2.1 Vision and approach

TRiBE stands for “Internet Beyond the Usual” and belongs to the Inria theme “Networks and Telecommunications” as well as contributes to the “Challenge no 11: Toward a trustworthy Internet of Everything” of the strategic plan of Inria. Building on an approach combining protocol design, data analytics, and experimental research, the research contributions of TRiBE aim at contributing to the design of smart, unified, and tactful Internet edge networks, skilled for answering applications, services, or end-users' purposes.

All the emerging IoT specificities and requirements (i.e., heterogeneity of devices and services, densification, traffic growth, ubiquitous cyber-physical context, etc) bring new demands and consequently, new scientific and technological challenges to the edge of the Internet. In this context, our conviction is that the success of the Internet of Things is rooted: in the network design's choices involving the devices, in the intelligence of the protocols and associated services as well as in the capability of reaction and adaptation of the edge-core network's communication loop.

Toward this belief, we base our approach on the combination of protocol design, data analysis, and experimental research, while meeting the requirements and challenges brought by the IoT to the edge of the Internet. Therefore, the research of TRiBE is organized around the following research directions:

• Technologies for accommodating low-end IoT devices: we tackle the optimization, simplification, and unification requirements imposed by the heterogeneity and low capabilities of low-end IoT devices. This brings the necessity to deal with limitations and to propose solutions close to hardware and software specifications.
• Technologies for leveraging high-end IoT devices’ advent: we focus on learning the behaviors of high-end IoT devices, the smart devices. More specifically, the idea is to take advantage of the “how in the spatiotemporal scale” and the “for what purpose” these devices are used. This brings the human element into play, which dynamics are shaping the way their smart devices are interacting with the edge of the Internet and, consequently, are requesting and consuming network resources and services.
• Technologies for edge-core network interaction:This element closes the network $\leftrightarrow$ usability $\leftrightarrow$ device $\leftrightarrow$ network loop" by bringing solutions supporting functions and communication between IoT devices and the core of the Internet, while putting into practice the solutions proposed in the two previous directions.

Through these three research axes, the team places its efforts on the three main elements composing the ecosystem of IoT devices: (1) the device itself, (2) their usability, and (3) their network context. Together, these research directions will contribute to our vision toward a Smart, Unified, and Tactful Internet edge skilled for answering the application, services, or end-users’ purposes.

2.2 New challenges

The Internet has steadily evolved over the past decades from a small homogeneous to a gigantic Internet of Things (IoT) interconnecting an extremely wide variety of machines (e.g., PCs, smartphones, sensors/actuators, smart appliances, smart vehicles), and providing an extremely wide variety of services. Globally, devices and connections are growing faster than both the population and Internet users, as foreseen by Cisco. Forecasts mention an IoT market that will attain a compound annual growth rate of 28.5% from 2016 to 2020 as well as an installed base of IoT devices of over 75.4B devices by 2025. Added to these statistics is the fact that global mobile data traffic will grow nearly twice as fast as fixed IP traffic from 2017 to 2022: Smartphones account for most of this growth.

Hence, the edge of this network now consists of dense deployment of machines ranging from PCs to smartphones, from sensors/actuators to smart appliances, and from smart vehicles to diverse kinds of robots. As a consequence, humans are immersed in a highly connected and ubiquitous cyber-physical context, and as end-users of the network and its numerous services, their satisfaction has become the main focus.

In this context, the IoT is simultaneously used as a tool to gather more data, and as a means to automate more advanced control. Some businesses and institutions aim to gather more data to better understand their customers, so as to improve services. Other efforts aim to further immerse their customers into a flourishing, integrated cyber-physical environment, which can automatically and optimally adapt to their needs. All these emerging IoT-related opportunities bring new requirements and consequently, new scientific and technological challenges to the edge of the Internet.

First, the densified deployment of heterogeneous low-end IoT devices (e.g. sensors, actuators, etc.) at the edge of the Internet requires dealing with (1) the accommodation of machines with extremely limited capabilities, with a primary focus on low power requirements while (2) allowing their seamless integration in interoperable systems (often using IP as a common factor).

Second, today's pervasiveness of high-end IoT devices (e.g. smart handheld devices) distribute increasing capabilities (i.e., processing, storage, connectivity) at the edge of the network, and make our real-life and virtual activities seamlessly merge together. In this domain, we need a better understanding of: (1) when, where, and for what the high-end IoT devices are used, (2) how the uses vary among individuals, and (3) how social norms and structure dictate individuals' behavior influence the way they interact with network services and demand resources.

Related to the challenge hereabove, people's mobility and activity patterns are general in nature, and similarities emerge in different cities worldwide. The analysis of these patterns reveals many interesting properties of human mobility and activity patterns. While all these properties have been investigated at length, the COVID-19 pandemic highly perturbed our mobility patterns and use of urban spaces. This raises important questions: (1) how mobility patterns at an urban scale were affected by the pandemic; (2) can the modeling of such patterns provide a clear association with an epidemic spread, such as COVID-19 in different areas of a city?; last but not least, (3) can we still recommend safe outdoor path inside cities in order to limit the exposure to virus propagation? The 1st question answer is also closely related to the changes in “how” and “where” network resources were demanded.

The research contributions of TRiBE aim at dealing with such requirements and challenges brought to the Internet's edge. One should design adapted algorithms and communication mechanisms and network users' behavior modeling for addressing such challenges while leveraging the new technological opportunities brought by the Internet of Things.

3.1 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.

• [Axis 1] 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.
• [Axis 2] Technologies for leveraging high-end IoT devices' advents: High-end IoT devices are one of the most important instances of connected devices supporting a noteworthy shift towards mobile Internet access. As our lives become more dependent on pervasive connectivity, our social patterns (as human beings 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. On the other side, social norms and structures dictating human behavior (e.g., interactions, mobility, interest, cultural patterns) are now directly influencing the way individuals interact with 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 into 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, diversity, or routines. Finally, the current epidemic crises also showed a new potential impact of mobility's understanding and patterns modeling: such investigation can potentially provide a clear association with the epidemic spread (e.g., such as COVID-19) in different areas of a city.
• [Axis 3] 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 the 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 that the 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.

5.1 Footprint of research

Our research activities are not expected to impact the environment, since we work on algorithm design and software editing. Our experiments are not going beyond extremely short-scale lab experiments. The IT activities that are most likely to impact the climate are massive data stored in data centers, bitcoin mining and heavy deep learning training and we are not practicing any of them (although we plan to do some distributed machine learning for optimizing protocols).

Furthermore, we believe our research can positively impact society and the environment. This belief is due to the following ascertainments, which naturally conduct our research and our envisaged outcomes.

Assertion: The energy efficiency in the ICT and data centers sectors is considered a key part of the energy and climate targets for 2020-2030, of the European energy policies. The high energy consumption (past and forecasted future: forecasted to consume 13% of the worldwide electricity by 2030) is due not only to the in-expansion electricity needs of technological advances (e.g., data centers, new traffic demand, and connected devices) but also due to the energy-harmful over-provisioning tendency in the ICT sector.

For example, from one side, the community agrees there is a limit on how far energy-efficient data centers could go. This limitation calls for a new architectural paradigm, where Internet intelligence should move from centralized computing facilities to distributed and in-network computation. Still, the very fast-growing trend at the Internet edge (kept by the different types and capabilities of IoT devices and consequently, by their communication needs) accelerates the unprecedented proliferation of new performance-hungry IoT applications and services. Such devices will require increasing computational power and will be more power-hungry than ever.

On the other hand, considering smart devices inherit the dynamics and the decision-making of their users, mobility and heterogeneous behavior of individuals add uncertainties on where and when network resources will be needed. The standard practice in the current Internet to tackle this instability has been the any-and-anywhere extra-supplying of resources in the network. Nevertheless, in an Internet that has become essentially mobile, such over-provisioning will make energy consumption rapidly inflate, which becomes too costly and a practice that asks for revision.

TRiBE environmental responsibilities:

• TRiBE research is naturally targetting a scenario where network intelligence is pushed much closer to end-users – and consequently, to the edge of the Internet. In this sense, edge intelligence (i.e., learning, reasoning, and decision-making) will provide distributed autonomy, replacing the classical centralized structures. TRiBE results will thus contribute to (1) using a lower amount of aggregated power in dispersed locations and (2) avoiding the energy consumption related to the transmission of information back and forth to the Internet core. This conviction is the common thread in the suitable by-design solutions of the 2nd and 3rd TRiBE's axis, which will naturally contribute to the new energy-efficient architectural evolution of the Internet.
• TRiBE research pursues the conviction that methods allowing to smartly and efficiently allocate/use resources (of devices and the network) at the Internet edge are energy-friendly and contribute to the IT sector's electricity needs. This conviction is also the common thread behind the 2nd and 3rd TRiBE's axis.
• In the 1st TRiBE's axis, TRiBE goals also relate to the provision of optimized communication protocols and software solutions designed to fit the stark specificities of low-end IoT devices while taking into account radio technology evolution. The motivation here is to efficiently use and manage the billions of low-end devices expected to (i) gradually connect to and (2) drastically increase the communication, and consequently, the energy consumption, on the Internet. TRiBE's 1st research axis pursues the conviction that the smart accommodation of low-end IoT devices' related solutions will contribute to energy efficiency at the Internet edge. In a part of our research work, we focus on constrained devices (constrained in processing power and energy) and provide efficient algorithms in computation and communication reduction, both being translated into energy savings. Moreover, by making complex computations feasible on IoT devices and at the edge, we avoid inefficiencies in transmitting information back and forth.

5.2 Impact of research results

The rise of the Internet of Things will naturally lead to an increase by a significant factor: the number of connected devices. This a priori would negatively impact the environment since it would multiply the power consumption of networks. Nevertheless, one of the main IoT applications is the control of the environment by monitoring and curing critical environmental situations. Most of them would be low-powered wireless low-end devices, which are very likely powered by solar energy sources. Our research focuses (1) on the optimization and standardization of very efficient low-end networks, (2) on the power usage contention of high-end devices, and (3) on the cost limitation of creating a sensor field's digital twin by a green blockchain design. This second goal focuses on optimizing the quantity of information device-local applications should move outside the Internet edge, such as for edge machine learning.

Besides, the understanding of the way carried high-end IoT devices move and interact with one another (i.e., related to axis 2 and 3 of TRiBE) have the potential to impact epidemiology studies, urbanization investigation, and Internet provisioning (e.g., in the successful comprehension of the spread of epidemics or of the population; in urban planning; in intelligent transportation systems in smart cities; for urban space management; or in more suitable resource allocation for devices). The SafeCityMap 5 and Ariadne Covid Inria-Covid projects carried by members of the team reinforce such assertion.

In particular, the SafeCityMap project started its 2nd phase with investigations on the impact of the 1st, 2nd, and 3rd lockdown on the regular mobility habits of Paris population. Results of such investigations are periodically posted in the interactive webpage here: SafeCityMap website. Besides, we are currently investigating if the mobility modeling performed in the 1st and 2nd phase of the project, can be used as proxies at the inference of pollution and noise indicators. Preliminary investigations show a strong correlation between such indicators and SafeCityMap mobility modeling.

A sizable part of our research activities is carried on top of open-source software that we develop, and especially the open source software platform RIOT, an OS for the Internet of Things, targeting low-power embedded devices based on microcontrollers (i.e., related to axis 1 of TRiBE). Several TRiBE members contribute actively to this platform, around which a large international community has snowballed. In this way, research and developments that improve energy efficiency are made readily available to IoT practitioners, e.g. through RIOT or other software in the ecosystem.

Last but not least, another means for our research results to have an impact is through contributions to standardization (including IETF): TRiBE members co-author standards and help to define and specify efficient protocols and their optimization.

7.1 New software

7.2 New platforms

Open Experimental IoT Platforms

Participants: Cedric Adjih, Francois-Xavier Molina, Alexandre Abadie, Koen Zandberg, Emmanuel Baccelli.

One necessity for research in the domain of IoT is to establish and improve IoT hardware platforms and testbeds, that integrate representative scenarios (such as Smart Energy, Home Automation etc.) and follow the evolution of technology, including radio technologies, and associated experimentation tools. For that, we plan to build upon the FIT IoT-LAB federated testbeds, that we have participated in designing and deploying recently. We plan to further develop FIT IoT-LAB with more heterogeneous, up-to-date IoT hardware and radios that will provide a usable and realistic experimentation environment. The goal is to provide a tool that enables testing a validation of upcoming software platforms and network stacks targeting concrete IoT deployments.

In parallel, on the software side, IoT hardware available so far has made it uneasy for developers to build apps that run across heterogeneous hardware platforms. For instance, Linux does not scale down to small, energy- constrained devices, while microcontroller-based OS alternatives were so far rudimentary and yield a steep learning curve and lengthy development life-cycles because they do not support standard programming and debugging tools. As a result, another necessity for research in this domain is to allow the emergence of it more powerful, unifying IoT software platforms, to bridge this gap. For that, we plan to build upon RIOT, a new open source software platform that provides a portable, Linux-like API for heterogeneous IoT hardware. We plan to continue to develop the systems and network stacks aspects of RIOT, within the open source developer community currently emerging around RIOT, which we co-founded together with Freie Universitaet Berlin. The key challenge is to improve usability and add functionalities while maintaining architectural consistency and a small enough memory footprint. The goal is to provide an IoT software platform that can be used like Linux is used for less constrained machines, both (i) in the context of research and/or teaching, as well as (ii) in industrial contexts. Of course, we plan to use it ourselves for our own experimental research activities in the domain of IoT e.g., as an API to implement novel network protocols running on IoT hardware, to be tested and validated on IoT-LAB testbeds.

8.1 Lightweight virtualization and fault isolation for small software functions on low-power IoT microcontrollers [Axis 1]

Participants: Koen Zandberg, Emmanuel Baccelli, Shenghao Yuan, Frederic Besson, JP Talpin.

Low-power operating system runtimes used on IoT microcontrollers typically provide rudimentary APIs, basic connectivity and, sometimes, a (secure) firmware update mechanism. In contrast, on less constrained hardware, networked software has entered the age of serverless, microservices and agility. With a view to bridging this gap, in the paper, we design Femto-Containers, a new middleware runtime that can be embedded on heterogeneous low-power IoT devices. Femto-Containers enable the secure deployment, execution and isolation of small virtual software functions on low-power IoT devices, over the network. We implement FemtoContainers, and provide integration in RIOT, a popular open-source IoT operating system. We then evaluate the performance of our implementation, which was formally verified for fault isolation, guaranteeing that RIOT is shielded from logic loaded and executed in a Femto-Container. Our experiments on various popular microcontroller architectures (Arm Cortex-M, ESP32 and RISC-V) show that Femto-Containers offer an attractive trade-off in terms of memory footprint overhead, energy consumption, and security

This work was published at A and A* conferences ACM MIDDLEWARE'22 31 and at CAV'22 30.

8.2 Standardization of General-Purpose Secure Software Updates for IoT Devices [Axis 1]

Participants: Koen Zandberg, Emmanuel Baccelli.

TRiBE co-authors the new IETF standard (work-in-progress) providing low-end IoT devices with secure software updates. The Internet Draft draft-ietf-suit-manifest-21 specifies a Concise Binary Object Representation (CBOR)-based Serialization Format for the Software Updates for Internet of Things (SUIT) Manifest. This specification describes the format of a manifest. A manifest is a bundle of metadata about the firmware for an IoT device, where to find the firmware, the devices to which it applies, and cryptographic information protecting the manifest. Firmware updates and secure boot both tend to use sequences of common operations, so the manifest encodes those sequences of operations, rather than declaring the metadata. The manifest also serves as a building block for secure boot.

This work is published in the IETF Internet Draft available online at draft-ietf-suit-manifest-21.

8.3 Post-Quantum Cybersecurity for Low-power IoT [Axis 1]

Participants: Koen Zandberg, Emmanuel Baccelli, Benjamin Smith, Gustavo Banegas, Adrian Herrmann.

As the Internet of Things (IoT) rolls out today to devices whose lifetime may well exceed a decade, conservative threat models should consider adversaries with access to quantum computing power. The IETF-specified SUIT standard defines a security architecture for IoT software updates, standardizing metadata and cryptographic tools – digital signatures and hash functions – to guarantee the update's legitimacy. SUIT performance has been evaluated in the pre-quantum context, but not yet in a post-quantum context. Taking the open-source implementation of SUIT available in RIOT as a case study, we survey post-quantum considerations, and quantum-resistant digital signatures in particular, focusing on low-power, microcontroller-based IoT devices with stringent memory, CPU, and energy consumption constraints. We benchmark a range of pre- and post-quantum signature schemes on a range of IoT hardware including ARM Cortex-M, RISC-V, and Espressif (ESP32), which form the bulk of modern 32-bit microcontroller architectures. Interpreting our benchmarks in the context of SUIT, we estimate the real-world impact of the transition from pre- to post-quantum signatures.

This work is published in ACNS'22 33.

8.4 Modern Random Access: Irregular Repetition Slotted Aloha (IRSA) [Axis 1]

Participants: Iman Hmedoush, Cédric Adjih, Paul Mühlethaler [Inria, EVA], Chung Shue Chen [Nokia Bell Labs], Charles Dumas [Nokia Bell Labs], Lou Salaun [Nokia Bell Labs], Kinda Khawam [UVSQ].

Wireless communications play an important part in the systems of the Internet of Things (IoT). Recently, there has been a trend towards long-range communications systems for the IoT, including cellular networks. For many use cases, such as massive machine-type communications (mMTC), performance can be gained by moving away from the classical model of connection establishment and adopting random access methods. Associated with physical layer techniques such as Successive Interference Cancellation (SIC), or Non-Orthogonal Multiple Access (NOMA), the performance of random access can be dramatically improved, giving rise to novel random access protocol designs.

In this line of work, we are studying a modern method of random access for packet networks, named “Irregular Repetition Slotted Aloha (IRSA)”, that had been recently proposed: it is based on repeating transmitted packets and on the use of successive interference cancellation at the receiver. In classical idealized settings of slotted random access protocols (where slotted ALOHA achieves 1/e), it has been shown that IRSA could asymptotically achieve the maximal throughput of 1 packet per slot.

This year, we considered that the IRSA random access, as others, can be formalized as competition and can be analyzed from a Game Theory point of view; finally, we applied more complex machine-learning techniques to IRSA (in Section 8.4.2). Most of the work had been in the context of Iman Hmedoush's Ph.D. thesis which was defended in May 2022.

8.5 UAV Communications

8.6 5G Communications

8.7 On the Edge of the Deployment: A Conceptual and Practical Survey on Multi-Access Edge Computing Implementations [Axis 3]

Participants: Pedro Cruz, Aline Carneiro Viana, Nadjib Achir.

Multi-Access Edge Computing (MEC) attracts much attention from the scientific community due to its scientific, technical, and commercial implications. Still, MEC remains unfinished. In their majority, the existing MEC implementations are incomplete, which hardens or invalidates their practical deployment. As an effort to the future solutions aiming to fill this gap, it is essential to study and understand a series of experimental implementations and deployments. In this context, this work first brings a discussion on existing MEC implementations regarding the applications they target and their vision (i.e., whether they are more network-related or more distributed systems). Second, we list literature on MEC implementations according to their strategies and their consequences for the overall implementation project. We then discuss the deployment effort for each implementation. We also compare the tools developers used to make their MEC systems a reality. Finally, we discuss the issues that MEC implementations are yet to address. By bringing a better comprehension of MEC implementations, we hope this work will help developers develop their own or use MEC implementations.

This work was accepted in ACM Computing Surveys Journal 14 and published online in December 2022.

8.8 Impact of User Privacy and Mobility on Edge Offloading [Axis 3]

Participants: Esper João Paulo [Universidade Federal de Minas Gerais, Brazil], Nadjib Achir, Kleber Vieira Cardoso [Universidade Federal de Goiás, Brazil], Jussara M. Almeida [Universidade Federal de Minas Gerais, Brazil].

Offloading high-demanding applications to the edge provides better quality of experience (QoE) for users with limited hardware devices. However, to maintain a competitive QoE, infrastructure and service providers must adapt to users' different mobility patterns, which can be challenging, especially for location-based applications. Another issue that needs to be tackled is the increasing demand for user privacy protection. With less (accurate) information regarding user location, preferences, and usage patterns, forecasting the performance of offloading mechanisms becomes even more challenging. This work discusses the impacts of users' privacy and mobility when offloading to the edge. Different privacy and mobility scenarios were simulated and discussed to shed light on the trade-offs among privacy protection, mobility, and offloading performance.

This article was submitted to ICC 2023.

8.9 Energy Efficient adaptive sampling frequency of human mobility [Axis 2]

Participants: Panagiota Katsikouli, Aline Carneiro Viana, Marco Fiore, Diego Madariaga.

In this work, we aim at answering the question “at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?”.

Our analyses on fine-grained GPS trajectories from users around the world unveil (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. Our analysis of fine-grained trajectories unveils a novel linear scaling law of the localization error with respect to the sampling interval. Such results were published at IEEE Globecom 2017  44.

Building on these results, we challenge the idea of a fixed sampling frequency and present a lightweight, energy-efficient, mobility aware adaptive location sampling mechanism. We thus present DUCTILOC, a location sampling mechanism that takes advantage of the law above to profile users and then adapt the position tracking frequency to their mobility. Our design is energy efficient, as DUCTILOC does not rely on power-hungry sensors or expensive computations; moreover, it provides a handy knob to control energy usage, by configuring the target positioning accuracy. Real-world experiments with an Android implementation of DUCTILOC show that it can effectively adjust the sampling frequency to the mobility habits of each individual and target accuracy level, reducing the energy consumption by 60% to 98% with respect to a baseline periodic sampling.

This work is under resubmission to the IEEE ACCESS journal and also registered at Hal Inria as the Research Report 8. It represents the final contribution of the collaboration with Marco Fiore, Panagiota Katsikouli, who spent 5 months in our team working as an internship in 2017, and Diego Madariaga who spent 3 months in 2018 in our team working as an internship).

8.10 Data offloading decision via mobile crowdsensing [Axis 3]

Participants: Emanuel Lima [U. of Porto], Aline Carneiro Viana, Ana Aguiar [U. of Porto], Paulo Carvalho [Univ. Do Minho].

Several studies on the analysis of human mobility patterns have been carried out focusing on the identification and characterization of important locations in users' life in general. We extended these works by studying human mobility from the perspective of mobile data offloading. In our first study, We define Offloading Regions (ORs) as areas where a commuter's mobility would enable offloading, and propose an unsupervised learning method to extract ORs from mobility traces.

Next, we leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We evaluate the offloading opportunities (ORs) provided to users while they are travelling in terms of availability, time window to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed method. Moreover, results also show that (i) attending to users mobility, ten seconds is the minimum offloading time window that can be considered; (ii) offloading predictive methods can have variable performance according to the period of the day; and (iii) per-user opportunistic decision models can determine offloading system design and performance. This work was published at ACM CHANTS 2018 (jointly with ACM MobiCom)  46. Next we extended the above work as following.

We then assess the mobility predictability in an offloading scenario using theoretical and algorithmic evaluation of several mobility predictors. The results show that mobility predictability for offloading purposes is far more challenging than mobility between PoIs. Here, machine learning (ML) predictors outperform common Markov Chain (MC) predictors used in the literature by at least 15%, revealing the importance of context information in an offloading scenario. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets.

This last extended work is published at the IEEE Transactions on Network and Service Management  18.

This is an on-going work with the PhD Emanuel Lima, a collaboration started when he spent 4 months in 2018 as an intern in our previous team, and his advisors.

8.11 POPAyI: Muscling Ordinal Patterns for low-complex and usability-aware transportation mode detection [Axis 2]

Participants: Isadora Cardoso, Joao B. Borges, Aline Carneiro Viana, Antonio F. Loureiro, Heitor Ramos.

The comprehension of preferences related to mobility decisions of an urban population opens new perspectives to tackle the consequences of urbanization. Detecting transportation modes' usability in spatiotemporal urban trajectories enriches such mobility comprehension. With this goal, we introduce Popayi, a transportation mode detection strategy that bases its design on the Ordinal Pattern (OP) transformation applied to mobility-related time series.

Popayi can quantify time-series dynamics in linear time, muscling time series' characteristics that straightforward classification strategies can use. This new strength comes with a low-complex cost, avoiding the need for high computational and methodological complexities in the current Machine Learning (ML) and Deep Learning (DL) literature. Popayi uses polar geodesic representation and amplitude information in time series, bringing the multivariate capability to the standard uni-dimensional OP transformation.

Our experiments show that Popayi: (i) perfectly adapts to multi-dimensional mobility time series and individuals' natural non-linear mobility behavior. (ii) presents consistent detection results in any considered number of transportation mode's classes with efficiency in terms of storage and computation complexity, using fewer features than ML approaches and computational resources than DL methods. Indeed, Popayi presents classification results equivalent to DL approaches, requiring 10 to 1000 times fewer parameters. For instance, we can increase the F1-score by 2% using 1000 fewer parameters than a lightweight DL approach.

This work is currently under submission at the IEEE Transactions on Intelligent Transportation Systems.

8.12 Predicting Mobility with Small Data and Physical Principles[Axis 2]

Participants: Haron Calegari Fanticelli, Antonio Tadeu A. Gomes, Aline Carneiro Viana.

The study of human mobility is fundamental because of its impact on urban planning, the spread of diseases, the well-being of the population, and the mitigation of pollution, among other applications. Among the open challenges in the area, we have as one of the most important the interpretability and generality of the generated models, and the unbalanced volume of available data; several areas have little data available, making it impossible to use existing models.

We intend to face these challenges in a way not addressed before in the literature, which is with the use of mathematical models inspired by natural phenomena—normally modeled as differential equations—combined with established ML techniques to develop prediction models in the area of mobility. We intend with this combination to bring more interpretability to the models and reduce the need for large volumes of data.

This work is focused on the area of aggregate mobility prediction because of the data that we have to carry out this work. The available data describe the flow of people between administrative regions of Paris, France, with a sampling frequency of one hour and during fourteen days. More specifically, we intend to model the visitation routine of the people to predict the population density of areas in an instant of time, thus considering mobility and people's routine as a phenomenon to be modeled.

Our work mainly seeks to answer the following problem: Is it possible, with a good level of assertiveness, to model people's visitation routine through mathematical models combined with machine learning? Several works available in the literature show that the movements of people are typically characterized by routine behavior: daily cyclical movements (home to work), few places visited in their routine, and displacements that reveal preferred trajectories. The use of mathematical models to add domain knowledge of mobility as a phenomenon in ML techniques is new in the area and to advance this study, bringing more applicability to models, is a valuable knowledge gain for applications in urban planning or epidemiology.

We believe that, in addition to producing a generic interpretable model and requiring less training data to predict the number of people present in each of the study regions at an instant of time, our thesis will open up new opportunities for the development of mobility prediction models that consider other aspects, such as the trajectories that are expected to be taken by individuals or groups of individuals.

8.13 Revealing an inherently limiting factor in human mobility prediction: novelty-seeking mobility behavior [Axis 2]

Participants: Licia Amichi, Aline Carneiro Viana, Mark Corvella [Boston Univ.], Antonio F. Loureiro [Federal U. of Minas Gerais].

Predicting how humans move within space and time is a central topic in many scientific domains such as epidemic propagation, urban planning, and ride-sharing. However, current studies neglect individuals' preferences to explore and discover new areas. Yet, neglecting novelty-seeking activities at first glance appears to be inconsequential on the ability to understand and predict individuals' trajectories.

We claim and show the opposite in this work: exploration-like visits strongly impact mobility understanding and anticipation. Hence, we focus on such phenomenon, which has rarely been tackled in the literature but indeed, represents a real issue. We first work on the understanding of the exploration phenomenon and answer the following question: What type of visits characterize the mobility of individuals?. We show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon – Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Our results unveil important novelty preferences of people, which are ignored by literature prediction models. We then discuss how our profiling methodology could be leveraged to improve prediction.

This first work was published at the Student workshop of ACM CONEXT 2019 and in the French Algotel conference 2020 4. An extended version was then published at the ACM SigSpatial 2020  3. A journal under submission to the IEEE Transactions on Emerging Topics in Computing, where we evaluate the impacts of novelty-seeking and the prediction task formulation on the theoretical and practical predictability extents. A technical report on this last work is available at Hal Inria  41, and  42.

Next, we claim the tackling of such limitation first requires identifying the purpose of the next probable movement. In this context, we propose a novel framework for adjusting prediction resolution when probable explorations are going to happen. In fact, the geographical occurrences of explorations are far from being random in a coarser-grained spatial resolution. Exploiting these properties, instead of directly predicting a user's next location, we design a two-step predictive framework. First, we predict the mobility purpose of an individual: (i) a return, i.e., a visit to a previously known location, or (ii) an exploration, i.e., a discovery of a new place. Next, we predict the next location or the next coarse-grained zone depending on the inferred type of movement. The results demonstrate substantial improvements in the accuracy of prediction by dint of fruitfully forecasting coarse-grained zones used for exploration activities.

This work was published at the ACM SigSpatial 2021 1.

Finally, we evaluate the impacts of novelty-seeking, quality of the data, and the prediction task formulation on the theoretical and practical predictability extents. The results show the validity of our profiling and highlight the obstructive impacts of novelty-seeking activities on the predictability of human trajectories. In particular, in the next-place prediction task, from 40% to 90% of predicted locations are wrong, notably with Scouters.

This work was accepted for publication at the IEEE Transactions on Network Science and Engineering in Dec. 2022 13.

Licia Amichi did a PhD under the supervision of Aline C. Viana. She defended in November 2021 and the final version of her manuscript is on-line since Feb. 2022.

8.14 SafeCityMap: From spatiotemporal mobility of our society to the COVID propagation understanding. [Axis 2]

Participants: Aline Carneiro Viana, Artur Ziviani [LNCC], Haron Calegari Fanteceli [LNCC,Inria], Lucas Santos De Oliveira, Razvan Stanica [Inria], Solohaja Rabenjamina [Inria].

SafeCitymap is a data-driven project investigating how individuals' mobility patterns at a metropolitan scale were affected by the Covid-19 pandemic, and especially by the harsh French lockdown conditions enforced from March 17, 2020 to May 11, 2020 (i.e., two weeks before and during the first, second, and third lockdown). For this, we used spatiotemporal aggregated mobile phone data provided by SFR, a major SFR French telecom operator, covering a geographical region focused on the city of Paris. An essential property of this data is its fine-grained spatial resolution, which, to the best of our knowledge, is unique in the COVID-related mobility literature.

We perform a data-driven mobility investigation and modeling to quantify (in space and time) the population attendance and visiting flows in different urban areas. Second, when looking at periods both before and during the lockdown periods, we quantify the consequences of mobility restrictions and decisions on an urban scale. For this, per zone, we define a so-called signature, which captures behaviors in terms of population attendance in the corresponding geographical region (i.e., their land use) and allows us automatically detect activity, residential, and outlier areas. We then study three different types of graph centrality, quantifying the importance of each zone in a time-dependent weighted graph according to the habits in the mobility of the population. Combining the three centrality measures, we compute per zone of the city, its impact-factor, and employ it to quantify the global importance of zones according to the population mobility.

Our results firstly reveal the population's daily zone preferences in terms of attendance and mobility, with a high concentration on business and touristic zones. Second, results show that the lockdown mobility restrictions significantly reduced visitation and attendance patterns on zones, mainly in central Paris, and considerably changed the mobility habits of the population. As a side effect, most zones identified as mainly having activity-related population attendance in typical periods became residential-related zones during the lockdown, turning the entire city into a residential-like area. Shorter distance displacement restrictions imposed by the lockdown increased visitation to more “local” zones, i.e., close to the population's primary residence. Decentralization was also favored by the paths preferences of the still-moving population. On the other side, “jogging activities” allowing people to be outside their residences impacted parks visitation, increasing their visitation during the lockdown. By combining the impact factor and the signatures of the zones, we notice that areas with a higher impact factor are more likely to maintain regular land use during the lockdown.

Results of such investigations are periodically posted in the interactive webpage here: SafeCityMap website. This work is published at the ACM Transactions on Spatial Algorithms and Systems 15 and a extended report is available at  43.

Currently, we are investigating if the previously described mobility modeling can be used as proxies for the inference of pollution and noise indicators in a metropolitan city. While the polution and noise depend on physical deployed sensors, mobility datasets provide more precise spatiotemporal information of larger geographical areas. Preliminary investigations show a strong correlation between such indicators and SafeCityMap mobility modeling.

8.15 Edge Learning as a Hedonic Game in LoRaWAN

Participants: Kinda Khawam, Samer Lahoud, Cedric Adjih, Serge Makhoul, Rosy Al Tawil, Steven Martin.

Federated learning provides access to more data which is paramount for constrained LoRaWAN devices with limited memory storage. Learning on a larger data set will reduce the variance of the learned model, hence reducing its error. However, federating the learning process incurs a communication cost among learning devices that must be taken into account. In this paper, we formulate a Cooperative Hedonic game and introduce a new cost function that captures both the learning error and communication cost. LoRaWAN devices engage in the devised game by identifying if they should keep their learning local or federate with other devices in order to reduce both their learning error and communication cost. We compute the optimal size of formed coalitions and assess their stability. Then, we show through extensive simulations that devices have anincentive to form learning coalitions depending on the data characteristics at hand and the communication cost of LoRaWAN.

This article was accepted at the conference ICC 2023.

8.16 Privacy and security issues in collected wireless data [Axis 2]

When performing analytics from collected data related to smart devices, privacy issues come into play that can not be ignored. Dealing with such issues is essential to allow the leveraging of any extracted data knowledge in networking solutions. In this line of work, we investigate solutions allowing the design of privacy-compliant networking solutions that take profit from individuals' wireless data.

8.17 Quantum Learning Theory

Participants: Philippe Jacquet.

We consider a problem related to optical fiber communication where information is encoded in photon polarizations. We will show that the learning regret cannot decay faster than $1/\sqrt{T}$ where $T$ is the size of the training dataset, and that incremental gradient descent may converge worse.

This work has been presented in Maxnet 2022 and published in 25.

8.18 Energy Saving in Mobile Multi-Hop Sensor Networks

Participants: Bartlomiej Blaszczyszyn, Philippe Jacquet, Bernard Mans, Dalia Popescu.

This work has been published in 37 and 17.

8.19 Information-Theoretic Study of Covid Genome

Participants: Philippe Jacquet.

We have analyzed the genome sequence of covid 19 from an information point of view and we compare it with past and present genomes. We use the powerful tool of joint complexity in order to quantify the various potential parent genomes. Our finding does not find any significant match between the Covid genome and the genome of VIH. The apparent matches which gave rise to significant turmoil in the medical community seem to be merely accidental and unavoidable due to the large database of the sequenced variant of the VIH genomes.

The working paper has been published in 39.

8.20 Precise Minimax Regret in Logistic Regression

Participants: Philippe Jacquet, Gil Shamir, Wojciech Szpankowski.

This work has been presented at ISIT 2022 and published in 32.

8.21 Theoretical Study of Depth First Search in Random Digraph

Participants: Philippe Jacquet, Svante Janson.

This work has been presented in AofA 2022 and is published in 34.

8.22 TinyML and Edge AI

Participants: Cédric Adjih, Nadjib Achir, Emmanuel Baccelli, Anis Laouiti [IPP/Telecom SudParis], Wassim Seifeddine.

This year, work continued on the topic of TinyML and EdgeAI. In particular, we continued to work on the novel technique for embedded IoT systems that uses support from edge or cloud servers, and we propose a hierarchical execution model as shown in the following figure, and published in proposed in 11.

• "Embedded AI for Empty Places Recognition in Parking Lots", internship, Hong Jowell (with UTP and Telecom Sud-Paris)
• "Embedded AI for Drone Swarm Identification", internship, Nik Ahmad Syakir Nik Adib (with UTP and Telecom Sud-Paris)

Finally, a keynote was given on Machine Learning for IoT in Oct. 2022.

9.1 Bilateral grants with industry

10.1 International initiatives

10.2 International research visitors

10.3 European initiatives

10.4 National initiatives

10.5 Regional initiatives

11.1 Promoting scientific activities

11.2 Teaching - Supervision - Juries

11.3 Popularization

12.1 Major publications

• 1 inproceedingsL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A. a.Antonio a F Loureiro. From movement purpose to perceptive spatial mobility prediction.ACM SIGSPATIAL 2021Beijing, ChinaNovember 2021
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• 2 reportL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A.Antonio Loureiro. Impacts of novelty seeking on predictability in human mobility.InriaDecember 2021
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• 3 inproceedingsL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A. A.Antonio A F Loureiro. Understanding individuals' proclivity for novelty seeking.ACM SIGSPATIAL 2020 - 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsSeattle, Washington, United StatesNovember 2020
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• 4 inproceedings L.Licia Amichi, A. C.Aline Carneiro Viana, M.Mark Crovella and A. A.Antonio A. F Loureiro. Explorateur ou Routinier: Quel est votre profile de mobilité? ALGOTEL 2020 – 22èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications Lyon, France September 2020
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• 5 reportH. C.Haron C Fantecele, S.Solohaja Rabenjamina, A.Aline Carneiro Viana, R.Razvan Stanica and A.Artur Ziviani. SafeCityMap (1st phase) – COVID INRIA mission: Investigating population mobility habits in metropolitan zones and the lockdown impact using mobile phone data.InriaMay 2021
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• 6 articleP.Philippe Jacquet, D.Dimitris Milioris and W.Wojciech Szpankowski. Joint String Complexity for Markov Sources: Small Data Matters *.Discrete Mathematics and Theoretical Computer Science2020
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• 7 inproceedingsL.Loïc Jouans, A.Aline Carneiro Viana, N.Nadjib Achir and A.Anne Fladenmuller. Associating the Randomized Bluetooth MAC Addresses of a Device.Consumer Communications & Networking ConferenceLas Vegas, United StatesJanuary 2021
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• 8 reportP.Panagiota Katsikouli, D.Diego Madariaga, M.Marco Fiore, A.Aline Carneiro Viana and A.Alberto Tarable. A Novel Sensor-Free Location Sampling Mechanism.University of Copenhagen, Faculty of Science, Denmark; Inria Saclay; NIC Chile Research Labs, University of Chile; IMDEA Software Institute; CNR - IEIITFebruary 2021
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• 9 articleA. J.Anne Josiane Kouam, A.Aline Carneiro Viana and A.Alain Tchana. SIMBox bypass frauds in cellular networks: Strategies, evolution, detection, and future directions.Communications Surveys and Tutorials, IEEE Communications Society2021
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• 10 miscM.Masahiko Saito, M.Makoto Matsumoto, V.Vincent Roca and E.Emmanuel Baccelli. R. E.RFC Editor (https://www.rfc-editor.org/)TinyMT32 Pseudorandom Number Generator (PRNG) (RFC 8682).RFC 8682, Standards Track, TSVWG (Transport Area) working group of IETF (Internet Engineering Task Force), https://www.rfc-editor.org/rfc/rfc8682.htmlJanuary 2020
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• 11 inproceedingsW.Wassim Seifeddine, C.Cédric Adjih and N.Nadjib Achir. Dynamic Hierarchical Neural Network Offloading in IoT Edge Networks.PEMWN 2021 - 10th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired NetworksOttawa / Virtual, CanadaIEEENovember 2021, 1-6
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• 12 inproceedingsK.Koen Zandberg, E.Emmanuel Baccelli, S.Shenghao Yuan, F.Frédéric Besson and J.-P.Jean-Pierre Talpin. Femto-Containers: Lightweight Virtualization and Fault Isolation For Small Software Functions on Low-Power IoT Microcontrollers.Middleware 2022 - 23rd ACM/IFIP International Conference Middlewarequebec, CanadaNovember 2022, 1-12
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12.2 Publications of the year

12.3 Cited publications

• 41 techreportL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A.Antonio Loureiro. An inherent limiting factor of human mobility prediction.INSTITUT POLYTECHNIQUE DE PARIS ; INRIA Saclay, équipe TribeFebruary 2021
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• 42 techreportL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A.Antonio Loureiro. Impacts of novelty seeking on predictability in human mobility.InriaDecember 2021
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• 43 techreportH. C.Haron C Fantecele, S.Solohaja Rabenjamina, A.Aline Carneiro Viana, R.Razvan Stanica and A.Artur Ziviani. SafeCityMap (1st phase) -- COVID INRIA mission: Investigating population mobility habits in metropolitan zones and the lockdown impact using mobile phone data.InriaMay 2021
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• 44 inproceedingsP.Panagiota Katsikouli, A.Aline Carneiro, M.Marco Fiore and A.Alberto Tarable. On the Sampling Frequency of Human Mobility.IEEE GLOBECOM 2017Singapore, SingaporeDecember 2017, 1-6
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• 45 unpublishedA. J.Anne Josiane Kouam, A.Aline Carneiro Viana and A.Alain Tchana. SIMBox bypass frauds in cellular networks: Strategies, evolution, and detection survey.January 2021, working paper or preprint
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• 46 inproceedingsE.Emanuel Lima, A.Ana Aguiar, P.Paulo Carvalho and A.Aline Carneiro Viana. Impacts of Human Mobility in Mobile Data Offloading.ACM Chants 2018 - 13th Workshop on Challenged Networks4New Delhi, IndiaOctober 2018
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