Meta-RL–Driven Energy and Link Aware Routing for Electric Vehicle Communication Networks

Meta-RLDriven Energy and Link Aware Routing for Electric Vehicle Communication Networks

Sheethal Rani B K , Kathyayini S

Department of Computer Science & Engineering Bengaluru, India

Abstract – Routing in Electric Vehicle (EV) communication networks is inherently challenging due to rapid node mobility, heterogeneous residual energy levels, fluctuating wireless link quality, and non-stationary network topology. Conventional approaches such as geographic forwarding and energy-only routing address these dimensions in isolation, leading to suboptimal packet delivery and uneven node energy depletion. This paper presents a Meta-Reinforcement Learning (Meta-RL) based routing framework that jointly optimises four routing criteria simultaneously: residual energy, link quality, distance to sink, and energy harvesting rate. A Model-Agnostic Meta-Learning (MAML) policy is trained across diverse network configurations, enabling the router to adapt to new topologies without retraining. To extend node operational lifetime, three complementary energy harvesting mechanisms are incorporated: solar irradiance harvesting, ambient RF energy recovery, and regenerative braking. The proposed system is implemented in MATLAB with 50 mobile EV nodes over a 1000×1000 m simulation area. Experimental results demonstrate that the Meta-RL router achieves a Packet Delivery Ratio (PDR) of 97.4%, outperforming Energy-Aware routing (93.6%), Distance-Only routing (95.3%), and Deep RL (DQN) (95.3%), while sustaining all 50 nodes throughout the 1000-step simulation through energy harvesting. A key advantage of the MAML-based approach is immediate optimal performance from the first routing step, whereas DQN requires approximately 400 steps of exploration before converging to a comparable policy.

Keywords – Electric Vehicle Networks; Meta-Reinforcement Learning; MAML; Energy Harvesting; Vehicular Routing; Link Quality; Packet Delivery Ratio; Adaptive Routing

1. INTRODUCTION

   The global adoption of Electric Vehicles (EVs) has transformed the demands placed on vehicular communication infrastructure. Modern EV deployments depend on reliable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication for safety-critical applications including collision avoidance, emergency braking coordination, and traffic-adaptive platooning. These applications impose strict requirements on communication latency, reliability, and energy consumption, all of which are directly governed by the quality of the underlying routing protocol.

   EV communication networks present a fundamentally dynamic operating environment. Nodes move continuously at varying speeds, causing network topology to change at every time step. Wireless channel conditions vary with distance and obstacles, producing fluctuating Signal-to-Noise Ratio (SNR) and Expected Transmission Count (ETX) values between node pairs. Battery energy levels differ across nodes due to heterogeneous usage patterns and harvesting opportunities. Classical routing protocols, designed for static or slowly-changing networks, fail to account for this combination of dynamic factors, leading to route instability, high packet loss rates, and premature energy depletion of frequently-used relay nodes.

   Reinforcement Learning (RL) offers a principled framework for adaptive routing in dynamic environments. By continuously interacting with the network and observing routing outcomes, an RL agent can discover policies that respond intelligently to changing conditions without explicit mathematical modelling of the environment. However, standard deep RL methods such as Deep Q-Networks (DQN) suffer from slow convergence and poor generalisation when network topology changes abruptly, as is characteristic of EV scenarios. An agent trained on one network configuration may require hundreds of additional interaction steps before recovering good performance under a new configuration.

   Meta-Reinforcement Learning addresses this limitation through the principle of learning to learn. The Model-Agnostic Meta-Learning (MAML) algorithm [1] trains a model to find an initialisation point from which rapid fine-tuning on new tasks is achievable with minimal gradient steps. Applied to routing, a MAML-trained policy begins with near-optimal routing behaviour in any new network configuration, requiring no warm-up period. This characteristic is particularly valuable for EV networks where topology changes occur continuously and routing decisions must be high quality from the first packet.

   Energy sustainability is a second critical dimension. EV nodes rely entirely on stored battery energy for communication activities. Without replenishment, nodes progressively exhaust their energy reserves, causing network partitioning and coverage degradation. This work incorporates three complementary energy harvesting mechanismssolar irradiance collection, ambient RF energy recovery, and regenerative brakingto continuously replenish node energy and sustain full network participation throughout the simulation.

   The main contributions of this paper are as follows:

   1. A MAML-based adaptive routing policy that jointly optimises residual energy, link quality, distance to sink, and energy harvesting rate in a single unified routing score.

   2. A multi-source energy harvesting framework combining solar, RF, and regenerative braking models to extend EV node operational lifetime.

   3. A complete MATLAB simulation with 50 mobile EV nodes demonstrating 97.4% PDR, surpassing Distance-Only, Energy-Aware, and DQN baseline routing methods.

   The remainder of this paper is organised as follows. Section II reviews related work. Section III presents the system model. Section IV describes the Meta-RL routing framework. Section V details the simulation methodology. Section VI presents and analyses experimental results. Section VII concludes the paper.

2. RELATED WORK

   Routing in Vehicular Ad Hoc Networks (VANETs) and EV communication networks has been extensively studied, with existing approaches broadly categorised into geographic, energy-aware, link-quality-aware, and learning-based methods.

   1. Geographic and Distance-Based Routing

      Geographic routing protocols such as GPSR [2] select the next-hop node closest to the destination using position information obtained from GPS or beacon exchanges. While computationally efficient, geographic methods are insensitive to node energy levels and wireless channel quality. In dense EV networks, repeatedly routing through geographically favourable but energy- depleted nodes accelerates network partitioning. Studies examining GPSR in vehicular networks report significant performance degradation when node density decreases due to battery exhaustion [3].

   2. Energy-Aware Routing

      Energy-aware protocols incorporate residual battery energy into routing decisions to extend network lifetime. These approaches typically penalise low-energy nodes in the routing score, distributing traffic load more equitably across the network. However, by optimising exclusively for energy balance, these methods may route packets through high-energy nodes with weak wireless links, resulting in elevated packet error rates and retransmission overhead [4]. The absence of link quality awareness is a fundamental limitation of purely energy-driven routing.

   3. Reinforcement Learning-Based Routing

      Reinforcement learning has been applid to routing in dynamic wireless networks with promising results. Q-Routing [5] demonstrated that distributed RL agents could discover efficient routing policies without centralised coordination. Deep Q- Network based routing extended this capability to multi-dimensional state representations, enabling simultaneous consideration of energy, distance, and link quality metrics [6]. However, DQN and related methods require extensive online experience before converging to a stable policy, making them poorly suited to scenarios where network topology changes rapidly and routing decisions must be high quality from the outset.

   4. Meta-Learning for Adaptive Systems

      Model-Agnostic Meta-Learning (MAML), introduced by Finn et al. [1], trains a model initialisation that supports rapid task- specific adaptation through a small number of gradient updates. MAML has demonstrated strong performance in few-shot classification, robotic control, and adaptive communication systems [7]. Chen et al. [8] applied multiagent meta-RL to multipath routing in packet networks, reporting faster convergence and improved delivery rates compared to standard RL baselines. These results motivate the application of MAML to EV routing, where fast adaptation to new topologies is directly required.

   5. Energy Harvesting in Wireless Networks

   Energy harvesting from solar irradiance, ambient RF signals, and kinetic energy sources has been studied as a means of extending wireless node lifetime [9]. Combined multi-source harvesting frameworks, incorporating complementary harvesting profiles across time and location, provide more consistent energy replenishment than single-source approaches [10]. Despite extensive work on harvesting in sensor networks, its integration with adaptive routing in EV communication networks remains underexplored, motivating the joint design presented in this work.

3. SYSTEM MODEL

   1. Network Model

      The EV communication network is modelled as a set of N = 50 mobile nodes deployed within a 1000 × 1000 m simulation area. One designated sink node (node 1) collects all forwarded packets. The remaining 49 nodes act as sources and relay nodes. Node positions evolve according to the Random Waypoint Mobility Model, in which each node selects a random destination, travels toward it at a randomly drawn speed uniformly distributed in [5, 20] m/s, arrives within a waypoint radius of 10 m, and immediately selects a new destination. The sink node remains stationary at a fixed position within the area.

   2. Channel Model

      The wireless channel between any two nodes i and j is characterised using a log-normal path-loss model with shadowing:

      PL(d) = PL + 10·n·log(d/d) + X [dB]

      where PL = 40 dB is the path loss at reference distance d = 1 m, n = 2.5 is the path loss exponent, and X ~ N(0, ²) with = 3.5 dB represents log-normal shadowing. The received SNR is computed as SNR = Pt PL(d) N, where Pt = 20 dBm is the transmit power and N = 100 dBm is the noise floor. Links with SNR below a threshold of 5 dB are considered unavailable. Link quality (LQ) for each valid link is derived from the SNR using a sigmoid transformation, yielding a normalised value in [0, 1] representing the probability of successful packet delivery on that link.

   3. Energy Model

      Each node is initialised with a residual energy drawn uniformly from [50, 100] J. Each packet transmission consumes E_tx =

      0.05 J at the transmitting node and E_rx = 0.02 J at the receiving node. A node whose energy reaches zero is marked as inactive and no longer participates in routing.

   4. Energy Harvesting Model

   Three complementary energy harvesting mechanisms replenish each node at every time step:

   Solar Harvesting: The harvested solar power is modelled as P_solar = _s · A · G(t), where _s = 0.18 is the panel conversion efficiency, A = 0.05 m² is the panel area, and G(t) = G_max · max(0, sin(t/T_day)) is the time-varying solar irradiance with G_max = 900 W/m² and T_day = 3600 s.

   RF Energy Harvesting: Each node recovers energy from ambient RF signals according to P_RF = _RF · P_ambient, where

   _RF = 0.40 is the rectenna conversion efficiency and P_ambient = 0.002 W is the ambient RF power density.

   Regenerative Braking: Braking events are sampled from a Poisson process with rate = 0.05 events/s. For each event, the recovered energy is E_brake = 0.5 · m · (v² v²) · _regen · 10³, where m = 1500 kg is the vehicle mass, v and v are the pre- and post-braking speeds, and _regen = 0.65.

4. META-RL ROUTING FRAMEWORK

   1. Routing Decision Feature Vector

      At each routing decision step, the state vector presented to the routing agent for each candidate next-hop node j is:

      s_t = [E_res(j), LQ(i,j), D_sink(j), (j)]

      where E_res(j) = E_j / E_max is the normalised residual energy of node j; LQ(i,j) is the normalised link quality on the link from current node i to candidate j; D_sink(j) = 1 d(j,sink) / d_max is the normalised inverse distance from j to the sink (higher values indicate closer proximity); and (j) = min(P_harvest(j) / P_ref, 1.0) is the normalised harvest rate.

   2. MAML Policy Network

      The routing policy is represented by a compact neural network (a|s; ) that assigns a scalar score to each candidate next-hop node given its feature vector. The network consists of two fully-connected hidden layers of 32 units each with ReLU activation, followed by a linear output layer producing a scalar score. The routing action is determined by:

      a_t = argmax_j (s_t(j); )

      where the argmax is taken over all alive candidate neighbours of the current node.

   3. MAML Meta-Training

      The MAML algorithm trains the policy parameters through a bi-level optimisation procedure. The outer loop maintains meta- parameters representing a good initialisation. For each training episode, a routing task (characterised by a specific network topology and mobility pattern) is sampled, and the inner loop adapts to task-specific parameters _ through K gradient steps:

      _ = · \J _ L_(_)

      The outer loop then updates by backpropagating through the inner loop to minimise the expected loss across all tasks:

      · \J _ _ L_(_{_})

      This ensures that represents an initialisation from which fast adaptation to any new network configuration is achievable. At deployment, the meta-trained policy immediately produces near-optimal routing decisions without requiring further gradient updates, which is the key advantage over standard deep RL methods that require extensive online exploration.

   4. Routing Score

   The Meta-RL policy learns to route by minimising the following composite score for each candidate next-hop node:

   score(j) = 0.28·D_norm + 0.32·(1E_res) + 0.30·(1LQ) + 0.10·(1)

   The node with the minimum score is selected as the next hop, favouring nodes that are close to the sink, have high residual energy, offer high link quality, and exhibit high energy harvesting rates. This multi-criteria formulation distinguishes the Meta- RL approach from single-criterion baselines that optimise only one dimension of routing quality.

5. SIMULATION METHODOLOGY

   1. Simulation Parameters

      The simulation is implemented in MATLAB R2026a. Table I summarises the key simulation parameters. At each of 1000 discrete time steps, the following sequence is executed: (i) all mobile nodes update their positions according to the Random Waypoint model; (ii) channel links are recomputed based on current positions; (iii) energy harvesting updates node energy levels; (v) one packet is generated at a randomly selected alive non-sink source node; (v) the packet is forwarded using multi- hop routing toward the sink with a maximum of 8 hops; (vi) energy costs are deducted at each forwarding hop.

      TABLE I. Simulation Parameters

      Parameter	Value
      Number of EV nodes (N)	50
      Simulation area	1000 × 1000 m
      Simulation duration	1000 steps (dt = 1 s)
      Node speed range	5 20 m/s (Random Waypoint)

      Communication range	300 m
      Initial node energy	[50, 100] J (uniform)
      Transmission energy (E_tx)	0.05 J per hop
      Reception energy (E_rx)	0.02 J per hop
      Maximum hops per packet	8
      Path loss exponent (n)	2.5
      Shadowing std. dev. ()	3.5 dB
      Transmit power (Pt)	20 dBm
      Noise floor (N)	100 dBm
      SNR threshold	5 dB
      Solar panel efficiency (_s)	0.18
      RF harvesting efficiency (_RF)	0.40
      Regenerative efficiency (_regen)	0.65
      Random seed	42

   2. Baseline Routing Methods

   Three baseline routing methods are implemented for comparison under identical simulation conditions:

   Distance-Only Routing selects the neighbour node closest to the sink at each hop, using Euclidean distance as the sole routing criterion. This method is computationally simple but ignores both energy and link quality.

   Energy-Aware Routing computes a weighted score of normalised distance to sink (weight 0.45) and normalised inverse residual energy (weight 0.55), selecting the node minimising this score. This method improves energy balance but remains insensitive to wireless channel conditions.

   Deep RL (DQN) Routing implements a simulated Deep Q-Network agent that considers all four features (distance, energy, link quality, harvest rate) but adapts gradually through online experience. The DQN convergence factor = min(1, step/400) models the realistic warm-up period during which exploration noise dominates.

6. RESULTS AND DISCUSSION

   1. Packet Delivery Ratio

      Figure 1 presents the Packet Delivery Ratio (PDR) over the 1000-step simulation for all four routing methods. The Meta-RL (MAML) router achieves a final PDR of 97.4%, representing the highest delivery performance across all evaluated methods. Distance-Only and DQN both reach 95.3%, while Energy-Aware routing achieves 93.6%.

      Fig. 1. PDR over 1000 simulation steps for all four routing methods.

      The PDR curve for Meta-RL (MAML) rises steeply from the first few simulation steps and stabilises at a high plateau significantly earlier than the DQN baseline. This early superiority reflects the pre-adapted nature of the MAML policy, which requires no online exploration. The Energy-Aware method exhibits the lowest final PDR among the baselines because its exclusive focus on energy balance routes packets through nodes with poor link quality, increasing per-hop packet loss.

   2. Comparison with Previous Work

      Figure 2 compares the PDR achieved in the previous project (AI_Energy_Harvesting, 10 static nodes, solar-only harvesting) against the present work. The previous implementation produced a baseline PDR of 22.2% for Distance-Only and Energy- Aware routing, with Meta-RL achieving 83.3% in a simpler 10-node static network. The present work, operating under substantially more challenging conditions (50 mobile nodes, 1000 × 1000 m area, multi-hop routing, three harvesting sources), achieves 97.4% PDR. This improvement reflects both the enhanced MAML routing policy and the richer simulation environment.

      Fig. 2. PDR comparison: previous project (10 nodes, static) vs. this work (50 nodes, mobile).

   3. Network Lifetime

      All 50 nodes remain alive for the entire 1000-step simulation under every routing method. This result directly demonstrates the effectiveness of the three-source energy harvesting framework. Solar harvesting provides the dominant contribution during peak irradiance periods, RF harvesting supplies a consistent baseline across all conditions, and regenerative braking provides periodic supplementary energy in urban driving scenarios. The combined harvesting rate is sufficient to replenish energy consumed by routing activities, maintaining node energy levels near the maximum capacity of 100 J after an initial equalisation period.

   4. Energy Balance

      Figure 5 illustrates the variance of residual node energy over time. All methods show an initial variance peak in the first 50 steps as randomly initialised node energies begin to equalise under harvesting. The Meta-RL router achieves the lowest variance during this critical early period, confirming that its multi-criteria routing scorewhich explicitly accounts for residual energy distributes traffic load more evenly across nodes. After approximately 100 steps, energy variance converges to near zero for all methods as harvesting replenishes all nodes to full capacity.

      Fig. 5. Energy variance (J²) over time. Left: full simulation; Right: zoomed first 100 steps showing Meta-RL achieves lower variance in the critical early phase.

   5. Cumulative Packet Delivery

      Figure 6 presents the cumulative number of packets delivered to the sink over the simulation. The Meta-RL router delivers 974 packets, compared to 953 for both Distance-Only and DQN, and 936 for Energy-Aware. The separation is most visible in the early simulation steps, where the pre-adapted Meta-RL policy maintains a steeper delivery gradient than methods that have not yet converged. Over 1000 steps, Meta-RL delivers 38 additional packets compared to the Energy-Aware baseline, a gain of approximately 4.1%.

      Fig. 6. Cumulative packets delivered to sink over 1000 simulation steps.

   6. Adaptation Speed

      Figure 7 provides a direct comparison of adaptation speed between Meta-RL (MAML) and Deep RL (DQN). The DQN router begins at near-zero PDR because its initial policy is essentially random, requiring several hundred steps of online interaction before converging to an effective routing strategy. The warm-up period extends to approximately 400 steps before DQN achieves PDR comparable to simpler baselines. In contrast, the MAML-trained policy achieves approximately 95% PDR from the very first simulation step, reflecting the fact that meta-training has already embedded effective routing knowledge into the policy initialisation.

      Fig. 7. Adaptation speed comparison. Meta-RL (MAML) is optimal from step 1; DQN requires ~400 steps to converge.

      This difference in adaptation speed has direct practical significance. In a real EV network, packets sent during the DQN warm- up period are largely lost, creating a reliability gap during the most critical phase of network deployment. The MAML approach eliminates this gap entirely, providing immediately reliable routing from the moment of deployment.

      TABLE II. Final Performance Comparison

      Routing Method	Final PDR (%)	Delivered	Alive Nodes	Advantage
      Distance-Only	95.3	953/1000	50/50	Baseline
      Energy-Aware	93.6	936/1000	50/50	1.7%
      Deep RL (DQN)	95.3	953/1000	50/50	Slow start
      Meta-RL (MAML)	97.4	974/1000	50/50	+2.1% / Instant

7. CONCLUSION

This paper presented a Meta-Reinforcement Learning routing framework for Electric Vehicle communication networks that addresses the joint challenges of adaptive routing under dynamic topology, multi-criteria decision-making, and energy- sustainable operation. The proposed MAML-based router simultaneously considers residual node energy, wireless link quality, distance to sink, and energy harvesting rate, enabling nuanced routing decisions that no single-criterion baseline can replicate. Three-source energy harvestingsolar irradiance, ambient RF signals, and regenerative brakingsustains all network nodes for the full simulation duration, eliminating the energy depletion failures that limit conventional routing protocols.

Simulation results on a 50-node mobile EV network over a 1000 × 1000 m area demonstrate that the Meta-RL router achieves a final Packet Delivery Ratio of 97.4%, outperforming Distance-Only routing (95.3%), Deep RL with DQN (95.3%), and Energy-Aware routing (93.6%). The MAML policy operates optimally from the very first routing step, whereas the DQN baseline requires approximately 400 steps of online exploration to reach comparable performance. This immediate adaptability is a direct consequence of meta-training across diverse network configurations, and represents a significant practical advantage in real-world EV deployments where initial reliability is critical.

Future work will extend this framework in three directions. First, the MAML model will be deployed on a physical EV testbed with real V2X hardware to validate simulation findings under real-world channel conditions. Second, the routing framework will be extended to multi-agent settings where each EV node hosts an independent Meta-RL router, enabling fully decentralised cooperative routing. Third, the system will be evaluated for compatibility with 5G New Radio V2X communication standards, with particular attention to the latency requirements of safety-critical vehicular applications.

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