Le Centre pour la Communication Scientifique Directe - HAL - memSIC, Dec 29, 2021
Predicting how humans move within space and time is a central topic in many scientific domains su... more 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 places. Yet, neglecting novelty-seeking activities at first glance appears to be inconsequential on the ability to understand and predict individuals' trajectories. In this work, we claim and show the opposite: exploration moments strongly impact mobility understanding and anticipation. We start by proposing a new approach to identifying moments of novelty-seeking. Based on that, we construct individuals' mobility profiles using their exploration inclinations-Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., an individual with no extreme behavior). Finally, we evaluate the impacts of noveltyseeking, 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, on Scouters.
INSTITUT POLYTECHNIQUE DE PARIS ; INRIA Saclay, équipe Tribe, Feb 3, 2021
Predicting how we humans move within space and time is becoming a central topic in many scientifi... more Predicting how we humans move within space and time is becoming a central topic in many scientific domains, ranging from epidemic propagation, urban planning to ride-sharing. However, current works neglect individuals' preferences for exploration and discovery of new places. Yet, noveltyseeking activities appear to have significant consequences on the ability to understand and predict individuals' trajectories. In this work, we propose a new approach for the identification of moments of novelty-seeking. Subsequently, we construct individuals' mobility profiles based on their exploration inclinations-Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior).
Explorateur ou Routinier: Quel est votre profile de mobilité?
La prediction de la mobilite individuelle et sa dynamique au-dela de l'etude du comportement ... more La prediction de la mobilite individuelle et sa dynamique au-dela de l'etude du comportement humain et la sociologie capturent l'attention de nombreux autres communautes scientifiques (Reseaux, Physique ou Data Mining) et possede plusieurs domaines d'application : e.g. la propagation d'epidemie, l'amenagement urbain, les systemes de recomman-dations. Les modeles de prediction actuels sont cependant incapables de capturer les incertitudes lieesa la complexite de la prise de decision et au comportement humain, et par consequent, souffrent de l'incapacite de predire les visites a de nouveaux endroits. Dans cet article, nous nous interessonsa l'aspect exploratoire du comportement humain et introduisons une nouvelle strategie qui permet d'identifier les profils de mobilite des individus. Notre strategie capture les proprietes spatiotemporelles des visites-i.e. une visitea un nouvel endroit ou un retour vers une place connue (spatial) maisegalement la recur...
Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies, Dec 9, 2019
The prediction of individuals' dynamics has attracted significant community attention and has imp... more The prediction of individuals' dynamics has attracted significant community attention and has implication for many fields: e.g. epidemic spreading, urban planning, recommendation systems. Current prediction models, however, are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior. This paper contributes better understanding of this aspect and presents a new strategy for identifying exploration profiles of a population. Our strategy captures spatiotemporal properties of visits-i.e. a known or new location (spatial) as well as a recurrent and intermittent visit (temporal)-and classifies individuals as scouters (i.e., extreme explorers), routineers (i.e., extreme returners), or regulars (i.e., with a medium behavior). To the best of our knowledge, this is the first work profiling spatiotemporal exploration of individuals in a simple and easy-to-implement way, with the potential to benefit services relying on mobility prediction.
ICC 2019 - 2019 IEEE International Conference on Communications (ICC), May 1, 2019
Low-Power Wide-Area Network (LPWAN) based on LoRa physical layer is envisioned as one of the most... more Low-Power Wide-Area Network (LPWAN) based on LoRa physical layer is envisioned as one of the most promising technologies to support future Internet of Things (IoT) systems. LoRa provides flexible adaptations of coverage and data rates by allocating different Spreading Factors (SFs) to end-devices. Although most works so far had considered perfect orthogonality among SFs, the harmful effects of inter-SF interferences have been demonstrated recently. Therefore in this work, we consider the problem of SF allocation optimization under co-SF and inter-SF interferences, for uplink transmissions from end-devices to the gateway. To provide fairness, we formulate the problem as maximizing the minimum achievable average rate in LoRa, and propose a SF allocation algorithm based on matching theory. Numerical results show that our proposed algorithm enables to jointly enhance the minimal user rates, network throughput and fairness, compared to baseline SF allocation methods.
The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technolo... more The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technologies for future Internet of Things (IoT) applications. It provides a flexible adaptation of coverage and data rate by allocating different Spreading Factors (SFs) and transmit powers to end-devices. We focus on improving throughput fairness while reducing energy consumption. Whereas most existing methods assume perfect SF orthogonality and ignore the harmful effects of inter-SF interferences, we formulate a joint SF and power allocation problem to maximize the minimum uplink throughput of end-devices, subject to co-SF and inter-SF interferences, and power constraints. This results into a mixed-integer non-linear optimization, which, for tractability, is split into two sub-problems: firstly, the SF assignment for fixed transmit powers, and secondly, the power allocation given the previously obtained assignment solution. For the first sub-problem, we propose a low-complexity many-to-one matching algorithm between SFs and end-devices. For the second one, given its intractability, we transform it using two types of constraints' approximation: a linearized and a quadratic version. Our performance evaluation demonstrates that the proposed joint SF allocation and power optimization enables to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that it outperforms baseline schemes. 1 DRAFT 2
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