Advanced Networking for Mobile Internet of Things

How can we create mobile SDNs that perform local load balancing and interference management and yet  throughput, utility, etc., globally? Can we crowdsource access points and their current performance and use this for mobile apps?


Open Wi-Fi access points (APs) are demonstrating that they can provide opportunistic data services to moving vehicles. We present CrowdWiFi, a novel vehicular middleware to identify and localise roadside WiFi APs that are located outside or inside buildings. CrowdWiFi consists of two components: online compressive sensing and offline crowdsourcing. On- line compressive sensing (CS) presents an efficient framework for the coarse-grained estimation of nearby APs along the driving route, where received signal strength (RSS) values are recorded at runtime, and the number and locations of  APs are recovered immediately based on limited RSS readings.

Offline crowdsourcing assigns the online CS tasks to crowd-vehicles and aggregates answers on a bipartite graphical model. Crowd-server runs offline crowdsourcing and iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results. It then refines the estimation of APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the number of measurements needed for AP recovery, while maintaining satisfactory counting and localisation accuracy. In addition, the impact of CrowdWiFi middleware on Wi-Fi handoff and data transmission is examined.

UbiFlow is the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. It adopts multiple controllers to divide urban-scale SDN into different geographic partitions and achieve distributed control of IoT flows. A distributed hashing-based overlay structure is deployed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been examined and studied.


The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimised selection of access points in multi-networks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multi-networks; e.g. 67.21% improvement of throughput, 72.99% improvement of delay, and 69.59% improvement of jitter, in comparison with other OpenFlow protocols.

Key outcomes:

We have deployed different sensor platforms on existing Intel Galileo based gateway architecture (weather, light, air quality and agri sensors).

We used live sensor data to drive responsive story telling in the park via a City-Insights app.

We used anonymised and aggregated mobile phone data to track the flow of people through the park.