Navigating the Evolving Landscape of Sensor Networks and IoT
Sensor networks have rapidly increased in popularity during recent years, emerging as a key subject of research and innovation. These networks typically include low-cost, battery-powered, and energy-constrained dispersed sensor nodes (SNs), each with limited energy resources, making it challenging to replenish after use. Consequently, energy-efficient communication strategies with less packet loss amongst SNs are essential to extend the lifespan of the entire network.
One promising solution to mitigate energy usage and packet loss in ground networks is the utilization of unmanned aerial vehicles (UAVs), which offer more flexibility and maneuverability in data capturing use cases. UAVs can serve as mobile relays, aerial base stations, or data acquisition platforms for wireless sensor networks (WSNs), providing a reasonably less polluted solution for data gathering from dispersed ground SNs.
Sensor-networks.org explores the adaptive sensor placement and energy management strategies that can enhance the efficiency of data collection networks, leveraging the capabilities of UAVs and software-defined networking (SDN) technologies.
Optimizing UAV-Enabled Data Gathering: Balancing Efficiency and Flexibility
Designing a cost-effective UAV-based WSN data gathering model requires addressing three key areas: an energy-efficient ground network architecture, a reliable UAV-Ground communication approach, and a power-efficient UAV path planning model.
Conventional UAV-based WSN data gathering arrangements often restrict the UAV’s path to limited options, as the physical topology of each group of ground sensor nodes is fixed. This limitation can be overcome by setting the gateways to be selected dynamically, allowing the groups’ topologies to be flexibly re-organized through software. This concept, referred to as the Fuzzy Travel Route, enables the UAV’s path to be dynamically adjusted according to the updated ground network topology, enhancing the energy efficiency of both the UAV and the SNs.
The software-defined wireless sensor network (SDWSN) approach can facilitate this flexibility by separating the control plane from the data plane. The control plane transmits the logical operations and relevant decisions to orchestrate the network structure, while the data plane forwards the data packets to an appropriate interface, such as a cloud processing center. This separation enables an intelligent routing mechanism and the orchestration and re-orchestration of the network topology to support energy consumption and enhance UAV path flexibility.
Enhancing UAV Path Design through Ground Network Adaptability
The Fuzzy Travel Route concept allows the UAV’s flight path to be chosen from a wider range of alternatives, rather than being fixed in one defined path. This organization enables the UAV’s path to be dynamically adjusted according to the updated ground network topology, providing flexibility for the network formations.
The proposed model integrates the UAV path design, air-to-Ground connectivity, and ground network communication, leveraging the SDWSN functionality to support the orchestration and re-orchestration of the ground network. This approach aims to jointly minimize the UAV propulsion energy usage and ground SNs energy consumption while maximizing the packet delivery to the UAV, resulting in an optimal UAV path design within the UAV’s fuzzy domain.
Adaptive Ground Network Organization: Enabling Flexible Data Collection
The ground network in the proposed model is organized into leaf nodes, router-capable nodes, and gateway-capable nodes, each with the ability to be software-defined and reconfigured as needed. This flexible architecture allows the network to adapt to changing demands and support the movement of the UAV, enhancing the energy efficiency of the communication network.
The gateway-capable nodes can be software-defined to operate as gateways or dropped to lower-level routers and leaf nodes, depending on their updated roles and their distributions in random positions within the ground network. The presence of multiple gateways on a large-scale network facilitates the prevention of rapid energy draining of gateway-configured nodes and improves reliability by mitigating the risk of the failure of a single gateway.
Optimizing UAV Path and Ground Network Performance
The proposed optimization problem aims to jointly minimize the UAV propulsion energy usage and ground SNs energy consumption while maximizing the packet delivery to the UAV. This is achieved by defining a cost function that considers the UAV’s trajectory, communication throughput, and the energy consumption of the ground network.
The optimization problem is formulated as a non-convex problem, which is then solved using a sequential convex approximation (SCA) technique. This approach allows the problem to be converted into a linear programming problem that can be efficiently solved.
Evaluating the Proposed Approach: Simulation and Performance Analysis
To evaluate the proposed model, a comprehensive simulation framework is developed, leveraging multiple tools:
- MATLAB: For defining the spatial distribution of ground sensor nodes, designing the UAV’s fuzzy route and relaxed path, and calculating the UAV’s energy consumption and performance metrics.
- Contiki-Cooja: For simulating the ground network communication, including the topological pre-orchestration scanning, orchestration, and data gathering post-orchestration phases, and analyzing the ground network’s energy consumption and packet delivery.
- CupCarbon: For simulating the air-to-Ground communication between the elected gateways and the UAV, evaluating the packet delivery rate, UAV receiver energy consumption, and overall network cost.
- Mission Planner: For visualizing and validating the UAV’s smooth path design based on the outputs from the MATLAB simulations.
The simulation outcomes demonstrate the advantages of the proposed SDWSN-enabled data gathering model in terms of energy efficiency, packet delivery rate, and communication latency, compared to traditional UAV-based data collection approaches and the successive convex approximation (SCA) method.
Conclusion and Future Directions
This article has presented a comprehensive approach to enhancing the efficiency of data collection networks by leveraging the capabilities of UAVs and the flexibility of software-defined wireless sensor networks (SDWSNs). The proposed model integrates the UAV path design, air-to-Ground connectivity, and ground network communication, enabling adaptive sensor placement and energy management strategies.
The key contributions of this work include:
- Defining the Fuzzy Travel Route concept to provide a flexible span for UAV path design, allowing the path to be dynamically adjusted according to the updated ground network topology.
- Applying the SDWSN functionality to the ground network, supporting the orchestration and re-orchestration of the network structure and enhancing the energy efficiency of the communication network.
- Formulating an optimization problem to obtain the optimal UAV path design within the fuzzy domain, considering the joint minimization of UAV propulsion energy usage and ground SNs energy consumption while maximizing the packet delivery to the UAV.
As future work, the integration of fixed-wing UAVs and the development of secure clustering approaches for data reliability in the data gathering stage could further enhance the proposed model. Additionally, the exploration of artificial intelligence (AI)-enabled models to predict the re-orchestration of the ground nodes in response to network events could provide valuable insights for flexible IoT-based sensor network organization.
Sensor-networks.org continues to drive innovation in the field of sensor networks and IoT, showcasing cutting-edge research and practical solutions that empower organizations to harness the power of adaptive and energy-efficient data collection systems.