The increasing density and diversity of connected wireless devices, together with diverging applications requirements, are generating new technological challenges:
- IoT applications leverage more and more on open, standardized IoT stacks and semantics (e.g. IPSO-LWM2M/CoAP/UDP/6LoWPAN/6top/TSCH stack), are typically distributed, and change over time. However, scheduling of IoT network resources remains decoupled from the applications, and as a consequence there exist no solutions for automated reconfiguration upon application layer changes.
- Today’s wireless networks interconnect many heterogeneous devices (TV, smartphone, wearables, smart home solutions…) offering an ubiquitous connectivity platform using heterogeneous networks (Wi-Fi, IEEE 802.15.4, cellular). However, the deployment of the various connectivity platforms still happens in an uncoordinated way making it very difficult to cope with the increasing density of wireless devices.
- Sub-GHz technologies (433/868/915 MHz) have the potential to revolutionize the IoT domain due to their long range, low cost and high energy efficiency. But, current technologies lack the scalability, spectrum management and QoS needed for industry applications. Deployments by competing network operators are further hindered due to cross-network interference.
- With the exponential increase of both high throughput and resource constrained devices, the cost of operating and managing large-scale networks is becoming tremendous. Self-adaptive management and control mechanisms are crucial for addressing this scale.
- Recent advances in chip development, radio hardware platforms and software architectures pave the way towards very flexible wireless networks offering the potential to change the radio and network behavior without without disrupting the operation of wireless networks. However, there is a lack of intelligent strategies for true collaborative sharing of limited spectral resources between independent networks.
- Wireless communications is a catalyst for new intelligent applications such as localization and tracking, providing real-time insights for contextual enablers. However, current localization methods are either inaccurate, too costly, too energy hungry or not applicable for large-scale and challenging environments.
These challenges call for (1) collaborative and intelligent solutions to optimize radio resource usage, and (2) for flexible software/hardware architectures enabling runtime instantiation, (re)configuration and (re)programming of wireless devices . To cope with these challenges, the wireless activities at IDLab are structured in 6 research priorities:
- Application-driven network configurability and programmability: focussing on automatic translation of both standard-based and innovative IoT application dynamics (CoAP, bindings, grouping,…) into network reconfigurations
- High throughput network protocols: designing the new link layer, network and transport protocols for 10Gbps connectivity and more in very dense environments, complex topologies and using a myriad of technologies (Wi-Fi, cellular, 60 GHz, etc.)
- Resource constrained IoT devices & networks: investigating ubiquitous solutions that transparently support a plethora of technologies (e.g., LoRa, DASH7, BLE, IEEE 802.15.4g), targeting co-creation of hardware prototyping and embedded protocol stack design as well as improved interactions with embedded devices.
- Management and control of complex wireless networks: battling the heterogeneous and dense wireless environments of the future by relying on self-adaptive and learning-based approaches.
- Cognitive collaborative radio networks: realizing collaborative radio resource management between independent co-located wireless networks consisting of real-time, reconfigurable software defined radios by applying advanced learning and reasoning strategies.
- Localisation & tracking: localizing people and things in challenging environmental conditions by leveraging on off the shelf wireless technologies (a.o. UWB, Wi-Fi, Zigbee, BLE, Sigfox, DASH7) & through large-scale validations in testbeds.