Section: Research Program

Research program

Following up on the effort initiated by the team members during the last few years and building on an approach combining protocol design, data analytics, and experimental research, we propose a research program organized around three closely related objectives that are briefly described in the following.

• Technologies for accommodating low-end IoT devices: The IoT is expected to gradually connect billions of low-end devices to the Internet, and thereby drastically increase communication without human source or destination. Low-end IoT devices differ starkly from high-end IoT devices in terms of resources such as energy, memory, and computational power. Projections show this divide will not fundamentally change in the future and that IoT should ultimately interconnect a dense population of devices as tiny as dust particles, feeding off ambient power sources (energy harvesting). These characteristics constrain the software and communication protocols running on low-end IoT devices: they are neither able to run a common software platform such as Linux (or its derivatives), nor the standard protocol stack based on TCP/IP. Solutions for low-end IoT devices require thus: (i) optimized communication protocols taking into account radio technology evolution and devices constrained requirements; (ii) tailored software platforms providing high level programming, modular software updates as well as advanced support for new security and energy concentration features; (iii) unification of technologies for low-end IoT, which is too fragmented at the moment, guaranteeing integration with core or other edge networks.

• Technologies for leveraging high-end IoT devices' advents: High-end IoT devices are one of the most important instances of the connected devices supporting a noteworthy shift towards mobile Internet access. As our lives become more dependent on pervasive connectivity, our social patterns (as human being in the Internet era) are nowadays being reflected from our real life onto the virtual binary world. This gives birth to two tendencies. From one side, edge networks can now be utilized as mirrors to reflect the inherent human dynamics, their context, and interests thanks to their well organized recording and almost ubiquitous coverage. From the other side, social norms and structure dictating human behavior (e.g., interactions, mobility, interest, cultural patterns) are now directly influencing the way individuals interact with the network services and demand resources or content. In particular, we observe the particularities present in human dynamics shape the way (i.e., where, when, how, or what) resources, services, and infrastructures are used at the edge of the Internet. Hence, we claim a need to digitally study high-end IoT devices' end-users behaviors and to leverage this understanding in networking solutions' design, so as to optimize network exploitation. This suggests the integration of the heterogeneity and uncertainty of behaviors in designed networking solutions. For this, useful knowledge allowing the understanding of behaviors and context of users has to be extracted and delivered out of large masses of data. Such knowledge has to be then integrated in current design practices. This brings the idea of a more tactful networking design practice where the network is assigned with the human like capability of observation, interpretation, and reaction to daily life features and entities involving high-end IoT devices. Research activities here include: (i) the quest for meaningful data, which includes the integration of data from different sources, the need for scaling up data analysis, the usage and analysis of fine-grained datasets, or still, the completion of sparse and coarse grained datasets; (ii) expanding edge networks' usage understanding, which concerns analysis on how and when contextual information impact network usage, fine-grained analysis of short-term mobility of individuals, or the identification of patterns of behavior and novelty-seeking of individuals; (iii) human-driven prediction models, extensible to context awareness and adapted to individuals preferences in terms of novelty, difersity, or routines.

• Articulating the IoT edge with the core of the network: The edge is the interface between the IoT devices and the core network: some of the challenges encountered by IoT devices have their continuity at the edge of the network inside the gateway (i.e., interoperability, heterogeneity and mobility support). Besides, the edge should be able to support intermediary functions between devices and the rest of the core (e.g., the cloud). This includes: (i) proxying functionality, facilitating connections between devices and the Internet; (ii) machine learning enhanced IoT solutions, designed to improve performance of advanced IoT networked systems (e.g., through methods such as supervised, unsupervised or reinforcement learning) at adapted levels of the protocol stack (e.g., for multiple access, coding, choices); (iii) IoT data contextualization, so collection of meaningful IoT data (i.e., right data collected at the right time) can be earlier determined closer to the data source; (iv) intermediary computation through fog or Mobile Edge Computing (MEC) models, where IoT devices can obtain computing, data storage, and communication means with lower latency in a decentralized way; or (v) security of end-to-end IoT software supply-chain, including remote management and over-the-air updates.