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Wireless network management is important to ensure the performance, utilization, allocation, and robustness of the network is optimized. Until now, wireless network management has typically been dictated by in-band information, such as wireless measurements, client locations, or even device state. This position paper explores fundamental new ways to manage the network by utilizing out-of-band data provided by a rich deployment of sensors. Out-of-band data can capture information about the users, objects, or environments associated with a network device, meaning that richer contextual policies can be implemented in the network. We propose an architecture called SenseNet, which builds upon three recent trends: (1) the massive deployment of sensors today, (2) the existence of deep-learning algorithms to glean meaningful insights from the sensory data, and (3) the provisioning of edge computing resources to provide real-time actuation of new sensor-based policies. A brief evaluation shows the feasibility and motivates SenseNet and afterwards challenges towards practical deployment are discussed.

Rethinking Wireless Network Management Through Sensor-driven Contextual Analysis