Extended reality (XR) applications are characterised by their unique multi-modal traffic pattern. However, XR user equipment (UE) presents significant battery-related challenges. Meanwhile, the discontinuous reception (DRX) mechanism was designed and standardised to extend the UE battery life on cellular networks. The DRX mechanism is based on a number of parameters, and studies focused on modelling its behaviour targeted diverse types of traffic on UMTS, LTE, and 5G NR networks. Some of these studies target generating analytical DRX models for specific traffic patterns, while others optimise the DRX mechanism with predictive models for specific applications. With evolving application types on the network, such as XR, there is a need to expand on these previous works to prepare current and future networks for the demands of new-age applications. In this paper, we examine prediction-based DRX optimisation tailored for XR applications. We design a dynamic DRX optimisation algorithm that utilises predictions of traffic inter-arrival times to select dynamic DRX parameters with two performance metrics: energy efficiency and delay. Using publicly available XR data, we utilised state-of-the-art forecasting tools such as Long Short Term Memory and Gaussian Processes models to design our dynamic DRX solution, and we report an improvement over the energy efficiency obtained by the standard DRX mechanism with minimal extra-delay.

Instant messaging represents the most popular digital service worldwide, and WhatsApp is the most used app of this kind. By studying WhatsApp traffic, we can gain major insight into both human behavior and network infrastructure needs. This work presents a unique analysis of mobile networks using WhatsApp uplink traffic. We apply information theory metrics such as Shannon Permutation Entropy, Statistical Complexity, and the Causality Complexity-Entropy Plane to understand specific network patterns, usage behaviors, and areas where users are more likely to engage in online conversations. We also demonstrate how these metrics can be used as features for machine learning techniques such as the K-means algorithm, showing they can be used to identify regions with similar patterns in the WhatsApp network traffic.

Voice call services remain the fundamental use case for mobile phones, even as modern smartphones have grown into sophisticated computing platforms. While prior work has addressed the general power behavior of mobile devices, little attention has been paid to the specific energy footprint of voice services at a component level. In this paper, we address this gap by performing fine-grained energy measurements, isolating the power rail of the radio frequency (RF) module within commercial smartphones. Our analysis compares total device and RF-only energy consumption across multiple voice call technologies under controlled conditions. We demonstrate that the accuracy of measuring energy consumption in voice call technology, when based only on the total device consumption, can distort conclusions and lead to incorrect analyses.

With more mobile devices in the world than the global population and with growing environmental concerns, the energy consumption of mobile devices is becoming an important topic. At the same time, video streaming represents the most popular service on the Internet in terms of traffic volume, and video content is increasingly consumed by users on mobile devices. Therefore, in this study, we design and conduct an experimental campaign to measure the energy consumption of mobile devices and their wireless network modules in the context of video streaming services. We compare the energy consumption for five video streaming platforms over multiple radio access technologies (WiFi, 3G, 4G, and 5G), using commercial networks as well as a private cellular network set-up. Our results show a significant variation in terms of energy consumption between video platforms, with very different behaviors appearing at the inspection of the collected traces. We also show that the 5G technology represents, at least for now, the most energy-hungry solution at the user level. Finally, we test the different video quality options available for the different platforms, showing once again a very heterogeneous behavior in terms of power consumption patterns.

Thanks to its ability to provide secure and efficient communication capabilities, network slicing has been adopted as a key technology, not just in commercial networks but also in tactical networks. Based on multiple virtual networks, each dedicated to a specific service, a sliced architecture meets the diverse requirements of highly heterogeneous tactical services. However, tactical networks operate in challenging environments with inherent power constraints, where the need for energy-efficient network slicing management solutions is paramount. In this direction, we tackle a joint slice activation/deactivation and user association problem with the aim of studying trade-offs between energy efficiency and user quality of service. To solve the problem, we introduce our original approach: a split reinforcement learning-based energy-efficient slicing deployment algorithm, namely SNOW. SNOW divides the deep neural network into multiple sections, where the front-end part of the model is trained over multiple user devices and then the back-end part of the model is trained by the central nodes (i.e. base stations in this case), without sensitive data sharing. Extensive simulation results reveal that the proposed scheme is superior to the considered benchmarks in improving energy efficiency while maintaining network performance.

The evolution of mobile networks towards more diverse services and open architectures has led to the emergence of mobile network function virtualization. This allows to match the reserved resources for network operation to the actual resources that are needed in the network at a certain place and time. Mobility-related network functions, specifically handover, can also be virtualized in this new paradigm. This virtualization can be facilitated by studying handover behavior at the base station level. In this work, using agglomerative hierarchical clustering, we show the existence of different base station profiles in terms of handovers, including three primary profiles: producer, receiver, and balanced. We also show that the use of these profiles, in addition to the dynamic reconfiguration enabled by virtualization, can reduce reserved resources by more than 50% compared to the current static system.

Network slicing is one of the major catalysts proposed to turn future telecommunication networks into versatile service platforms. Along with its benefits, network slicing is introducing new challenges in the development of sustainable network operations, as it entails a higher energy consumption compared to non-sliced networks.Using a sliced architecture, which includes guaranteeing the communication and computation requirements for each slice, is essential for operators to provide a satisfying user quality of service (QoS) in a multi-service network. At the same time, building sustainable mobile networks, with the least amount of resources used, is crucial today, for both economic and environmental reasons. As a result, mobile operators need to find a middle ground between these two objectives – a tough nut considering they are both antithetical and important. In this light, we investigate a joint slice activation/deactivation and user association problem, with the aim of minimizing energy consumption and maximizing the QoS. The proposed multI-agent fully CooperativE deCentRalizEd frAMework (ICE-CREAM) addresses the formulated joint problem, with agents acting at two different granularity levels. Not only all the agents can access the shared information with their direct neighbors, but also they are trained with one global reward, which is an ideal approach in multi-agent cooperative settings. We evaluate ICE-CREAM using a real-world dataset that captures the spatio-temporal consumption of three different mobile services in France. Experimental results demonstrate that the proposed solution provides more than 30% energy efficiency improvement compared to a configuration where all the slice instances are always active while maintaining the same level of QoS. From a broader perspective, our work explicitly shows the impact of prioritizing the energy over QoS, and vice versa.

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from São Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80% compared to an exact method, while achieving optimal solutions.

Cell switch off (CSO) is a technique in the mobile networks sphere that has been proposed and implemented widely to achieve reduced energy consumption of base station (BS) deployments. The energy consumption of BSs has increasingly become an interesting research area and rightly so, given the advancement in modern networking technology and the increase in network resources demand. The aim of this work is to examine CSO closely by evaluating it over a real urban area in Europe and using real data from the mobile network deployment of a national European mobile network operator. Our aim is to quantify the energy impact of applying CSO techniques to real BS network deployments. Through a detailed modelling of the BS power consumption, we show that a total of 18 MW can be saved over the studied urban area in 24 hours within the purview of the defined quality of service constraints.

Network slicing is one of the major catalysts to turn future telecommunication networks into versatile service platforms. Along with its benefits, network slicing is introducing new challenges in the development of sustainable network operations. In fact, guaranteeing slices requirements comes at the cost of additional energy consumption, in comparison to non-sliced networks. Yet, one of the main goals of operators is to offer the diverse 5G and beyond services, while ensuring energy efficiency. To this end, we study the problem of slice activation/deactivation, with the objective of minimizing energy consumption and maximizing the users quality of service (QoS). To solve the problem, we rely on two Multi-Armed Bandit (MAB) agents to derive decisions at individual base stations. Our evaluations are conducted using a real-world traffic dataset collected over an operational network in a medium size French city. Numerical results reveal that our proposed solutions provide approximately 11-14% energy efficiency improvement compared to a configuration where all the slice instances are active, while maintaining the same level of QoS. Moreover, our work explicitly shows the impact of prioritizing the energy over QoS, and vice versa.

An emergency communication system is necessary for first responders, who need to enter areas with no network coverage or damaged network infrastructure due to natural or man-made disasters to perform emergency tasks. The first responders usually rely on fixed base stations deployed according to the needs of the mission, to meet the communication needs. However, fixed base stations can pose several limitations, typically for supporting the mobility of groups of users. Thanks to the development of software defined radio and software defined networks, embedding the functions of a base station on a movable platform, such as a vehicle or a drone, is now a possible alternative. The base station carried by the movable platform can react to changes in the network in real time, allowing more flexibility and introducing a new degree of freedom for the emergency communication network. In this work, we present a quantitative network performance comparison between a fixed base station and a movable base station, when a group of first responders is moving in a geographical area for an emergency rescue mission. We show that a movable base station provides up to 4 times the throughput of a fixed base station.

Long-Range Wide Area Networks (LoRaWAN) enable low-power data collection over long distances, and they are thus widely used for Internet of Things applications, despite their limitations to meet some traffic requirements (e.g., reliability). To achieve the quality of service required by the applications, service differentiation is a promising approach which can be provided by network slicing. In this work, we show that related works are either incompatible with the LoRaWAN specifications, or do not isolate traffic, or assume an a priori known and stable traffic. In this paper, we propose a lightweight approach to achieve slicing in LoRaWAN, compatible with the LoRaWAN specifications. Our approach allows to isolate traffic, to protect confirmed traffic, and to deal with unsolicited traffic.

Sixth-generation and mobile edge computing (MEC) systems are expected to empower a wide range of applications. Unmanned aerial vehicles (UAVs) can play a vital role in improving network connectivity. Hence, our problem is to minimize the user equipment (UE) energy consumption during task offloading in a UAV assisted MEC system. To address the formulated NP-hard problem, we propose task scheduling and assignment algorithms for mapping UE tasks to fixed edge servers using UAV. Lastly, the simulation results demonstrate that the proposed algorithms yield better results than other benchmark methods in terms of total UE energy consumption.

Next-generation mobile networks are expected to meet the requirements of a wide range of new vertical services. Hence, the network slicing concept has been introduced, in which Mobile Virtual Network Operators (MVNOs) are allowed to provide various types of services over the same physical infrastructure, owned by an Infrastructure Provider (InP). To cope with an ever-changing traffic demand, MVNOs seek to pre-allocate/reconfigure the resources at the base stations in an anticipatory manner, based on traffic demand predictions. Ideally, conducting per-slice traffic forecasting requires information that is likely to disclose MVNO confidential information (i.e., business strategy or private user data). To secure data ownership while conducting traffic forecasting, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station; subsequently, an associated InP global model can be updated through the aggregation of the local models. The results obtained by training the models on a real-world dataset indicate that the forecasting performance of our proposed approach is as accurate as state-of-the-art centralized solutions, while improving data privacy. To enable scalability, we further propose the Information-based Clustering FPLSTM (IC-FPLSTM) and Random Clustering FPLSTM (RC-FPLSTM) frameworks, dealing with large-scale cellular networks. These solutions demonstrate computation and communication cost efficiency significantly above the state-of-the-art.

In this work, taking the perspective of Mobile Virtual Network Operators (MVNOs), we tackle the multi-slice traffic forecasting problem, while respecting the data privacy of users. To this end, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train at each base station their local models with their private datasets, without compromising data privacy. Prediction results obtained by evaluating the models on a real-world dataset indicate that the forecast of FPLSTM is as accurate as state-of-the-art solutions while ensuring data privacy as well as computation and communication costs efficiency.

Data collected from mobile phones or from motion detection sensors are regularly used as a proxy for user presence in networking studies. However, little attention was paid to the actual accuracy of these data sources, which present certain biases, in capturing actual human presence in a given geographical area. In this work, we conduct the first comparison between mobile phone data collected by an operator and human presence data collected by motion detection sensors in the same geographical area. Through a detailed spatio-temporal analysis, we show that a significant correlation exists between the two datasets, which can be seen as a cross validation of the two data sources. However, we also detect some significant differences at certain times and places, raising questions regarding the data used in certain studies in the literature. For example, we notice that the most important daily mobility peaks detected in mobile phone data are not actually detected by on ground sensors, or that the end of the work-day activities in the considered area is not synchronised between the two data sources. Our results allow to distinguish the metrics and the scenarios where user presence information is confirmed by both mobile phone and sensor data.

Network slicing is one of the cornerstones for next-generation mobile communication systems. Specifically, it enables Mobile Virtual Network Operators (MVNOs) to offer various types of services over the same physical infrastructure owned by an Infrastructure Provider (InP). To satisfy the dynamic user requirements and ensure resource efficiency, MVNOs need to estimate the future traffic demand in advance, to pre-allocate/reconfigure the resources at the base stations. However, this per-slice traffic forecasting exploits information that is clearly sensitive for the MVNOs from a business point of view, and which might even disclose private data regarding some users. Hence, it is vital for MVNOs to ensure data privacy while conducting traffic forecasting. Bearing this in mind, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station without compromising data privacy. Simultaneously, an InP global model is updated through the aggregation of local models weights. Prediction results obtained by training the models on a real-world dataset indicate that the forecasting performance of FPLSTM is as accurate as state-of-the-art solutions, while ensuring data privacy, computation and communication cost efficiency.

Self-deployable mobile networks represent a new type of cellular networks, that can be rapidly deployed, easily installed, and operated on demand, anywhere, anytime. They can provide network services when a classical cellular network fails, is not suitable, or does not exist. Using network virtualization techniques, core network and base station functions can be co-located together into a single equipment. This brings the network functions closer to the user, but the placement of the local core network has a significant impact on the network performance, mainly in terms of capacity and delay. In this work, we are focused on the delay minimization from BSs to the local core network. We propose a heuristic for the local core network placement, with the objective of reducing the delay. We show that the delay obtained by our solution decreases significantly compared to a strategy that places the local core network in order to maximize the capacity. We also show that considering a capacity-based placement strategy leads to infinite delay in some cases.

While current medium access control solutions in low-power wide area networks are generally based on Aloha, recent studies demonstrated the interest of adding carrier sense mechanisms to the picture. In this paper, we investigate the impact of the carrier sense threshold parameter in this particular context. We show that its impact on the average behavior of the network is limited, but this changes when looking at the individual node performance. Our simulation results demonstrate an important heterogeneity among nodes, both in terms of packet success probability and of energy consumption. Moreover, the performance of the nodes is strongly correlated with the percentage of contending nodes that they can sense. By simply using two different carrier sense thresholds in the network, we achieve an increased fairness among nodes.

In this work, we propose a robust planning tool that allocates power statically in homogeneous and heterogeneous cellular networks with non-regular base station (BTS) placement, to mitigate interference and improve overall performance. Each BTS will use the total available spectrum, but it will divide it into multiple sub-bands, and each BTS will transmit with a specific pre-computed power on each sub-band. We refer to such a power allocation as a power map. Our offline planning tool computes a robust power map for a given topology, by solving a non-convex, non-linear optimization problem, through simple transformations, based on geometric programming. The power map is computed based solely on the network topology, and it is made available to all BTSs that use it throughout the network operation to perform scheduling using a fast quasi-optimal online algorithm that we propose. We evaluate our planning tool for different homogeneous and heterogeneous networks (HetNets), first in a static setting where scheduling is performed optimally and then in a dynamic setting when scheduling is performed with our online scheduler. Results show that our solution significantly outperforms a classical equal power/fixed frequency reuse scheme in terms of sum-rate, by up to 30% in homogeneous networks and by up to 70% in HetNets.

An increased density of access points is common today in WiFi deployments, and more and more parameters need to be configured in such networks. In this paper, we question current industrial guidelines for both residential and enterprise scenarios. More precisely, we investigate the joint channel, power, and carrier sense threshold allocation problem in IEEE 802.11ac networks, showing that the current practice, which is to use narrower channels at maximum power when the deployment is dense, yields much worse performance than a solution using the widest possible channel with a much lower power.

The use of LED-to-camera communication opens the door to a wide range of use cases and applications, with diverse requirements in terms of quality of service. However, while analytical models and simulation tools exist for all the major radio communication technologies, the only way of currently evaluating the performance of a network mechanism over LED-to-camera is to implement and test it. Our work aims to fill this gap by proposing a Markov-modulated Bernoulli process to model the wireless channel in LED-to-camera communications, which is shown to closely match experimental results. Based on this model, we develop and validate CamComSim, the first network simulator for LED-to-camera communications.

A novel trend in mobile networks is to co-locate the base stations with virtualized core network functions, such as session management and routing. The goal is to lose the long-standing physical dependency between the radio access and the core network, and improve network resiliency. In this work, we focus on the placement of virtualized core functions within a network of multiple base stations interconnected via a potentially limited backhaul. Since all data and signaling traffic are exchanged on the links interconnecting the base stations, the placement of these functions deeply impacts the backhaul load. We compare centralized and distributed placement strategies, with respect to the overall backhaul bandwidth consumption. Results show that distributing instances of the core functions (e.g., routing) in the network is significantly less costly from a backhaul point of view, and can economize backhaul consumption by 86%.

Human mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories.

The development of virtualization techniques enables an architectural shift in mobile networks, where resource allocation, or even signal processing, become software functions hosted in a data center. The centralization of computing resources and the dynamic mapping between baseband processing units (BBUs) and remote antennas (RRHs) provide an increased flexibility to mobile operators, with important reductions of operational costs. Most research efforts on Cloud Radio Access Networks (CRAN) consider indeed an operator perspective and network-side performance indicators. The impact of such new paradigms on user experience has been instead overlooked. In this paper, we shift the viewpoint, and show that the dynamic assignment of computing resources enabled by CRAN generates a new class of mobile terminal handover that can impair user quality of service. We then propose an algorithm that mitigates the problem, by optimizing the mapping between BBUs and RRHs on a time-varying graph representation of the system. Furthermore, we show that a practical online BBU-RRH mapping algorithm achieves results similar to an oracle-based scheme with perfect knowledge of future traffic demand. We test our algorithms with two large-scale real-world datasets, where the total number of handovers, compared with the current architectures, is reduced by more than 20%. Moreover, if a small tolerance to dropped calls is allowed, 30% less handovers can be obtained.

Mobile traffic data collected by network operators is a rich source of information about human habits, and its analysis provides insights relevant to many fields, including urbanism, transportation, sociology and networking. In this paper, we present an original approach to infer both spatial and temporal structures hidden in the mobile demand, via a first-time tailoring of Exploratory Factor Analysis (EFA) techniques to the context of mobile traffic datasets. Casting our approach to the time or space dimensions of such datasets allows solving different problems in mobile traffic analysis, i.e., network activity profiling and land use detection, respectively. Tests with real-world mobile traffic datasets show that, in both its variants above, the proposed approach (i) yields results whose quality matches or exceeds that of state-of-the-art solutions, and (ii) provides additional joint spatiotemporal knowledge that is critical to result interpretation.

An isolated base station is a base station having no connection to a traditional core network. To provide services to users, an isolated base station is colocated with an entity providing the same functionalities as the traditional core network, referred to as Local EPC. In order to cover wider areas, several base stations are interconnected, forming a network that should be served by a single Local EPC. In this work, we tackle the Local EPC placement problem in the network, to determine with which of the base stations the Local EPC must be co-located. We propose a novel centrality metric, flow centrality, which measures the capacity of a node to receive the total amount of flows in the network. We show that co-locating the Local EPC with the base station having the maximum flow centrality maximizes the total amount of traffic the Local EPC can receive from all base stations, under certain capacity and load distribution constraints. We compare the flow centrality to other state of the art centrality metrics, and emphasize its advantages.

Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in 10 cities. Results unveil the diversity of baseline communication activities across countries, but also provide evidence of the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses.

In the next few years, mobile networks will undergo significant evolutions in order to accommodate the ever-growing load generated by increasingly pervasive smartphones and connected objects. Among those evolutions, cognitive networking upholds a more dynamic management of network resources that adapts to the significant spatiotemporal fluctuations of the mobile demand. Cognitive networking techniques root in the capability of mining large amounts of mobile traffic data collected in the network, so as to understand the current resource utilization in an automated manner. In this paper, we take a first step towards cellular cognitive networks by proposing a framework that analyzes mobile operator data, builds profiles of the typical demand, and identifies unusual situations in network-wide usages. We evaluate our framework on two real-world mobile traffic datasets, and show how it extracts from these a limited number of meaningful mobile demand profiles. In addition, the proposed framework singles out a large number of outlying behaviors in both case studies, which are mapped to social events or technical issues in the network.

Energy harvesting capabilities are challenging our understanding of wireless sensor networks by adding recharging capacity to sensor nodes. This has a significant impact on the communication paradigm, as networking mechanisms can benefit from these potentially infinite renewable energy sources. In this work, we study the consequences of implementing photovoltaic energy harvesting on the duty cycle of a wireless sensor node, in both outdoor and indoor scenarios. We show that for the static duty cycle approach in outdoor scenarios, very high duty cycles, in the order of tens of percents, are achieved. This further eliminates the need for additional energy conservation schemes. In the indoor case, our analysis shows that the dynamic duty cycle approach based solely on the battery residual energy does not necessarily achieve better results than the static approach. We identify the main reasons behind this behavior, and test new design considerations by adding information on the battery level variation to the duty cycle computation. We demonstrate that this approach always outperforms static solutions when perfect knowledge of the harvestable energy is assumed, as well as in realistic deployments, where this information is not available.

Mobile traffic data has been recently used to characterize the urban environment in terms of urban fabric profiles. While showing promising results, the existing urban fabric detection solutions are built without a clear understanding of the detection process chain. In this paper, we distinguish and analyze the different steps common to all urban profiling techniques. By evaluating the impact of each step of the process, we are able to propose a new solution that outperforms the state of the art techniques. Our approach uses the weekly periodicity of human activities, as well as a median-based filtering technique, resulting in a better clustering in terms of both coverage and entropy, as shown by results obtained on two large scale mobile traffic datasets covering the urban areas of Milan and Turin, in Italy.

Cellular communications are undergoing significant evolutions in order to accommodate the load generated by increasingly pervasive smart mobile devices. Dynamic access network adaptation to customers' demands is one of the most promising paths taken by network operators. To that end, one must be able to process large amount of mobile traffic data and outline the network utilization in an automated manner. In this paper, we propose a framework to analyze broad sets of Call Detail Records (CDRs) so as to define categories of mobile call profiles and classify network usages accordingly. We evaluate our framework on a CDR dataset including more than 300 million calls recorded in an urban area over 5 months. We show how our approach allows to classify similar network usage profiles and to tell apart normal and outlying call behaviors.

Vehicle ad-hoc networks are considered as an essential building block of future intelligent transportation systems. One of the major roles of vehicular communication is the dissemination of information on the road in order to increase the awareness of the drivers and improve road safety. The facilities layer is a recently standardized component in the vehicular communication architecture, with an important role to play in the process of information dissemination. In this paper, we propose facilities layer-based mechanisms for information propagation and we show they outperform classical network layer solutions. We also demonstrate that previous studies that do not consider the cohabitation of different types of safety messages on the vehicular control channel highly under-estimate the dissemination delay, which can lead to unrealistic assumptions in the design of safety applications.

Floating Car Data (FCD) is currently collected by moving vehicles and uploaded to Internet-based processing centers through the cellular access infrastructure. As FCD is foreseen to rapidly become a pervasive technology, the present network paradigm risks not to scale well in the future, when a vast majority of automobiles will be constantly sensing their operation as well as the external environment and transmitting such information towards the Internet. In order to relieve the cellular network from the additional load that widespread FCD can induce, we study a local gathering and fusion paradigm, based on vehicle-to-vehicle (V2V) communication. We show how this approach can lead to significant gain, especially when and where the cellular network is stressed the most. Moreover, we propose several distributed schemes to FCD offloading based on the principle above that, despite their simplicity, are extremely efficient and can reduce the FCD capacity demand at the access network by up to 95%.

Inter-vehicular communications are considered to be an efficient proactive approach for reducing the number and the consequences of road accidents. After a series of remarkable standardisation efforts, one of the last points needing to be addressed in order for safety vehicular networks to become a reality is the scalability problem of the CSMA-based medium access control layer. With node densities that can range from very sparse to several hundred contending stations, the MAC protocol needs the capacity to adapt to the state of the vehicular network without compromising the performance of the safety applications. While previous studies focused on individual mechanisms for data rate selection or transmission power control from a global point of view, this paper proposes a complete congestion control framework aiming to increase the message reception probability in the immediate neighbourhood under heavy congestion conditions. We propose a new concept for physical carrier sensing, which takes into account the location of the transmitter, and we combine it with transmission power control and a recently proposed backoff mechanism to obtain an important improvement over the original protocol. Several implementation problems are discussed, showing the feasibility of the solution using existing hardware, and a simulation study confirms the performance of this enhanced channel access method.

Contention-based protocols for Medium Access Control (MAC) in Vehicular Ad-Hoc Networks (VANET) are currently under development in several standardization organizations. The availability and maturity of the IEEE 802.11 technology makes it the first choice for the future vehicle-to-vehicle communications. At the same time, safety applications in a vehicular environment are expected to intensively use broadcast messages. However, the IEEE 802.11 standard has not been designed for broadcast communication and a number of problems arise from this. In this paper, we analyze the impact of the minimum Contention Window (CW) on the MAC layer performance in a realistic vehicular environment and we propose a simple solution for adapting CW to the network density in order to improve the reception probability of broadcast messages.

Adding communication capabilities to vehicles and road infrastructure has become a major goal in the intelligent transportation systems industry. The IEEE 802.11p amendment has specially been conceived for the Wireless Access in Vehicular Environments (WAVE) architecture. In this paper we study the performance of this standard by the means of extensive simulations and we argue that the current version of the protocol can not cope with high vehicular densities. We propose a simple but efficient modification of the back off mechanism which has an important impact on the quality of communications on the control channel.

Enhancing road safety using vehicle-to-vehicle communication has become an important goal for the automotive industry. A lot of effort has been put in the design of an efficient medium access control protocol, capable to function correctly even under heavy congestion. The solutions proposed so far focus on data rate or transmission power control. In this paper, we argue that the most important parameter for congestion control in vehicular ad hoc networks is the carrier sense threshold. We support this theory with analytical and simulation results and we demonstrate that the optimal threshold depends on the vehicular density. Furthermore, we propose an adaptive mechanism for physical carrier sense control and analyse its performance, showing an important increase in message reception probability.

The medium access control protocol of a future vehicular ad-hoc network is expected to cope with highly heterogeneous conditions. An essential parameter for protocols issued from the IEEE 802.11 family is the minimum contention window used by the backoff mechanism. While its impact has been thoroughly studied in the case of wireless local area networks, the importance of the contention window has been somehow neglected in the studies focusing on vehicle-to-vehicle communication. In this paper we show that the adjustment of the minimum contention window depending on the local node density can notably improve the performance of the IEEE 802.11 protocol. Moreover, we compare through simulation in a realistic framework five different methods for estimating the local density in a vehicular environment, presenting the advantages and the shortcomings of each of them.

The use of inter-vehicle communication is considered the next step to be taken in order to reduce the number of traffic accidents. The design of a versatile and efficient protocol that would manage the access to the control channel reserved for safety applications would represent a significant progress towards a generally accepted technology. However, the solutions proposed hitherto rarely took into account the broadcast nature of the messages produced by the safety applications. Furthermore, the specific properties of periodic vehicular beaconing are yet to be considered by any technical committee or research study. We present the specificities of this type of messages and discuss their impact on the performance of the medium access control layer, using both analytical and simulation tools.

Vehicular Ad-Hoc Networks (VANET) aim to increase security in the current transportation system by the means of wireless communication between vehicles and between vehicles and road infrastructure. In order to realize this goal, the Medium Access Control (MAC) protocol has to be highly reliable and has to assure a certain fairness between the nodes. In this paper, we make an in-depth analysis of the properties required from a MAC protocol in a VANET context. We then use these properties to compare the performance of two MAC protocols, one based on Carrier-Sense Multiple Access and the other using Time Division Multiple Access techniques in the case of a "heartbeat" application.