The use of Unmanned Aerial Vehicles (UAVs) has increased significantly over the past decade. UAVs can be used for healthcare delivery, particularly for timecritical deliveries such as defibrillators and organs as well as for search and rescue operations. However, managing large fleets of UAVs in urban airspaces requires addressing key challenges including safety, collision avoidance, scalability, and coordination in dynamic environments. To this end, we propose MAS4D, a Multi-Agent Systems (MAS) based simulation framework in which UAVs operate in a three-dimensional (3D) environment. Our approach integrates the classical path planning algorithm ($\mathrm{A}^{*}$) with Multi-Agent Proximal Policy Optimization (MAPPO) to combine the reliability of deterministic planning with the adaptability of reinforcement learning. This integration is expected to enhance collision avoidance and improve scalability compared to traditional solutions. The proposed framework will be evaluated using metrics such as collision rate, path length, energy consumption, and robustness under increasing UAV density.

Mobile phone metadata is widely used to extract socio-economic activity metrics at scale. Properly utilized, this data provides unique insights into downstream tasks and business value. This paper uses large scale multi-region service-level mobile data traffic to investigates geospatial temporal clustering, aiming to cluster similar urban grids sharing the same social signature. Leveraging deep learning, time series clustering transcends morphology, achieving more accurate clustering in the hidden space. However, most current algorithms struggle to extract both temporal and spatial information. We propose a novel method, Deep Spatial Temporal Clustering (DSTC), for clustering fine-grained urban grids based on geospatial temporal characteristics of mobile traffic data. DSTC employs a lightweight spatiotemporal autoencoder and graph construction, optimizing clustering and reconstruction objectives to learn representations and cluster assignments. Using data from the NetMob 2023 Data Challenge, we demonstrate DSTC’s feasibility and interpretability through extensive case studies on two French metropolitan cities and three applications. This work advances geospatial temporal clustering and offers practical insights and business value from mobile phone metadata, reflecting real-world industrial scenarios. The source code for this paper is available at https://github.com/ComplexNetTSP/DSTCourGitHubrepository

The rapid adoption of electric vehicles (EVs) is driving increasing demand for efficient and strategically placed charging stations. While numerous studies have explored optimization methods for the placement of EV charging stations, most focus on smaller geographic areas, leaving the challenge of optimizing station distribution across larger regions unresolved. This paper presents a novel approach for optimizing both the placement and capacity of EV charging stations using the H3 spatial grid system and queuing theory. By leveraging the hexagonal structure of the H3 grid, we accurately model spatial data and analyze EV charging demands in both urban and non-urban areas. Queuing theory is employed to predict station utilization and optimize the allocation of charging points, minimizing user wait times and ensuring efficient resource distribution. The proposed method is adaptable to future growth in EV adoption and addresses infrastructure needs in both high-demand and underserved regions. This paper outlines the framework developed for the 13th SIGSPATIAL Cup (GISCUP 2024), which achieved top-5 performance. Results based on real-world data demonstrate the model's effectiveness in enhancing the spatial distribution of charging stations, improving accessibility and efficiency in EV infrastructure.

Communication-enabled devices routinely carried by individuals have become pervasive, opening unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. Our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. More importantly, it enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. By leveraging substantial real-world mobile network metadata and ground-truth information, we demonstrate that the accuracy of our solution is superior to that granted by state-of-the-art methods in practical heterogeneous urban scenarios.

With the rapid expansion of cities around the world, large number of movements are made daily as people commute from their homes to their destinations, including workplaces. From these movements, trends and patterns can be derived which in turn, can provide valuable insights for urban planning. This is particularly relevant in the 'smart cities' context. However, such movement data are often difficult to gather and analyse without infringing on privacy rights, especially with the increasing concerns on privacy issues. This paper reports on the use of aggregated mobile phone tracking data together with train network data to analyse movement patterns in, out, and within La Défense (Paris' business district). The findings can assist city planners by providing a better understanding of people's travel patterns.

Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the classification efficiency. A second support vector regression (SVR) based algorithm is built to make an online prediction of traffic load using the history of CDRs. Then, these algorithms will be integrated to a network planning tool which will help cellular operators on planning optimally their access network.

Smartphones and other mobile devices are today pervasive across the globe. As an interesting side effect of the surge in mobile communications, mobile network operators can now easily collect a wealth of high-resolution data on the habits of large user populations. The information extracted from mobile network traffic data is very relevant in the context of population mapping: it provides a tool for the automatic and live estimation of population densities, overcoming the limitations of traditional data sources such as censuses and surveys. In this paper, we propose a new approach to infer population densities at urban scales, based on aggregated mobile network traffic metadata. Our approach allows estimating both static and dynamic populations, achieves a significant improvement in terms of accuracy with respect to state-of-the-art solutions in the literature, and is validated on different city scenarios.

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper , an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.

This paper first defines a novel spatial-temporal coverage metric, k-depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd, a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and k-depth coverage objectives/ constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals-i.e., Goal. 1 near-maximal k-depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined k-depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher k-depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same k-depth coverage constraint.

In a smart grid environment, we study the coalition formation of prosumers that aim at entering the energy market. It is paramount for the grid operation that the energy producers are able to sustain the grid demand in terms of stability and a minimum production requirement. We design an algorithm that seeks to form coalitions that will meet both of these requirements: a minimum energy level for the coalitions and a steady production level, which leads to finding uncorrelated sources of energy to form a coalition. We propose an algorithm that uses graph tools such as correlation graphs or clique percolation to form coalitions that meet such complex constraints. We validate the algorithm against a random procedure and show that, it not only performs better in terms of social welfare for the power grid, but also that it is more robust against unforeseen production variations due to changing weather conditions for instance.

For several years, works on cognitive radio have been published allowing to reuse TV Whitespaces. On another hand, many new results appear on user and crowd mobility due to big data recent developments, especially from cellular traces. We propose here a method for cellular resource planning taking into account user mobility. Since users move, the bandwidth resource should also move accordingly. We design a score based method using TV Whitespace, and user experience, to determine from which cell it should be removed and to which one it should be added. Combined with traffic history it calculates scores for each cell. Bandwidth is reallocated on a half day basis. Before that, real traces of cellular networks in urban districts are presented which confirm that static network planning is no longer optimal. A dynamic femtocell architecture is then presented. It is based on mesh interconnected elements and designed to serve the score based bandwidth allocation algorithm. The score method along with the architecture are simulated and results are presented. They confirm the expected improvement in bandwidth and delay per user while maintaining a low operation cost at the operator side.

This paper proposes a novel task allocation framework, CrowdTasker, for mobile crowdsensing. CrowdTasker operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model, and aims to maximize the coverage quality of the sensing task while satisfying the incentive budget constraint. In order to achieve this goal, CrowdTasker first predicts the call and mobility of mobile users based on their historical records. With a flexible incentive model and the prediction results, CrowdTasker then selects a set of users in each sensing cycle for PCS task participation, so that the resulting solution achieves near-maximal coverage quality without exceeding incentive budget. We evaluated CrowdTasker extensively using a large-scale real-world dataset and the results show that CrowdTasker significantly outperformed three baseline approaches by achieving 3%-60% higher coverage quality.

Location prediction based on cellular network traces has recently spurred lots of attention. However, predicting user mobility remains a very challenging task due to the fuzziness of human mobility patterns. Our preliminary study included in this paper shows that there is a strong correlation between the calling patterns and co-cell patterns of users (i.e., co-occurrence in the same cell tower at the same time). Based on this finding, we propose NextCell—a novel algorithm that aims to enhance the location prediction by harnessing the social interplay revealed in cellular call records. Moreover, our proposal removes the assumption held in previous schemes that binds locations of cell towers to concrete physical coordinates, e.g., GPS coordinates. We validate our approach with the MIT Reality Mining dataset that involves 32,579 symbolic cell tower locations and 350,000 hours of continuous activity information. Experimental results show that NextCell achieves higher precision and recall than the state-of-the-art schemes at cell tower level in the forthcoming one to six hours.

We study the problem of information dissemination in vehicular networks in this paper. Crucial information, such as traffic jamming status, is usually broadcasted inside the network. Each vehicle in the network has its own choice to either forward or drop packets for others. Although the energy consumption in general is not an issue in vehicular networks, forwarding every received packets is still not preferred as it may result in congestion in transmission. We model this problem via evolutionary game theory to investigate the cooperative behavior among vehicles. Simulation results show that, the cooperation ratio in the network is proportional to the setting of synergy factor for the evolutionary game. When nodes change their strategies according to the evolutionary game, information can be disseminated as fast as flooding scheme, while more bandwidth can be reserved.

Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, we propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. We attest that the tensor-based technique is effective for temporal link prediction applied to the intermittently connected networks. The validity of this method is proved when the prediction is made in a distributed way (i.e. with local information) and its performance is compared to well-known link prediction metrics proposed in the literature.

Cooperative geographic routing has emerged as an efficient, scalable routing paradigm that exploits the joint merit of cooperative diversity and position-aided localized operation to enhance the network performance. In terms of the hop efficiency, an existing scheme called Relay-Aware Cooperative Routing (RACR) has offered a cross-layer approach based on its quantitative study of how physical-layer cooperation leads to link-layer radio coverage extension, under the policy of equal power allocation (EPA) over the cooperating nodes. However, the effect of cooperation-based radio coverage extension dramatically varies with the power allocation policy in use. In this paper, we extend the RACR scheme by addressing a power-optimized cooperative geographic routing problem. With the joint optimization of relaying position and power allocation, the maximum cooperative transmission range is derived. We then, according to the result, present a modified cross-layer routing protocol taking into account the effectuation of optimal power allocation (OPA). Simulation results show that the proposed scheme effectively improves the coverage extension performance as well as reduces the average number of hops.

Several works have outlined the fact that the mobility in intermittently connected wireless networks is strongly governed by human behaviors as they are basically human-centered. It has been shown that the users' moves can be correlated and that the social ties shared by the users highly impact their mobility patterns and hence the network structure. Tracking these correlations and measuring the strength of social ties have led us to propose an efficient distributed tensor-based link prediction technique. In fact, we are convinced that the feedback provided by such a prediction mechanism can enhance communication protocols such as opportunistic routing protocols. In this paper, we aim to bring out that measuring the stabilities of the link and the proximity at two hops can improve the efficiency of the proposed link prediction technique. To quantify these two parameters, we propose an entropy estimator in order to measure the two stability aspects over successive time periods. Then, we join these entropy estimations to the tensor-based link prediction framework by designing new prediction metrics. To assess the contribution of these entropy estimations in the enhancement of tensor-based link prediction efficiency, we perform prediction on two real traces. Our simulation results show that by exploiting the information corresponding to the link stability and/or to the proximity stability, the performance of the tensor-based link prediction technique is improved. Moreover, the results attest that our proposal's ability to outperform other well-known prediction metrics.

It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve wide network characteristics. Studies have shown that Small-World properties, primarily low average path length (APL) and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small-world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small-world properties show a degree of distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the APL and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using the Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on APL, clustering coefficient and connectivity.

Cooperative communication for wireless networks has been extensively investigated from the perspective of physical-layer design. However, the impact of physical-layer cooperation on the network-layer routing design still remains unclear. In this paper, we examine the potential benefit of radio coverage extension from cooperation, and present how to incorporate this feature into the routing design. Specifically, with a series of mathematical formulations and derivations, we quantitatively identify direct and cooperative radio coverages based on the average symbol error rate (SER) performance requirement, elucidating how cooperative diversity gain can be translated into radio coverage extension. We then propose a cooperative geographic routing protocol with cross-layer design, namely the Relay-Aware Cooperative Routing (RACR) protocol, that exploits the merit of radio coverage extension for improving the non-cooperative geographic routing. Simulation results show that the RACR protocol performs significantly better than the non-cooperative geographic routing in terms of the average path length in dense ad hoc networks.

Many studies have tried to evaluate wireless networks and especially the IEEE 802.15.4 standard. Hence, several papers have aimed to describe the functionalities of the physical (PHY) and medium access control (MAC) layers. They have highlighted some characteristics with experimental results and/or have attempted to reproduce them using theoretical models. In this paper, we use the first way to better understand IEEE 802.15.4 standard. Indeed, we provide a comprehensive model, able more faithfully to mimic the functionalities of this standard at the PHY and MAC layers. We propose a combination of two relevant models for the two layers. The PHY layer behavior is reproduced by a mathematical framework, which is based on radio and channel models, in order to quantify link reliability. On the other hand, the MAC layer is mimed by an enhanced Markov chain. The results show the pertinence of our approach compared to the model based on a Markov chain for IEEE 802.15.4 MAC layer. This contribution allows us fully and more precisely to estimate the network performance with different network sizes, as well as different metrics such as node reliability and delay. Our contribution enables us to catch possible failures at both layers.

Cooperative relaying has been proposed as a promising transmission technique that effectively creates spatial diversity through cooperation among spatially distributed nodes. However, to achieve efficient communications while gaining full benefits from cooperation, more interactions at higher protocol layers, particularly the MAC (Medium Access Control) and network layers, are vitally required. This is ignored in most existing articles that mainly focus on physical (PHY)-layer relaying techniques. In this paper, we propose a novel cross-layer framework involving two levels of joint design-a MAC-network cross-layer design for forwarder selection (or termed routing) and a MAC-PHY for relay selection-over symbol-wise varying channels. Based on location knowledge and contention processes, the proposed cross-layer protocol, CoopGeo, aims at providing an efficient, distributed approach to select next hops and optimal relays to form a communication path. Simulation results demonstrate that CoopGeo not only operates properly with varying densities of nodes, but performs significantly better than the existing protocol BOSS in terms of the packet error rate, transmission error probability, and saturated throughput.

In this paper, we propose a Hybrid Multi-Channel Multi-hop Medium Access Control (HMM-MAC) meeting the safety and non-safety requirements in Vehicular Ad-hoc Networks when no infrastructure is present (V2V communication) with only a single transceiver at each vehicle. The simulation results showed that our scheme could achieve a higher delivery ratio of road safety messages and a high probability to select a free transmission channel in a certain region and during a certain time.

Most of geographic routing approaches in wireless ad hoc and sensor networks do not take into consideration the medium access control (MAC) and physical layers when designing a routing protocol. In this paper, we focus on a cross-layer framework design that exploits the synergies between network, MAC, and physical layers. In the proposed CoopGeo, we use a beaconless forwarding scheme where the next hop is selected through a contention process based on the geographic position of nodes. We optimize this Network-MAC layer interaction using a cooperative relaying technique with a relay selection scheme also based on geographic information in order to improve the system performance in terms of reliability.