Cloud Wi-Fi Connectivity Management for Moving Things
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Solution Overview
Problem
Current communication networks are inadequate for supporting complex arrays of both moving and static nodes, such as those found in autonomous vehicle networks and the Internet of Moving Things, as they fail to provide reliable, scalable, and efficient connectivity.
Innovation Solution
A communication network architecture that dynamically configures a combination of fixed and mobile nodes, utilizing multi-network on-board units (OBUs) and cloud-based data-driven management to ensure robust, scalable, and energy-efficient connectivity, with adaptive protocols for various environments and use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current communication networks are used to support moving and static nodes, then network simplicity is maintained, but reliability and scalability of connectivity deteriorate
Solution Approach 1:
The network is segmented into multiple independent components including OBUs, access points, controllers, and gateways. Each component operates autonomously with defined functions, allowing the system to scale by adding or removing segments without affecting overall reliability. The segmentation enables isolated failure containment and independent optimization of each network element.
Solution Approach 2:
The network architecture employs dynamic configuration where OBUs can switch between mobile and fixed node roles based on operational needs. The system dynamically adjusts network topology, routing paths, and resource allocation in real-time to maintain connectivity reliability as vehicles move through different geographic areas and network conditions change.
2Adaptability or versatility
If the network supports both static and mobile nodes, then versatility is improved, but energy consumption increases
Solution Approach 1:
Different network nodes are assigned specialized functions and capabilities appropriate to their role. Mobile OBUs are equipped with wireless communication and location services, while fixed infrastructure nodes provide stable anchoring and resource management. This local optimization allows each node to consume energy only for its specific functions rather than maintaining all capabilities universally.
Solution Approach 2:
The system changes operational parameters dynamically based on node mobility state. When vehicles are stationary, OBUs can enter low-power modes or switch to fixed node operation with reduced energy consumption. The network adjusts transmission power, sampling rates, and communication frequency according to whether nodes are moving or static, optimizing energy efficiency while maintaining versatility.
3Reliability
If dynamic network configuration is implemented, then connectivity reliability is improved, but system complexity increases
Solution Approach 1:
A cloud-based controller acts as an intermediary that manages the complexity of dynamic network configuration. The controller receives status information from OBUs, makes centralized decisions about network topology and resource allocation, and distributes configuration commands. This intermediary approach maintains network resilience through dynamic adaptation while shielding individual nodes from the complexity of coordination and decision-making.
Solution Approach 2:
The system implements continuous feedback loops where OBUs report their operational status, location, and network conditions to the controller. The controller uses this feedback to dynamically adjust network configuration, routing, and resource allocation. This feedback mechanism enables automatic adaptation to changing conditions without requiring complex manual configuration or intervention.
4Productivity
If cloud-based data-driven management is used, then resource utilization is optimized, but network latency increases
Solution Approach 1:
The system performs preliminary actions by pre-processing data locally at OBUs and access points before transmission to the cloud. Local filtering, aggregation, and preliminary analysis reduce the volume of data requiring cloud processing and enable faster local responses. Critical time-sensitive operations are executed locally without cloud intervention, while non-time-critical analytics are performed in the cloud to optimize overall resource utilization.
Data Source
AI summary
Systems and methods are provided for cloud-based data-driven Wi-Fi connectivity management in a network of moving things.