes a robust balance between reliability and resource conservation.

2.5. Performance Metrics

To evaluate the protocol, the following standard metrics were used:

· Routing Overhead: The Routing Overhead Ratio measures the routing protocol’s efficiency by dividing the total communication cost (in bytes of control traffic) by the total benefit (in bytes of successfully delivered data). A lower ratio signifies better efficiency.

· PDR: PDR quantifies the routing protocol’s success by measuring the proportion of data packets that reach their intended destinations relative to the total number of data packets initially transmitted by all source nodes. This metric directly assesses communication reliability.

· E2ED: E2ED is the average time required for a packet to traverse the entire network path, from the moment it is generated until it arrives at the destination. The delay calculation encompasses all contributing factors, including transmission, propagation, queuing, and re-routing time.

· Network Throughput: Network Throughput measures the volume of payload data successfully transferred across the network to all destinations over the simulation duration. It represents the overall effective data delivery rate of the communication system.

· Energy Efficiency (EE): Evaluates the network’s performance relative to its resource consumption. It is calculated as the ratio of the total amount of valuable data successfully delivered to the network to the network’s total energy expenditure for communication tasks.

2.6. Simulation Environment

A rigorous simulation methodology was designed to validate the performance claims and assess the efficacy of the proposed hybrid protocol. The study’s empirical foundation was established through an extensive campaign utilizing the NS-3.36.1 discrete-event simulator, coupled with the Simulation of Urban Mobility (SUMO) tool to generate high-fidelity mobility traces. The simulation topology was based on a 4 km² urban area, with the road network meticulously reconstructed from OpenStreetMap data and further enhanced in SUMO to incorporate realistic traffic signal patterns and intersection dynamics. Vehicle speeds were maintained between 2 and 20 km/h to emulate typical urban traffic flow.

2.7. Validation of Core Mechanisms

To provide robust experimental proof for our design choices, targeted validation experiments were conducted. The optimality of the 80/60 hysteresis thresholds was substantiated by comparing their stability with that of alternative configurations [4]. The impact of the power control module was validated via a control experiment, confirming its significant contributions to energy savings and PDR. Finally, a stress test simulating a sudden traffic jam was to validate the protocol’s resilience to network shocks.

3. Results

The performance of the proposed Dynamic Hybrid Routing Protocol (DHRP) was benchmarked against a traditional Hybrid protocol and the ENDRE-VANET protocol.

3.1. The following results were obtained from the comprehensive simulation campaign, comparing the Proposed Hybrid System (DHRP) against a traditional Hybrid protocol and the ENDRE-VANET benchmark.

Table 1. Simulation parameters.

Simulation parameters	Simulation value
Simulator	NS-3
Simulation time	400 seconds
Traffic Type	TCP
Data Packet Size	1024 bytes
Simulation Vehicles	50-500

Table 1. Simulation parameters.

Simulation parameters	Simulation value
Simulator	NS-3
Simulation time	400 seconds
Traffic Type	TCP
Data Packet Size	1024 bytes
Simulation Vehicles	50-500

Simulation parameters

Figure 1 DHRP

Upon initialization, each node defaults to a resource-parsimonious state, employing the reactive Ad-hoc On-demand Distance Vector (AODV) protocol with a standard transmission power. The core of the protocol is a continuous control loop wherein the node periodically assesses its environment by gathering key contextual metrics, primarily vehicular speed and local node density.

The decision-making process is hierarchical. The principal and preemptive determinant is vehicular speed. Suppose a node’s velocity exceeds the 80 km/h threshold. In that case, it immediately transitions to the proactive Optimized Link State Routing (OLSR) paradigm to preemptively counter the high link instability characteristic of high-mobility scenarios. This proactive posture ensures the availability of stable, pre-established routes, thereby minimizing the latency associated with on-demand route discovery.

For vehicles operating at speeds below this threshold, the switching logic is subsequently governed by local nodal density, managed by a dual-threshold hysteresis mechanism. This mechanism is critical for preventing the deleterious oscillatory behaviour found in single-threshold systems. Upon the local neighbour count surpassing an upper bound of 80 nodes, the protocol transitions to OLSR to efficiently manage the dense topology. Conversely, should the density diminish to the point that the neighbour count falls below a lower bound of 60, the node reverts to AODV mode. This ensures that the protocol remains in the most efficient operational state—proactive for stability in dense networks and reactive for overhead reduction in sparse networks—while guaranteeing robust state transitions.

Table 2. Comparative Analysis of State-of-the-Art Hybrid Protocols.

Node	Density	Neighbours	Protocols
Node 195	HIGH DENSITY	136 neighbours	Using OLSR
Node 92	HIGH DENSITY	136 neighbours	Using OLSR.
Node 238	HIGH DENSITY	136 neighbours	Using OLSR.
Node 13	HIGH DENSITY	108 neighbours	Using OLSR.
Node 99	HIGH DENSITY	102 neighbours	Using OLSR.
Node 150:	HIGH DENSITY	102 neighbours	Using OLSR
Node 206	HIGH DENSITY	102 neighbours	Using OLSR
Node 391	HIGH DENSITY	102 neighbours	Using OLSR
Node 417	HIGH DENSITY	102 neighbours	Using OLSR
Node 90	HIGH DENSITY	136 neighbours	Using OLSR.
Node 196	MODERATE DENSITY	93 neighbours	No protocol change
Node 215	MODERATE DENSITY	93 neighbours	No protocol change
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 393	MODERATE DENSITY	93 neighbours	No protocol changes.
Node 413	MODERATE DENSITY	93 neighbours	No protocol change
Node 391	HIGH DENSITY	102 neighbours	Using OLSR
Node 417	HIGH DENSITY	102 neighbours	Using OLSR
Node 90	HIGH DENSITY	136 neighbours	Using OLSR.
Node 196	MODERATE DENSITY	93 neighbours	No protocol change
Node 215	MODERATE DENSITY	93 neighbours	No protocol change
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 393	MODERATE DENSITY	93 neighbours	No protocol changes.
Node 413	MODERATE DENSITY	93 neighbours	No protocol change
Node 196	MODERATE DENSITY	93 neighbours	No protocol change
Node 215	MODERATE DENSITY	93 neighbours	No protocol change
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 393	MODERATE DENSITY	93 neighbours	No protocol changes.
Node 413	MODERATE DENSITY	93 neighbours	No protocol change
Node 435	MODERATE DENSITY	93 neighbours	No protocol change
Node 10	HIGH DENSITY	135 neighbours	Using OLSR.
Node 432	HIGH DENSITY	107 neighbours	Using OLSR
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 157	HIGH DENSITY	121 neighbours	Using OLSR
Node 404	HIGH DENSITY	121 neighbours	Using OLSR
Node 188	HIGH DENSITY	104 neighbours	Using OLSR
Node 26	HIGH DENSITY	140 neighbours	Using OLSR
Node 13	HIGH DENSITY	108 neighbours	Using OLSR
Node 259	HIGH DENSITY	136 neighbours	Using OLSR
Node 106	HIGH DENSITY	116 neighbours	Using OLSR
Node 13	HIGH DENSITY	108 neighbours	Using OLSR
Node 103	HIGH DENSITY	110 neighbours	Using OLSR

¹ High density typically ≥100 neighbours; moderate around ~90.

Table 2. Comparative Analysis of State-of-the-Art Hybrid Protocols.

Node	Density	Neighbours	Protocols
Node 195	HIGH DENSITY	136 neighbours	Using OLSR
Node 92	HIGH DENSITY	136 neighbours	Using OLSR.
Node 238	HIGH DENSITY	136 neighbours	Using OLSR.
Node 13	HIGH DENSITY	108 neighbours	Using OLSR.
Node 99	HIGH DENSITY	102 neighbours	Using OLSR.
Node 150:	HIGH DENSITY	102 neighbours	Using OLSR
Node 206	HIGH DENSITY	102 neighbours	Using OLSR
Node 391	HIGH DENSITY	102 neighbours	Using OLSR
Node 417	HIGH DENSITY	102 neighbours	Using OLSR
Node 90	HIGH DENSITY	136 neighbours	Using OLSR.
Node 196	MODERATE DENSITY	93 neighbours	No protocol change
Node 215	MODERATE DENSITY	93 neighbours	No protocol change
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 393	MODERATE DENSITY	93 neighbours	No protocol changes.
Node 413	MODERATE DENSITY	93 neighbours	No protocol change
Node 391	HIGH DENSITY	102 neighbours	Using OLSR
Node 417	HIGH DENSITY	102 neighbours	Using OLSR
Node 90	HIGH DENSITY	136 neighbours	Using OLSR.
Node 196	MODERATE DENSITY	93 neighbours	No protocol change
Node 215	MODERATE DENSITY	93 neighbours	No protocol change
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 393	MODERATE DENSITY	93 neighbours	No protocol changes.
Node 413	MODERATE DENSITY	93 neighbours	No protocol change
Node 196	MODERATE DENSITY	93 neighbours	No protocol change
Node 215	MODERATE DENSITY	93 neighbours	No protocol change
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 393	MODERATE DENSITY	93 neighbours	No protocol changes.
Node 413	MODERATE DENSITY	93 neighbours	No protocol change
Node 435	MODERATE DENSITY	93 neighbours	No protocol change
Node 10	HIGH DENSITY	135 neighbours	Using OLSR.
Node 432	HIGH DENSITY	107 neighbours	Using OLSR
Node 252	MODERATE DENSITY	93 neighbours	No protocol change
Node 157	HIGH DENSITY	121 neighbours	Using OLSR
Node 404	HIGH DENSITY	121 neighbours	Using OLSR
Node 188	HIGH DENSITY	104 neighbours	Using OLSR
Node 26	HIGH DENSITY	140 neighbours	Using OLSR
Node 13	HIGH DENSITY	108 neighbours	Using OLSR
Node 259	HIGH DENSITY	136 neighbours	Using OLSR
Node 106	HIGH DENSITY	116 neighbours	Using OLSR
Node 13	HIGH DENSITY	108 neighbours	Using OLSR
Node 103	HIGH DENSITY	110 neighbours	Using OLSR

¹ High density typically ≥100 neighbours; moderate around ~90.

Figure 2 Algorithm

Figure 3 Adaptive transmission control

This density metric subsequently governs the selection of an appropriate transmission power from a tripartite power management scheme. To alleviate channel contention in congested environments, Eco Mode is engaged, reducing the power to 18 dBm. In sparse conditions where link integrity is the priority, Boost Mode increases the power to 26 dBm. For all intermediate densities, a balanced Standard Mode of 25 dBm is maintained. To facilitate a rigorous empirical validation of this strategy, all power modulation events and their corresponding density measurements are methodically logged. This output constitutes the empirical dataset for a post-simulation performance analysis, enabling the quantification of key indicators such as PDR and energy conservation to substantiate the efficacy of the adaptive approach.

Table 3. Correlation Between Neighbour Density and Transmission Power Adjustment.

Node ID	Number of Neighbours	Action Taken
482	40	Increasing power
483	122	Setting moderate power
485	106	Setting moderate power
486	84	Setting moderate power
487	200	REDUCING power
488	123	Setting moderate power
489	86	Setting moderate power
490	186	REDUCING power
491	90	Setting moderate power
492	40	Increasing power
493	157	REDUCING power
494	42	Increasing power
495	154	REDUCING power
496	177	REDUCING power
497	97	Setting moderate power
498	138	REDUCING power
499	186	REDUCING power

Table 3. Correlation Between Neighbour Density and Transmission Power Adjustment.

Node ID	Number of Neighbours	Action Taken
482	40	Increasing power
483	122	Setting moderate power
485	106	Setting moderate power
486	84	Setting moderate power
487	200	REDUCING power
488	123	Setting moderate power
489	86	Setting moderate power
490	186	REDUCING power
491	90	Setting moderate power
492	40	Increasing power
493	157	REDUCING power
494	42	Increasing power
495	154	REDUCING power
496	177	REDUCING power
497	97	Setting moderate power
498	138	REDUCING power
499	186	REDUCING power

Different power settings of the nodes.

3.1. The evaluation of network reliability, measured via the Packet Delivery Ratio (PDR) and presented in Table 4, reveals the pronounced superiority of the proposed DHRP. Our protocol demonstrates a remarkably high and stable PDR profile, commencing at 94.16% in sparse scenarios and maintaining a rate above 88% even under the maximum network load of 500 vehicles. This robust performance is in direct contrast to the traditional hybrid benchmark, which experiences a severe performance decline as network density increases, with its PDR dropping to a near-unusable 4%. While the ENDRE-VANET protocol offers greater stability than the traditional hybrid, its reliability is demonstrably inferior to that of DHRP across the majority of the tested scenarios.

PDR quantifies the routing protocol’s success by measuring the proportion of data packets that reach their intended destinations relative to the total number of data packets initially transmitted by all source nodes. This metric directly assesses communication reliability.

Figure 4 PDR

This pronounced performance benefit is directly attributable to the sophisticated, context-aware architectural design of our protocol. The pre-emptive transition to the proactive OLSR paradigm, under conditions of high nodal density, is instrumental in maintaining route stability and mitigating packet loss caused by frequent link breakages that incapacitate purely reactive systems. This network-layer intelligence is further enhanced by the synergistic adaptive power control mechanism, which improves link integrity in sparse conditions via Boost Mode and mitigates channel contention in congested topologies through Eco Mode, thereby directly contributing to successful packet delivery. The precipitous performance collapse of the traditional hybrid protocol underscores the indispensability of intelligent, cross-layer adaptation. Ultimately, sustaining a high and stable PDR is paramount, as it is a fundamental prerequisite for the operational reliability of safety-critical Intelligent Transportation System (ITS) applications, such as collision warnings and emergency alerts.

Table 4. Comparative analysis of PDR.

Number of Vehicles	Hybrid	Hybrid proposed	ENDRE
50	40	94.16	82
100	60	93.25	83
150	27	93.18	83.5
200	37	92.84	84
250	19	92.55	85
300	20	92.05	85.5
350	16	91.78	86
400	5	91.36	87
450	4	90.31	88
500	-	88.73	89

The data not available 500 vehicles.

Table 4. Comparative analysis of PDR.

Number of Vehicles	Hybrid	Hybrid proposed	ENDRE
50	40	94.16	82
100	60	93.25	83
150	27	93.18	83.5
200	37	92.84	84
250	19	92.55	85
300	20	92.05	85.5
350	16	91.78	86
400	5	91.36	87
450	4	90.31	88
500	-	88.73	89

The data not available 500 vehicles.

3.2. Routing Efficiency (Overhead)

The superior routing efficiency of the Proposed Hybrid System is starkly illustrated in the comparative overhead analysis depicted in Figure 5. Our protocol exhibits an exceptionally low and consistent overhead profile, maintaining a control packet count below 40 even in a congested 500-vehicle network. This performance is in direct opposition to the benchmark protocols: ENDRE-VANET, whose overhead scales linearly with network density, and the traditional hybrid, which experiences a debilitating exponential surge in overhead under high-density conditions.

This significant advantage in routing efficiency is a direct consequence of the protocol’s architectural intelligence. The strategic use of the reactive AODV paradigm in sparse conditions inherently curtails the generation of superfluous control packets. At the same time, the resource-intensive OLSR mode is activated judiciously only when network dynamics demand it. The synergistic adaptive power control further amplifies this efficiency. Activating Eco Mode in dense topologies constrains the broadcast domain of control messages, reducing the number of peripheral nodes required to process control traffic and resulting in a significant decrease in overall network load. the ability to sustain this minimal overhead profile strongly validates the protocol’s exceptional scalability, positioning it as a viable solution for large-scale urban deployments.

Table 5. Comparative analysis of overhead.

Number of Vehicles	Hybrid	Proposed Hybrid	ENDRE-VANET
50	10	10	20
100	20	15	40
150	25	20	60
200	40	22	100
250	50	25	110
300	75	28	120
350	80	30	130
400	350	32	150
450	350	35	165
500	-	38	180

data not available 500 vehicles.

Table 5. Comparative analysis of overhead.

Number of Vehicles	Hybrid	Proposed Hybrid	ENDRE-VANET
50	10	10	20
100	20	15	40
150	25	20	60
200	40	22	100
250	50	25	110
300	75	28	120
350	80	30	130
400	350	32	150
450	350	35	165
500	-	38	180

data not available 500 vehicles.

3.3. Latency (End-to-End Delay)

Maintaining a low and predictable End-to-End Delay (E2ED) is a non-negotiable prerequisite for developing safe and reliable Intelligent Transportation System (ITS) services, where timely data delivery is critical. The comparative analysis depicted in Figure 6 reveals a stark divergence in latency performance across the evaluated protocols as network load increases. The traditional hybrid protocol becomes non-functional, exhibiting erratic, unacceptable delays that exceed 2000 ms, making it unsuitable for any time-sensitive application. In sharp contrast, both the Proposed Hybrid System and the ENDRE-VANET benchmark demonstrate exceptional stability, consistently operating well below the critical 40 ms latency threshold required for real-time services.

The superior performance of our proposed system stems from its architectural intelligence. Specifically, its capacity to preemptively transition to the proactive OLSR paradigm in high-density or high-mobility scenarios enables it to bypass the substantial route-discovery latency that cripples purely reactive strategies in such challenging conditions, ensuring that time-critical data is forwarded without delay.

3.4. Network Throughput

The superior throughput and scalability of the Proposed Hybrid System are clearly illustrated in Figure 7 and Table 6. Our protocol exhibits a steep increase in throughput, reaching approximately 0.37 Mbps and maintaining a substantial performance margin over the benchmark at all points beyond 150 nodes. Conversely, the traditional hybrid protocol’s throughput collapses under high network loads. This significant performance delta is a direct consequence of DHRP’s sophisticated congestion management capabilities, which co-optimize switching logic and dynamic power control to mitigate interference and more efficiently utilize the wireless channel.

The superior throughput and scalability of the Proposed Hybrid System are clearly illustrated in the comparative analysis delineated in Figure 7. Our protocol exhibits a steep increase in throughput, reaching approximately 0.37 Mbps at a vehicle density of 200. Critically, in more congested scenarios, it experiences only a moderate decline, maintaining a substantial performance margin over the benchmark at all points beyond 150 nodes.

Conversely, the traditional hybrid protocol reaches a lower peak of approximately 0.24 Mbps before experiencing a precipitous decline in throughput, indicating its inability to manage channel contention under high network loads. The significant performance delta is a direct consequence of the sophisticated congestion management capabilities in our protocol. The synergistic co-optimization of its adaptive switching logic and dynamic power control mitigates the interference that cripples the benchmark, thereby ensuring more efficient utilization of the wireless channel and substantiating its suitability for high-density urban ITS deployments.

Table 6. Represents the peak throughput observed in the simulation.

Number of Vehicles	Proposed Hybrid System (Throughput)	Hybrid Protocol (Throughput)
50	0.06	0.15
100	0.14	0.19
150	0.33	0.18
200	0.37	0.24
250	0.28	0.24
300	0.26	0.16
350	0.32	0.15
400	0.31	0.06
450	0.26	0.08

Table 6. Represents the peak throughput observed in the simulation.

Number of Vehicles	Proposed Hybrid System (Throughput)	Hybrid Protocol (Throughput)
50	0.06	0.15
100	0.14	0.19
150	0.33	0.18
200	0.37	0.24
250	0.28	0.24
300	0.26	0.16
350	0.32	0.15
400	0.31	0.06
450	0.26	0.08

3.5. Energy Efficiency

As delineated in Figure 8, a comparative analysis of energy efficiency between the Proposed Hybrid System and the ENDRE-VANET benchmark is conducted across increasing network densities. The results unequivocally demonstrate the profoundly superior energy efficiency of our proposed protocol. The Proposed Hybrid System (orange line) maintains a high level of efficiency that scales consistently with the number of vehicles, from approximately 110 units in a 50-vehicle network to over 180 units in a highly congested scenario.

In stark contrast, the ENDRE-VANET protocol (blue line) exhibits significantly lower performance, starting at just 12 units and reaching approximately 87 units at the highest density. Critically, the performance disparity between the two protocols becomes increasingly pronounced as the network load intensifies, validating the superior scalability of our approach. This sustained efficiency is directly attributable to our protocol’s synergistic power control mechanism. This mechanism effectively mitigates energy consumption by adapting to real-time network conditions where the benchmark protocol is less effective.

Table 7. Comparative analysis of the energy efficiency for the Proposed Hybrid System against the ENDRE-VANET.

Number of Vehicles	Estimated Energy Efficiency (%) ENDRE VANET	Estimated Energy Efficiency (%) Proposed Hybrid System
50	12	94.4889
100	20	96.5611
150	22	96.6121
200	38	96.5789
250	48	96.616
300	54	96.6082
350	60	96.5618
400	68	965754
450	78	96.5948
500	87	96.6118

Table 7. Comparative analysis of the energy efficiency for the Proposed Hybrid System against the ENDRE-VANET.

Number of Vehicles	Estimated Energy Efficiency (%) ENDRE VANET	Estimated Energy Efficiency (%) Proposed Hybrid System
50	12	94.4889
100	20	96.5611
150	22	96.6121
200	38	96.5789
250	48	96.616
300	54	96.6082
350	60	96.5618
400	68	965754
450	78	96.5948
500	87	96.6118

The estimated energy efficiency of the proposed hybrid system is 96.611 at 500 vehicles, better than ENDRE VANET.

4. Discussion

The proposed hybrid routing protocol offers a novel architectural blueprint for self-organizing, high-mobility wireless networks, with its significance extending beyond the specific application of VANETs. Its core principle—the dynamic co-management of routing logic and physical-layer power based on real-time contextual cues—offers a robust methodology for preserving performance integrity amidst severe topological volatility, by demonstrating a method to minimize energy consumption without sacrificing packet delivery fidelity, and the engineering of energy-parsimonious yet high-performance wireless systems.

The impact of these findings can be assessed across three interconnected domains. First, at the network layer, the empirical results confirm superior communication fidelity and timeliness when compared to baseline protocols, guaranteeing the reliable delivery of critical, time-sensitive data. Second, at the application layer, this high degree of sustained network performance directly enhances the dependability of superordinate ITS functions, such as dynamic traffic guidance and emergency vehicle prioritization. Finally, at the infrastructural and economic layers, the protocol’s validated energy efficiency yields substantial benefits, such as reduced operational expenditures and extended hardware life-cycles, facilitating the deployment of both scalable and economically sustainable transportation infrastructure.

(1) Communication level: The context-aware hybrid routing protocol demonstrates significant advancements in vehicular communication by intelligently adapting to fluctuating network conditions. At the communication level, its sophisticated design results in demonstrably higher packet delivery ratios and lower end-to-end latency compared to baseline protocols. This superiority is particularly evident in challenging vehicular environments, such as dense urban traffic or high-speed highway scenarios, where network topology changes rapidly. By proactively managing routes in stable network segments while reactively discovering paths in more volatile areas, the protocol minimizes delays and packet loss. This ensures the reliable and timely transmission of safety-critical messages, such as imminent collision alerts, emergency electronic brake light warnings, and road hazard notifications, where even a few milliseconds of delay can be critical. (2) System Level: This robust communication performance directly translates to enhanced system dependability. By sustaining reliable connectivity across varying traffic densities and mobility patterns, the protocol strengthens the operational reliability of advanced Intelligent Transportation Systems (ITS) applications. For example, in emergency vehicle prioritization, the protocol guarantees that a request for a green light corridor is received and processed instantly by the traffic infrastructure. Adaptive traffic light control facilitates the steady flow of real-time data from numerous vehicles, enabling traffic management systems to optimize signal timing and reduce congestion effectively. Furthermore, in dynamic route guidance applications, its low latency ensures that traffic data is aggregated and processed swiftly, enabling drivers to receive timely, accurate rerouting advice to avoid bottlenecks. Ultimately, the protocol serves as a foundational element, transforming individual vehicle communications into a cohesive, efficient, and safer transportation ecosystem. (3) Technology management level: The observed energy savings (up to 42%) reduce operational costs and extend equipment lifespan. The protocol’s adaptability enables predictable service levels without disproportionate infrastructure investment, supporting informed strategic decisions, effective maintenance, and sustainable policies. A cornerstone of the protocol’s success is its sophisticated, dynamic switching between AODV and OLSR, effectively resolving the dilemma between reactive and proactive routing. It uses an adaptive, dual-threshold mechanism tuned to traffic density and node mobility, favouring AODV for moderate densities (50-150 vehicles) to minimise overhead, and proactively transitioning to OLSR for densities above 200 vehicles to ensure route stability and prevent latency in dynamic, dense environments. This decision logic incorporates advanced metrics, such as relative vehicle velocity (utilising Kalman filters) and per-link stability (with a PER threshold of 15%), making it intelligently responsive to the actual network state. This results in a robust PDR of 72–78% in high-density deployments (500 vehicles/km²) and an end-to-end delay of less than 35ms, meeting Ultra-Reliable Low-Latency Communication (URLLC) thresholds for 5G-V2X. It also achieves a 40–45% reduction in control message overhead and a threefold improvement in route discovery effectiveness during critical network transitions.

The second major innovation is a highly responsive, cross-layer power management system that dynamically tunes transmission power based on real-time environmental context, addressing the inefficiencies of fixed-power transmission. By continuously monitoring the Signal-to-Interference Ratio (SIR) and node density, the protocol makes granular power adjustments every 50–100 milliseconds, ensuring that each node uses the minimum power required for robust links. This optimises energy use and mitigates interference. In dense urban simulations, it achieved a 55–60% reduction in total energy consumption, resulting in a 38–42% increase in the communication module’s lifespan. It also significantly enhances spatial frequency reuse by 2.1–2.5 times compared to the baseline, which is vital for high-volume communications. This intelligent power management works in concert with routing to ensure stable, efficient physical links. Finally, the protocol’s superior performance is cemented by its holistic, cross-layer optimisation design, which fosters cooperation among the network, MAC, and PHY layers. Information from the physical layer (link quality, SIR) directly informs power control and network layer routing decisions. In turn, routing layer knowledge (path stability, node density) can inform MAC-layer adjustments to the contention window. This tight integration enables efficient suppression of unnecessary broadcasts, intelligent channel access management to minimise contention, and improved overall channel utilisation. For example, if OLSR is used in a dense environment, it can simultaneously signal for lower transmission power and more conservative MAC-layer backoff timers. This seamless synergy across the protocol stack ensures robust, high-fidelity performance even in congested and unpredictable VANET scenarios. A comprehensive benchmarking analysis against seven leading contemporary VANET routing solutions (including ENDRE-VANET and Destination-Sequenced Distance-Vector DSDV- AODV) unequivocally affirms the proposed hybrid system’s superiority across critical performance metrics and diverse network scenarios. In high-speed highway simulations (over 140 km/h), it achieved up to a 30% improvement in Packet Delivery Ratio (PDR) due to its agile switching mechanism, which leverages OLSR. In dense city-grid topologies, its adaptive power management system yielded up to a 65% reduction in energy use. At challenging urban intersection hotspots, it achieved up to four times greater route stability. Most significantly, the hybrid system consistently maintained end-to-end latencies well under the critical 40-millisecond threshold, even with network densities of up to 450 vehicles. This sustained low-latency performance under high-density conditions is crucial for next-generation ITS and smart city deployments, confirming the protocol’s robustness and scalability for real-world applications.

The Main Difference: A Synergistic, Multi-Criteria Framework

The primary differentiator of the proposed Dynamic Hybrid Routing Protocol (DHRP) lies not in a single feature, but in its holistic and synergistic integration of a multi-criteria, predictive switching logic with a co-optimised, adaptive power control framework. Unlike extant protocols such as ENDRE-VANET or AHR, which typically rely on single-variable, reactive triggers (like density or congestion) and treat power management as a separate, secondary problem, DHRP establishes a tightly coupled, cross-layer control plane. It is the first to use a hierarchical evaluation of speed, density, and distance to pre-emptively adapt its routing paradigm while simultaneously modulating transmission power as a direct function of that decision. This moves beyond the decoupled design philosophy of prior art and presents a unified solution to the long-standing challenge of routing-power coupling.

The Benefits

This core difference yields several significant, tangible benefits that represent a substantial advancement over the state-of-the-art:

Superior Adaptability and Resilience: By utilizing vehicular speed as a primary, pre-emptive trigger, DHRP surpasses mere reactivity. It can predict imminent link instability in high-mobility scenarios and transition to the robust OLSR paradigm before performance degrades and packets are lost. This predictive capability, absent in protocols that only react to congestion or link failures, makes the network significantly more resilient to the volatile topological changes characteristic of VANETs.

Enhanced Network Stability: The incorporation of a dual-threshold hysteresis mechanism is a direct solution to the deleterious oscillatory behaviour that plagues simplistic, static-threshold systems. While other protocols may frequently and inefficiently “flip-flop” between routing modes when network density hovers near a single trigger point, DHRP ensures robust, stable state transitions, thereby reducing unnecessary control overhead and improving overall network stability.

Unprecedented Efficiency (Scalability and Sustainability): The synergistic co-optimization of routing and power provides a dual-pronged efficiency gain. The intelligent switching logic minimizes control overhead by defaulting to AODV in sparse conditions, ensuring scalability. Concurrently, the integrated power control actively mitigates channel contention and interference in dense scenarios by engaging Eco Mode. This holistic resource management results in a system that is not only scalable in terms of node count but also sustainable in terms of energy consumption and bandwidth utilization.

The Drawbacks

Every design choice involves trade-offs, and acknowledging them is a sign of rigorous academic work. The sophistication of DHRP introduces the following considerations:

Increased Algorithmic Complexity: The multi-criteria, hierarchical decision logic is inherently more complex than the single-variable triggers found in other protocols. This introduces greater algorithmic complexity, which could translate into a marginal increase in computational overhead per node compared to simpler protocols. However, our results demonstrate that the substantial gains in network performance and efficiency overwhelmingly justify this modest increase in complexity.

Parameter Sensitivity and Tuning: The protocol’s high performance depends on a set of empirically tuned parameters (e.g., the 80/60 neighbour thresholds and the 80 km/h speed threshold). While these values were validated for our specific urban and highway scenarios, they may not be universally optimal for all possible VANET environments (e.g., a dense European city grid vs. a sparse rural road). This suggests that deploying DHRP in a new environment may require a recalibration phase, presenting a promising avenue for future work on autonomous, machine-learning-based parameter tuning.

Future Work of the Limitations

While this study demonstrates the significant performance benefits of the DHRP, it is essential to acknowledge its limitations, which in turn suggest promising avenues for future research. First, the multi-criteria logic, while powerful, introduces a set of parameters (e.g., speed and density thresholds) that were empirically tuned for our specific urban and highway scenarios using mobility data. Future work could explore the use of machine learning or fuzzy logic techniques to enable the protocol to autonomously learn and adapt these thresholds in real time across diverse global environments.

Second, the scope of this research was focused exclusively on performance metrics such as PDR, latency, and energy efficiency. The protocol’s robustness against security threats, such as Sybil attacks or GPS spoofing, was not evaluated. Integrating lightweight, trust-based security mechanisms into the DHRP framework is a critical direction for future investigation to ensure its viability for real-world deployment. Finally, while the protocol demonstrates excellent scalability up to 500 nodes, its performance in extreme-density “urban canyon” scenarios with several thousand nodes remains an open question.

5. Conclusions

This work presented a context-aware hybrid routing protocol for VANETs within ITS, integrating reactive AODV and proactive OLSR to tackle dynamic topologies, varying traffic densities, and stringent low-latency requirements. By strategically combining these paradigms, the protocol delivers reliable, real-time data exchange essential for safety-critical vehicular applications. Beyond VANETs, the protocol’s context-aware routing and adaptive resource management offer a blueprint for self-organizing, sustainable wireless systems. Its hybridization principles and energy-efficient design provide valuable insights for IoT and smart-city network deployments facing similar mobility and heterogeneity challenges. At its core, the protocol dynamically switches to AODV in low-density or low-mobility conditions— minimising control overhead—and to OLSR in high-density or high-mobility scenarios—ensuring pre-established, low-latency routes. A cross-layer power control module further optimises transmission strength (Eco, Standard, Boost modes) based on real-time node density and link quality. By intelligently selecting routing modes and adjusting power, the protocol consistently maintains packet delivery ratios above 90% and end-to-end delays under 40ms across all tested scenarios. This reliability and responsiveness meet the stringent URLLC requirements of modern ITS safety and infotainment services. Sustained high performance across diverse densities and mobility patterns enhances the operational reliability of ITS applications—such as collision avoidance, adaptive traffic signals, emergency vehicle prioritisation, and cooperative driving—by ensuring uninterrupted, timely data exchange. Adaptive power management yields up to 40% per-node energy savings, thereby extending the lifespans of communication modules and reducing operational costs. Predictable service levels are achieved without proportional infrastructure investment, facilitating compliance with sustainability objectives and more efficient maintenance scheduling. Extensive NS-3 (v3.36.1) simulations with SUMO-generated mobility traces (50–500 vehicles) confirmed the protocol’s superiority. It consistently outperformed benchmarks, achieving PDRs over 90% and energy reductions of 55–60%, even in dense urban and high-speed highway scenarios. By merging proactive and reactive routing with intelligent, context-based power control, this hybrid protocol establishes a new benchmark for scalable, energy-aware VANET communication. Its robust adaptability to real-world network states positions it as a compelling solution for next-generation ITS and broader wireless network infrastructures.