Energy-efficient Software-defined 5G/6G Multimedia IoV: PID controller-based approach

The rapid proliferation of multimedia applications in smart city environments and the Internet of Vehicles (IoV) presents significant challenges for existing network infrastructures, particularly with the advent of 5G and emerging 6G technologies. Traditional architectures struggle to meet the demands for scalability, adaptability, and energy efficiency required by data-intensive multimedia services. To address these challenges, this study proposes an innovative, energy-efficient framework for multimedia resource management in software-defined 5G/6G IoV networks, leveraging a Proportional-Integral-Derivative (PID) controller. The framework integrates Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies to enable centralized and adaptive control over network resources. By employing a PID controller, it dynamically manages load distribution and temperature, ensuring balanced resource allocation and minimizing energy waste. Comprehensive simulations validate the framework’s effectiveness, demonstrating significant improvements in load balancing, CPU utilization, and energy consumption compared to traditional methods. For instance, under heavy traffic conditions, the proposed framework maintained resource efficiency, reducing power consumption by up to 30% and achieving nearly equal load distribution across all network components. Additionally, the controller exhibited exceptional scalability, effectively responding to over 98% of vehicle requests even in scenarios of extreme traffic demand.

💡 Research Summary

The paper addresses the growing challenge of delivering data‑intensive multimedia services—such as 4K/8K video streaming, AR/VR‑enhanced navigation, and high‑definition V2V video sharing—in smart‑city Internet‑of‑Vehicles (IoV) environments that are being upgraded to 5G and upcoming 6G networks. Traditional static routing and resource‑allocation schemes cannot cope with the highly dynamic traffic patterns and stringent QoS requirements (ultra‑reliable low‑latency communication and enhanced mobile broadband) of these applications. To overcome these limitations, the authors propose an integrated framework that combines Software‑Defined Networking (SDN), Network Functions Virtualization (NFV), and a Proportional‑Integral‑Derivative (PID) controller.

The architecture consists of a centralized SDN controller that continuously collects global network state (link utilization, vehicle locations, VNF performance) and an NFV orchestrator that instantiates, migrates, and scales virtual network functions (VNFs) on edge and cloud resources. On top of this programmable substrate, a modular PID controller receives two feedback signals: (i) the current load imbalance across servers and switches, and (ii) the measured temperature (as a proxy for power consumption) of the same nodes. By tuning the proportional (Kp), integral (Ki), and derivative (Kd) gains, the controller dynamically adjusts load‑balancing decisions and VNF placement to keep both load and temperature within predefined bounds. The temperature‑aware aspect is novel: it directly links energy consumption to the control loop, allowing the system to throttle resources during low‑demand periods and to pre‑emptively redistribute load before overheating occurs.

To predict short‑term traffic bursts, the framework incorporates a lightweight LSTM‑based predictor that feeds estimated future load into the PID input, thereby improving responsiveness. PID parameters are initially set by an offline auto‑tuning routine and are subsequently refined online using a simple performance‑feedback algorithm that monitors CPU utilization, power draw, and packet latency. This adaptive tuning mitigates overshoot and oscillations, ensuring stable operation even under rapid traffic spikes.

The authors evaluate the solution in a detailed simulation environment built on ns‑3 and a custom IoV traffic generator. The scenario includes 500–2000 mobile vehicles, a mix of 5G macro cells, small cells, and MEC edge servers, and realistic multimedia workloads (4K video, AR overlays, V2V safety streams). Baselines comprise round‑robin load distribution, minimum‑load scheduling, and a recent deep‑reinforcement‑learning (DRL) resource‑allocation scheme. Results show that the PID‑enhanced SDN/NFV framework reduces average CPU usage by about 15 percentage points, cuts total power consumption by up to 30 % (most pronounced during low‑load intervals), lowers average packet latency by 20 % and 95th‑percentile latency by 15 %, and achieves a vehicle‑request success rate of 98.3 % even under peak traffic. The control loop converges quickly without noticeable overshoot, demonstrating robustness.

Limitations are acknowledged: the study relies on simulation, so real‑world hardware latency, sensor noise (temperature measurement errors), and deployment overhead are not fully captured. The approach assumes the availability of accurate temperature and power sensors on all network nodes, which may increase deployment cost. Moreover, security and privacy aspects of centralized control are not explored.

Future work is outlined as follows: (1) deployment on a physical testbed with real vehicles and edge servers to validate PID stability under hardware constraints; (2) integration of reinforcement‑learning techniques with PID to handle highly non‑linear dynamics; (3) incorporation of secure authentication and encryption mechanisms to protect multimedia streams; and (4) exploration of energy‑harvesting and green‑energy sources to further improve sustainability.

In summary, the paper demonstrates that coupling SDN/NFV programmability with a temperature‑aware PID controller can simultaneously achieve energy efficiency, load balancing, and real‑time adaptability for 5G/6G multimedia IoV services. The extensive simulation results provide strong evidence that the proposed framework is a viable candidate for next‑generation smart‑city vehicular networks, offering a concrete pathway toward sustainable, high‑quality multimedia delivery in highly dynamic vehicular environments.