Sensor Networks and the Rise of IoT
Sensor networks have rapidly increased in popularity in recent years, becoming a key focus of research and innovation. These networks typically consist of low-cost, battery-powered, and energy-constrained sensor nodes (SNs) dispersed across an area, each with limited energy resources that are challenging to replenish. To extend the lifespan of the entire network, energy-efficient communication strategies with less packet loss amongst SNs are essential.
One promising solution to this challenge is the use of unmanned aerial vehicles (UAVs), which offer greater flexibility and maneuverability in data capture use cases. UAVs can act as mobile data relays, aerial base stations, or intelligent data collectors for wireless sensor networks (WSNs), providing a less polluted communication path with improved line-of-sight (LoS) connectivity compared to traditional ground-based WSN architectures.
However, integrating energy-efficient UAV-assisted data gathering with the constraints of the ground-based WSN is a complex task. Three key areas must be considered when designing a cost-effective UAV-based WSN data gathering model:
- Energy-efficient ground network architecture: Optimizing the organization and communication of the ground sensor network.
- Reliable UAV-ground communication: Ensuring robust and efficient data transfer between the UAV and ground sensors.
- Power-efficient UAV path planning: Designing the UAV’s flight trajectory to minimize energy usage while maximizing data collection.
Addressing the Challenges with Software-Defined Wireless Sensor Networks (SDWSN)
The proposed solution to these challenges lies in the concept of software-defined wireless sensor networks (SDWSN). SDWSN integrates the principles of software-defined networking (SDN) into WSN architectures, enabling greater flexibility and adaptability.
In a traditional UAV-based WSN data gathering approach, the UAV’s flight path is typically restricted to limited options once the physical topology of each group of ground sensor nodes is set. This fixed nature can lead to suboptimal performance. SDWSN, on the other hand, allows for the dynamic reconfiguration of the ground network topology to align with the UAV’s optimal data collection path.
The core idea of the Fuzzy Travel Route concept is to enable the UAV’s flight path to be selected from a wider range of alternatives, rather than being fixed to a single predefined route. This organization allows the UAV path to be dynamically adjusted according to the updated ground network topology, offering greater flexibility and efficiency.
Enabling Adaptive Network Orchestration with SDWSN
The proposed SDWSN-based model for UAV-assisted data gathering consists of three main phases:
- Topological Scanning (Pre-Orchestration): The UAV collects control information about the available gateways and relevant nodes, passing this data to the cloud for analysis.
- Orchestration: The cloud processes the collected data, identifies the elected gateways and related network structure, and sends the updated configuration back to the ground network.
- Data Collection (Post-Orchestration): The UAV moves within the designed, updated fuzzy route to gather the sensor data from the reconfigured ground network.
Sensor networks play a crucial role in the Internet of Things (IoT) ecosystem, providing the foundational data sources for a wide range of smart applications. The flexibility and adaptability enabled by SDWSN are essential for effectively integrating UAV-assisted data gathering into dynamic IoT environments.
Adaptive Ground Network Topology and UAV Path Optimization
The proposed SDWSN-based model leverages the separation of the control and data planes to enable intelligent routing and network orchestration/re-orchestration as needed. This allows the ground network topology to be flexibly adapted to support the UAV’s optimal data collection path, while also considering the energy efficiency of both the UAV and the ground sensor nodes.
The key components of the ground network in this SDWSN model are:
- Leaf Nodes: Responsible for data acquisition and passing sensor data to higher-level routers or gateways.
- Router-Capable Nodes: Can be software-defined as routers or reduced-function leaf nodes, transferring data between leaf nodes and gateways.
- Gateway-Capable Nodes: Can be software-defined as gateways or dropped to lower-level router/leaf functionality, responsible for collecting and forwarding data to the UAV.
The cloud-based orchestration process involves:
- Analyzing the control data collected by the UAV to identify the optimal gateway locations and network structure.
- Transmitting the updated configuration to the ground network, allowing the nodes to assume their new roles.
This flexible, software-defined approach enables the ground network topology to be dynamically adjusted to support the UAV’s optimal data collection path, which is determined by solving an optimization problem that jointly considers:
- Minimizing UAV propulsion energy consumption
- Minimizing ground sensor energy consumption
- Maximizing data delivery to the UAV
Evaluating the SDWSN-based Data Gathering Model
To validate the proposed SDWSN-based data gathering model, a comprehensive simulation framework was developed, leveraging multiple tools:
- MATLAB: Used for UAV path design, including the Fuzzy Travel Route concept and smooth path optimization.
- Contiki-Cooja: Simulated the ground-based WSN communication, evaluating metrics like energy consumption and packet delivery.
- CupCarbon: Modeled the air-to-ground communication between the UAV and ground gateways.
- Mission Planner: Visualized the UAV’s flight path and provided real-world scenario validation.
The simulation results demonstrated the benefits of the SDWSN-based approach:
- Improved ground network energy efficiency: The dynamic network orchestration and reconfiguration enabled by SDWSN reduced the overall energy consumption of the ground sensor nodes compared to traditional fixed topologies.
- Enhanced UAV energy efficiency: The ability to adapt the UAV’s flight path to the updated ground network topology resulted in a more power-efficient data collection process.
- Increased data delivery: The SDWSN-based model achieved higher packet delivery rates from the ground sensors to the UAV, thanks to the improved network organization and air-to-ground communication.
- Reduced communication latency: The analytical model for the SDWSN orchestration and reconfiguration phases showed lower latency compared to the data collection phase, highlighting the efficiency of the control plane operations.
Conclusion: Embracing Adaptive Sensor Networks for the IoT Era
The proposed SDWSN-based data gathering model represents a significant advancement in the integration of UAV-assisted data collection and wireless sensor networks. By enabling the dynamic reconfiguration of the ground network topology to align with the UAV’s optimal data collection path, this approach overcomes the limitations of traditional fixed-topology solutions.
The flexibility and adaptability offered by SDWSN are crucial for effectively leveraging UAVs in the Internet of Things (IoT) ecosystem, where sensor networks must be able to respond to changing environmental conditions and application requirements. By balancing the energy efficiency of both the UAV and ground sensors, the SDWSN-based model ensures a cost-effective and reliable data gathering solution for a wide range of IoT applications.
As the demand for dynamic, flexible, and energy-efficient sensor network architectures continues to grow, the SDWSN paradigm and the Fuzzy Travel Route concept presented in this article offer a promising path forward for the future of UAV-enabled IoT data collection and management.