§ Alfa Telecommunications, Palm Center, Beirut, Lebanon
Abstract—The wide deployment of Wi-Finetworks em- powers the implementation of numerous applications such as Wi-Fi positioning, Location Based Services (LBS), wireless intrusion detection and real-time tracking. Many techniques are used to estimate Wi-Fi client position. Some of them are based on the Time or Angle of Arrival (ToA or AoA), while others use signal power measurements and fingerprinting. All these techniques require the reception of multiple wireless signals to provide enough data for solving the localization problem. In this paper, we describe the major techniques used for positioning in Wi-Finetworks. Real experiments are done to compare the accuracy of methods that use signal power measurement and Received Signal Strength Indication (RSSI) fingerprinting to estimate client position. Moreover, we investigate a fingerprinting method constrained by distance information to improve positioning accuracy. Lo- calization techniques are more accurate when the estimated client positions are closer to the real geographical positions. Accuracy improvements increase user satisfaction, and make the localization services more robust and efficient.
In recent decades, service providers and enterprises have tried to fulﬁll the need of users to Wi-Fi connections inside residential buildings, campus, and public palaces with new centralized Wi-Fi frameworks. Although they have mostly solved the issue of Wi-Finetworks access and convergence by optimizing and softwarization of them using Software-Deﬁned Networking (SDN), there is still room to make this optimization as intelligent as possible. specially with rapid growth of user’s demand and application on smartphones. Moreover, SDN will allow us to improve the performance of centralized systems. A guaranteed connection is a key feature for wireless network users so that they can continue to use their application even if they are moving from one side of a network to another side. Handover process makes this happen by steering user from one access point to another access point or from one network to another network. Deciding when to move the user from one interface to another interface can aﬀect the QoS for users. In an enterprise Wi-Fi network, mobile users may be covered by multiple access points (APs). To optimize resource allocation, a soft handover is required in which the user’s device is seamlessly transferred from one AP to another, and this decision made centrally by a Wi-Fi network controller. Unfortunately, state-of-the-art soft handover mechanisms are often designed to optimize resources from the network provider’s point of view and do not take into account user’s real-time behaviors, which may aﬀect user’s Quality of Experience (QoE). In this thesis, a new machine learning (ML)-based method presented to deﬁne an optimal handover mechanism. This method allows predicting whether the handover that is going to happen will maintain QoE when users are moving inside a building. Our ﬁrst goal is to present a framework for handover prediction by introducing a continues score scaling based on user’s QoE. We study the behavior of tenant and eﬀect of this behavior on the handover mechanism using a data-set obtained from a real case study on a university campus. Then we deﬁne a set of rules based on our prediction results and observation inside the network. Our framework for handover prediction is completed by feeding the handcrafted features to a Support vector regression (SVR). The proposed method applied to more than one year of collected data from access points of the mentioned campus. The evaluation of results proves the eﬃciency, generalization power, and robustness of our presented framework for predicting a time-independent handover mechanism. Our proposed method improves 34% of user throughput compared to state-of-the-art algorithms.
IEEE 802.11; Beacons; Channel Saturation; Passive Measurements
Today, IEEE 802.11 Wi-Fi enabled devices (e.g., laptops, smart- phones, tablets) are ubiquitous and widely used by a large number of users accessing all sorts of applications and services. To meet the ever increasing demand for wireless connectivity, different actors have deployed IEEE 802.11 APs: Internet Service Provider (ISP) customers in their homes for their own use; businesses in their offices for their own employees, but also in public places for their customers (e.g., airports, shops, malls); public institutions serving larger areas (e.g., local administrations providing network coverage in a city). A large number of studies (see, for instance [1, 14, 17]) and projects (e.g., WiGle, OpenSignal, Sensorly) have shown that, especially in urban areas, several APs can be detected at any given location. Due to the unregulated and unplanned nature of Wi-Finetworks, APs in close proximity of each other often operate on the same channel, especially on the frequently used non-overlapping channels (1, 6, and 11 for the 2.4 GHz band). This can result in poor performance, in particular when the traffic demand exceeds
The Role of the Access Point in Wi-FiNetworks with Selfish Nodes
Abstract: In Wi-Finetworks, mobile nodes compete for accessing the shared channel by means of a random access protocol called Distributed Coordination Function (DCF), which is long term fair. But recent drivers allow users to configure protocol parameters differently from their standard values in order to break the protocol fairness and obtain a larger share of the available bandwidth at the expense of other users. This motivates a game theoretical analysis of DCF.
Nous avons proposé un tel service collaboratif (WMSP) : un sys- tème cloud pour collecter des données de réseau prises par les MS et les AP existants, et pour traiter ces données afin de générer une meilleure compréhension des réseaux existants. Le WMSP repose sur des mesures simples du réseau recueillies passivement par les MS et les AP. Nous avons constaté que le hardware existant peut être utilisé pour collecter les statistiques du réseau et qu’il est possible de combi- ner efficacement les mesures faites par les différents utilisateurs afin d’avoir une vue plus précise et à jour des réseaux Wi-Fi. Pour combi- ner plusieurs traces, nous avons proposé un algorithme qui, malgré ses limites et sa simplicité, illustre les avantages de l’agrégation de traces contenant des vues partielles de la topologie du réseau Wi-Fi. Avec l’ensemble minimal de AP et la réduction des temps de scan- ning, nous avons ainsi montré qu’il est possible d’améliorer les per- formances des processus Wi-Fi du côté réseau et du côté utilisateur. Alors que l’approche utilisée dans le cas d’utilisation de l’ensemble minimal de AP ne tenait pas compte de la qualité de service (QoS), elle confirmait la possibilité d’économiser de l’énergie en éteignant certains AP, tout en maintenant la couverture Wi-Fi.
We look at devices showing Wi-Fi problems to see if poor Web QoE episodes are consistent or intermit- tent. For each device with at least 20% poor Web QoE samples, we count instances when predicted Web QoE changes from M OS < 3 to M OS > 3 (and vice-versa). Figure 10 shows the number of Web QoE changes per hour. We see that 54% of devices present less than 10 Web QoE changes per hour, considering the average predictor. In these cases, Wi-Fi problems can be de- tected and tackled on the spot, either by a help desk operator or an end user equipped with a proper appli- cation, by executing changes in the home Wi-Fi net- work and immediately evaluating its effects. However, 5% of devices show more than 25 Web QoE changes per hour. Solving problems in these Wi-Finetworks is chal- lenging since changes cannot be evaluated immediately but require a long term monitoring approach to ensure that the applied changes were effective in solving the observed QoE issues.
IEEE 802.11n standard
Nowadays, everyone wants to have Internet connection at all time, to access to their online services anywhere. In order to fulfill this expectation, Wi-Finetworks are de- ployed in different places, including transports (trains, buses, etc ) to offer Internet connection and service access. These new services permit to the passengers to optimize their travel time and feeling productive. Then, the quality of the Internet connection is important for the attractiveness and the image of public transports. However, the transport environment can be exposed to significant electromagnetic interferences. For this reason, Wi-Fi service should work well in different electromagnetic environments.
Ubiquitous positioning technologies include but are not limited to Global Satellite Navigation Systems (GNSS) such as the American Global Positioning System (GPS), cellular and Wi-Finetworks, Radio Frequency Identification (RFID), Ultra-wide Band (UWB), ZigBee, and their integrations. Among these positioning technologies, Wi-Finetworks with the IEEE 802.11 license free communication standard have been rapidly developed in many metropolitan cities, e.g., in Australia, Hong Kong SAR of China, and Taiwan. The fundamental function of Wi-Finetworks is to provide a low-cost and effective platform for multimedia communications. In addition, the propagation of Wi-Fi signals, if properly modeled, can provide real-time positional information of mobile devices in both indoor and outdoor environments. Different Wi-Fi positioning approaches include Cell-Identification (Cell-ID), trilateration and fingerprinting. Detailed explanation of these approaches can be found in, for example [1,2].
4.2.2 Network traces
As suggested in Aschenbruck et al. (2011), user traces can be acquired through three different methods : monitoring location, communications or contacts.
The monitoring of location involves collecting successive positions of a user’s device and is mostly done with GPS. Using a network of 72 satellites, this technology can furnish a user’s position within a few meters. In the past, Liu et al. (2010) used GPS traces to study the mobility behavior of taxi drivers. Patterson and Fitzsimmons (2016) analyzed trace data collected through the smartphone travel survey application DataMobile. However, GPS data show limited application in indoor and dense urban environments, where obstacles can create a shadowing effect. This problem can be overcome by coupling the information from GSM and Wi-Finetworks (Aschenbruck et al., 2011), or in some cases with additional data sources like GTFS, as Zahabi et al. (ming) did to infer transit itineraries from smartphone data. However, as mentioned in Su et al. (2004), some users can be reluctant to share the history of their positions. Since this method is device-centric, it needs a user’s cooperation by accep- ting the burden of an additional device (e.g. GPS unit) or an energy consuming application on their own device (e.g. smartphone). We refer to this kind of location monitoring (i.e. location monitoring by a user’s device) as device-centered monitoring. This is distinguished from network-centered monitoring when device information is collected passively and auto- matically by Wi-Fi or GSM networks (Nguyen-Vuong et al., 2007), which we divide into two broad categories.
network nor the MDs have synchronized or enhanced clocks. In addition, the angulation tech- nique requires directional antennas or antenna arrays to measure the angle of incidence and this option is rarely available for ordinary mobile devices.
Wi-Fi based positioning systems have several advantages. Firstly, there has been a wide deploy- ment of Wi-Finetworks over the last few years. Wi-Fi infrastructure is available in most urban environments (universities, commercial buildings, airports, hospitals, etc.) and the number of mobile devices that have access to Wi-Fi is increasing. The second advantage is that the system is low-cost. The Wi-Fi infrastructure is composed of wireless LAN card, APs, and LAN bridges. The price of the AP and the LAN bridge is affordable, in the order of 100 CAD, and the price of a Wi-Fi card receiver is about 20 CAD. Also, this system does not require any extra hardware in the environment to fulfill the needs of the location based services. It only uses the signal strength measurements established by the Wi-Fi to locate the MDs. Thirdly, the Wi-Fi systems are attrac- tive for environments regardless of whether it is indoor or outdoor unlike other systems like the GPS that perform only on outdoors. Another advantage is that, unlike the Cellular system, the Wi-Fi technology is using license-free radio area and operates in low-power.
The Web-of-Things or WoT o ffers a way to standardize the access to services embedded on everyday objects, leveraging on well accepted standards of the Web such as HTTP and REST services. The WoT o ffers new ways to build mashups of object services, notably in smart buildings composed of sensors and actuators. Many things are now taking advantage of the progresses of embedded systems relying on the ubiquity of Wi-Finetworks following the 802.11 standards. Such things are often battery powered and the question of energy efficiency is therefore critical. In our research, we believe that several optimizations can be applied in the application layer to optimize the energy consumption of things. More specifically in this paper, we propose an hybrid layer automatically selecting the most appropriate communication protocol between current standards of WoT. Our results show that indeed not all protocols are equivalent in terms of energy consumption, and that some noticeable energy saves can be achieved by using our hybrid layer.
(B) La pluriethnicité
La FI des enseignants contribue-t-elle à l’éclosion de classes et d’écoles plus équitables pour les diverses minorités visibles et les divers groupes ethniques? Autrement dit, les futurs enseignants sont- ils préparés à une pratique pédagogique susceptible de refléter les expériences et la diversité des cultures de leurs élèves sans égard à la matière enseignée? Cette pratique que Berger (1995) appelle l’inclusion de la pluriethnicité ne devrait pas être l’affaire d’un seul cours comme c’est le cas dans les quelques institutions qui ont jusque-là tenté de préparer les futurs enseignants à la diversité culturelle des écoles canadiennes d'aujourd'hui. Elle devrait plutôt se retrouver dans les politiques et les finalités des programmes de formation à l’enseignement, dans les pratiques pédagogiques à la faculté et dans les milieux de stage, afin de permettre aux futurs enseignants de s’en imprégner. Dans cette perspective, l’éducation multiculturelle cesse d’être une tâche supplémentaire pour le formateur ou un thème
4.2 Efficient and Stealthy Data Exfiltration
A core element of this system is the exfiltration of the data collected by the node to the server. The design of this data reporting must ensure that it will be efficient in terms of computation and communication but also stealthy. Efficient, because the extra load of Wi-Fi tracking functionality must not disrupt the original functionality of the system; and stealthy because network traffic generated by data report- ing could reveal the existence of operating malware. Those objectives are tightly linked, since reducing the volume of reporting traffic will potentially reduce its detectability. Methods such as temporal aggregation could significantly reduce the amount of transmitted information. Probe re- quests are usually received by bursts of several milliseconds every 20 or 30 seconds. This data report corresponding to a burst could be aggregated by averaging the signal strength for instance. Similarly, reporting events such as arrival and
Pour ce faire, nous allons réaliser une nouvelle étude (commandée par la mairie de Montreuil) au niveau d’une autre place de Montreuil, la Place de la République, début septembre. Cette place regroupe de nombreux usages (métro, bus, voiture etc.), ce qui nous permettra d’avancer dans notre réflexion. Aujourd’hui, le prototype étant plus fiable, nous espérons pouvoir récolter des volumes de données plus importants, afin de pousser notre introspection autour des méthodes d’analyses. Concernant l’expérimentation sur la ligne de bus, les données Wi-Fi récoltées nécessitaient un grand apurement, ce qui a pu engendrer une perte d’information. De plus, compte tenu de l’abondance des données récoltées, il est difficile de confirmer avec certitude que l’échantillon final soit seulement composé d’usagers du bus. Il est prévu qu’une autre expérience de ce type soit testée dans un environnement plus fermé (type RER ou métro). Avec un capteur équipé d’une antenne moins puissante, il semblerait que ce type d’application soit plus adapté.
Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., Yumiki, K., Kobayashi, R. & T. Nakagaki (2010). Rules for biologically inspired adaptive network design. Science, 327(5964), 439-442.
Waters, N. (2006). Network and nodal indices. Measures of complexity and redundancy: A review. In A. Reggiani and P. Nijkamp (Eds.), Spatial dynamics, networks and modelling (pp. 13-33). Northampton: Edward Elgar Publishing.
Conventional wireless communication architecture, a backbone of our modern society, relies on actively generated carrier signals to transfer information, leading to important challenges including limited spectral resources and energy consumption. Backscatter communication systems, on the other hand, modulate an antenna’s impedance to encode information into already existing waves but suffer from low data rates and a lack of information security. Here, we introduce the concept of massive backscatter communication which modulates the propagation environment of stray ambient waves with a programmable metasurface. The metasurface ’s large aperture and huge number of degrees of freedom enable unprece- dented wave control and thereby secure and high-speed information transfer. Our prototype leveraging existing commodity 2.4 GHz Wi-Fi signals achieves data rates on the order of hundreds of Kbps. Our technique is applicable to all types of wave phenomena and provides a fundamentally new perspective on the role of metasurfaces in future wireless communication.
1 1 Intrusion
false alarms for human intrusion detection. In order to avoid such kinds of false alarms, we take advantage of the strength of Doppler velocity. Figure 4 shows the Doppler velocity spectrum when human motion occurs inside the house and outside the house where respectively corresponding to the cases of a direct human reflected path and a wall-blocked reflected path. The brightness of Doppler velocity on spec- trum indicates the strength of Doppler velocity. As we can see, when there is a wall blocking the reflected path between human body and Wi-Fi transceivers, the strength of Doppler velocity will be dramatically weakened. In other words, only when the human truly enters the room, human reflected path would not be blocked by walls and result in an effective Doppler velocity. Therefore, by extracting the power of the strongest Doppler velocity on spectrum, we could further determine whether human motion occurs outside the house or inside the house, where only human motion within the house means a true intrusion event. Considering that there may exist interior walls(i.e. toilet wall in Figure 5) in a typical house, one single device may have its limited sensing area as shown in Figure 5. Then in order to cover the entire room, we further combine the detection results of two Wi-Fi de- vices deployed in different places. As shown in Table 1, once there is one device detecting an effective Doppler velocity, it means that an intrusion event has occurred.
This work was supported by the LABEX IMU (ANR-10-LABX-0088) of Université de Lyon, within the program "Investissements d’Avenir" (ANR- 11-IDEX-0007) operated by the French National Research Agency (ANR).
Most mechanisms aiming at preserving privacy rely on datasets obfuscation to preserve users’ privacy or on homomorphic encryption, while transmitting requests on lo- calisation datasets to maintain users and datasets provider’s privacy . Concerning the homomorphic solution, it is proposed in  for localisation-based services using Wi- Fi fingerprints. When a user transmits queries containing ambient Wi-Fi fingerprints, the dataset provider answers with an estimated location, then user’s location is revealed. That is why mechanisms based on homomorphic encryption could handle this drawback. These two techniques which shall provide privacy to users do not protect the feeders of the datasets. Indeed, these datasets require that people car- rying devices embedding Wi-Fi and localisation capabilities transmit this data. The points of interests of these people are revealed when they transmit it. This could be tackled by device-to-device communications.