SDN-Based Channel Assignment Algorithm for Interference Management in Dense Wi-Fi Networks

The popularity of Wi-Fi-enabled devices alongside the growing demand for non-licensed spectrum, has made the Wi-Fi networks exceedingly congested. This endangers the efficiency of Wi-Fi and negatively affect the users' experience. The problem is especially pressing in dense areas (e.g. shopping centers) where Wi-Fi channel assignment is more likely to be uncoordinated and the working environment of Wi-Fi Access Points (APs) has become increasingly time-variant. As a result, the availability of Software-Defined Networking (SDN) and network virtualization technologies has motivated the use of centralized resource management as a solution. This paper provides an algorithm for channel assignment functionality in the context of SDN-based centralized resource management, which captures the live status of a Wi-Fi network and is capable of optimising the Radio Frequency (RF) channel assignment process. The APs' network arrangement, the current assignment of the channels and the characteristics of the RF channels in IEEE 802.11 have all been taken into account in the proposed model. The performance of the proposed model in terms of the level of the interference, the spectral efficiency at each AP and the Signal to Interference plus Noise Ratio (SINR) at the user-side is evaluated through simulation and compared against state of the art solutions

SDN-based channel assignment algorithm for interference management in dense Wi-Fi networks

The popularity of Wi-Fi-enabled devices alongside the growing demand for non-licensed spectrum, has made the Wi-Fi networks exceedingly congested. This endangers the efficiency of Wi-Fi and negatively affect the users' experience. The problem is especially pressing in dense areas (e.g. shopping centers) where Wi-Fi channel assignment is more likely to be uncoordinated and the working environment of Wi-Fi Access Points (APs) has become increasingly time-variant. As a result, the availability of Software-Defined Networking (SDN) and network virtualization technologies has motivated the use of centralized resource management as a solution. This paper provides an algorithm for channel assignment functionality in the context of SDN-based centralized resource management, which captures the live status of a Wi-Fi network and is capable of optimising the Radio Frequency (RF) channel assignment process. The APs' network arrangement, the current assignment of the channels and the characteristics of the RF channels in IEEE 802.11 have all been taken into account in the proposed model. The performance of the proposed model in terms of the level of the interference, the spectral efficiency at each AP and the Signal to Interference plus Noise Ratio (SINR) at the user-side is evaluated through simulation and compared against state of the art solutions

Machine Learning driven road safety over smart Networks based on Electroencephalography data analysis

Παρουσιάζω μια λύση για την οδική ασφάλεια χρησιμοποιώντας τα δεδομένα EEG που παρέχονται από την τεχνολογία EMOTIV και την ανάλυση μηχανικής μάθησης για να εξαγάγετε, πότε ο οδηγός έχει πρόβλημα και δεν μπορεί να λειτουργήσει το όχημα. Η λύση δοκιμάστηκε σε δύο SDN ενεργοποιημένες πλατφόρμες EmPOWER και Mininet-WiFi. Το EmPOWER στηρίζεται σε μια ενιαία πλατφόρμα, η οποία αποτελείται από υλικό γενικής χρήσης και λειτουργικό σύστημα και παρέχει νέες δυνατότητες και δυνατότητες τόσο για τον ασύρματο όσο και για τον τομέα κινητής τηλεφωνίας. Παρέχει επίσης τρεις τύπους εικόνικών πόρων δικτύου, όπως κόμβοι προώθησης (switches OpenFlow), κόμβους επεξεργασίας πακέτων (Micro Servers) και κόμβους ραδιοφωνικής επεξεργασίας (σημεία πρόσβασης WiFi ή LTE eNodeBs). Το Mininet-WiFi είναι ένας εξομοιωτής, ο οποίος δημιουργεί ένα εικονικό δίκτυο, πυρήνα, διακομιστή και κώδικα εφαρμογής, σε ένα μόνο μηχάνημα και επιτρέπει τη χρήση και των δύο WiFi Σταθμών και Σημείων Πρόσβασης. Επιπλέον, το EMOTIV είναι μια πλατφόρμα που χρησιμοποιεί αισθητήρες ηλεκτροεγκεφαλογραφίας (EEG) για τη μέτρηση σημάτων ενός εγκεφάλου και τη μετατροπή τους σε ανεπεξέργαστα δεδομένα, τα οποία αργότερα μπορούν να χρησιμοποιηθούν για ερευνητικούς και άλλους σκοπούς. Έθεσα την πλατφόρμα EmPOWER σε ένα τοπικό δίκτυο με ένα μηχάνημα που λειτουργούσε ως ελεγκτής, ένας εξυπηρετητή που λειτουργούσε ως εξυπηρετητή άκρης, μία εικονική μηχανή σε ένα εξυπηρετητή σε αρχιτεκτονική τύπου σύννεφο με το ρόλο του εξυπηρετητή τύπου σύννεφου της εφαρμογής, ένας δρομολογητής που ενεργούσε ως επεξεργαστής πακέτων και δύο δρομολογητές με δυνατότητα ασύρματης επικοινωνίας που έχει το ρόλο σημείων πρόσβασης στα οποία μπορούν να συνδεθούν οι πελάτες. Για το Mininet-WiFi, τρέχω την πλήρη εγκατάσταση σε μια ενιαία εικονική μηχανή και η τοπολογία της αποτελείται από ένα εξυπηρετητή ελεγκτή συνδεδεμένο σε διακομιστή OpenFlow, ο οποίος συνδέει τον κεντρικό διακομιστή, τον εξυπηρετητή σύννεφων και το Access Point. Οι πελάτες είναι δύο φορητοί υπολογιστές συνδεδεμένοι στο WiFi της πλατφόρμας EmPOWER ή δύο προσομοιωμένοι κόμβοι συνδεδεμένοι στο Σημείο Πρόσβασης του Mininet-WiFi. Η εφαρμογή αποτελείται από τρεις υλοποιήσεις, έναν διακομιστή σύννεφο, ο οποίος δημιουργεί το μοντέλο εκπαίδευσης και αποθηκεύει συμβάντα σε μια βάση δεδομένων, έναν εξυπηρετητή άκρων, ο οποίος ταξινομεί τα ληφθέντα δεδομένα και λαμβάνει τις κατάλληλες ενέργειες, και έναν πελάτη, ο οποίος στέλνει EEG και δεδομένα θέσης στην end server και λαμβάνει εντολές από τον ίδιο διακομιστή. Παρέχει τη δυνατότητα να ελέγξει εάν οι χειριστές ενός οχήματος, πελάτη των παραπάνω δικτύων, έχει τα μάτια του κλειστά και συνεπώς να είναι σε θέση να συνεχίσει την αποστολή του ή απαιτείται δράση δύο βημάτων. Στο πρώτο στάδιο, αν ο χειριστής έχει τα μάτια κλειστά, λαμβάνει χώρα μια ενέργεια αφύπνισης στο τερματικό του. Στο δεύτερο στάδιο, εάν ο χειριστής δεν ανοίξει τα μάτια του, τότε δίνεται μια ενέργεια για να σταματήσει το όχημα και αν κάποιος άλλος χειριστής βρίσκεται κοντά, ενημερώνεται για την κατάσταση από το τερματικό του. Επιπλέον, η απόφαση για τις ενέργειες λαμβάνεται από τον κεντρικό εξυπηρετητή χρησιμοποιώντας τον αλγόριθμο KNN, ο οποίος ταξινομεί, τα ακατέργαστα δεδομένα που λαμβάνονται, σε δύο κατηγορίες, κοντά στα μάτια και τα μάτια ανοιχτά. Η επικοινωνία μεταξύ των εξυπηρετητών και του πελάτη χρησιμοποιεί το πρωτόκολλο MQTT, το οποίο είναι μια ελαφριά μεταφορά μηνυμάτων μετάδοσης / εγγραφής και είναι ιδανικό για κινητές εφαρμογές σε σενάρια του Διαδικτύου των Πραγμάτων και έχει αποτελεσματική κατανομή πληροφοριών σε έναν ή πολλούς δέκτες. Τα αποτελέσματα καταδεικνύουν τη χρήση αυτών των τεχνολογιών στην οδική ασφάλεια, χρησιμοποιώντας τοπολογίες έξυπνων δικτύων, λύσεις παρακολούθησης χρηστών και τεχνικές εκμάθησης μηχανών. Λαμβάνοντας υπόψη τα οφέλη των προαναφερόμενων τεχνολογιών, καταλήγω στο συμπέρασμα ότι τέτοιες λύσεις παρουσιάζουν πολλά υποσχόμενα πλεονεκτήματα στον τομέα της οδικής ασφάλειας, αλλά αφήνουν περιθώρια βελτίωσης καθώς βρίσκονται σε πρώιμο στάδιο ανάπτυξης.I present a solution for road safety using EEG data provided from EMOTIV Technology and machine learning analysis to extract when a driver has a problem and cannot operate the vehicle. The solution was tested in two SDN enabled platforms EmPOWER and Mininet-WiFi. EmPOWER rests on a single platform, which consists of general-purpose hardware and operating system, and provides new features and capabilities both for the wireless and the mobile domain. It also provides three types of virtualized network resources such as forwarding nodes (OpenFlow switches), packet processing nodes (Micro Servers) and radio processing nodes (WiFi Access Points or LTE eNodeBs). Mininet-WiFi is an emulator, which creates a virtual network, kernel, switch and application code, on a single machine and allows the using of both WiFi Stations and Access Points. Moreover, EMOTIV is a platform that uses electroencephalography (EEG) sensors to measure signals of a brain and convert them to raw data, that later can be used for research and other purposes. I implemented the EmPOWER platform on a local network with a machine acting as the controller, one server acting as an edge server, one virtual machine on a cloud server with the role of the cloud server of the application, one router acting like packet processor and two routers with wireless capable hardware having the role of access points to which the clients can connect to. For the Mininet-WiFi, I run the complete setup on a single virtual machine and its topology consists from one controller server connected to OpenFlow switch, which connects the edge server, the cloud server and the Access Point. The clients are two laptops connected to the WiFi of the EmPOWER platform or two simulated nodes connected to the Mininet-WiFi’s Access Point. The application consists from three deployments, a cloud server, which creates the training model and stores incidents to a database, an edge server, which classifies the data received and takes the proper actions, and a client, which send EEG and position data to the edge server and receives commands from the same server. It provides the ability to check if the operators of a vehicle, client of the above networks, has his eyes closed and therefore be able to continue his task or a two-step action is required. On the first stage, if the operator has his eyes closed, a wakeup action is taking place to his terminal. On the second stage, if the operator does not open his eyes, then an action to stop the vehicle is given and if any other operator is at close proximity, he is informed about the situation from his terminal. Moreover, the decision for the actions is taken by the edge server using the KNN algorithm, which classifies, the raw data received, to two categories, eyes close and eyes open. The communication between the servers and the client uses the MQTT protocol, which is a lightweight publish/subscribe messaging transport and is ideal for mobile applications in Internet of Things scenarios and has efficient distribution of information to one or many receivers. The results demonstrate the usage, such technologies will have to road safety by utilizing smart network topologies, user monitoring solutions and machine learning techniques. Taking into consideration the benefits of the aforementioned technologies, I conclude that such solutions show promising advantages on the area of road safety, but leave room for improvement as they are at an early stage of development

Wireless Software Defined Network Deployment and Optimization with Emphasis on Internet of Things Applications

Μία εφαρμογή στην πλατφόρμα EmPOWER, υλοποιώντας μια διεπαφή για Ροή Πακέτων στο Ασύρματη Δικτύωση Βασισμένη στο Λογισμικό.An application on the EmPOWER platform, implementing a Wireless Software Defined Network Packet Flow Rule Interface

Load-Aware Traffic Control in Software-Defined Enterprise Wireless Local Area Networks

With the growing popularity of Bring Your Own Device (BYOD), modern enterprise Wireless Local Area Networks (WLANs) deployments always consist of multiple Access Points (APs) to meet the fast-increasing demand for wireless access. In order to avoid network congestion which leads to issues such as suboptimal Quality of Service (QoS) and degraded user Quality of Experience (QoE), intelligent network traffic control is needed. Software Defined Networking (SDN) is an emerging architecture and intensively discussed as one of the most promising technologies to simplify network management and service development. In the SDN architecture, network management is directly programmable because it is decoupled from forwarding layer. Leveraging SDN to the existing enterprise WLANs framework, network services can be flexibly implemented to support intelligent network traffic control. This thesis studies the architecture of software-defined enterprise WLANs and how to improve network traffic control from a client-side and an AP-side perspective. By extending an existing software-defined enterprise WLANs framework, two adaptive algorithms are proposed to provide client-based mobility management and load balancing. Custom protocol messages and AP load metric are introduced to enable the proposed adaptive algorithms. Moreover, a software-defined enterprise WLAN system is designed and implemented on a testbed. A load-aware automatic channel switching algorithm and a QoS-aware bandwidth control algorithm are proposed to achieve AP-based network traffic control. Experimental results from the testbed show that the designed system and algorithms significantly improve the performance of traffic control in enterprise WLANs in terms of network throughput, packet loss rate, transmission delay and jitter

Interference Management in Software-Defined Mobile Networks

Software-Defined Networking promises to deliver more flexible and manageable networks by providing a clear decoupling between control plane and data plane and by implementing the latter in a logically centralized controller. However, if such principles are to be applied also to wireless networks, new primitives and abstractions capable of providing programmers with a global view of the network capturing channel quality and interference must be devised. Moreover, the dynamic radio environment necessitates fast adaptation of physical parameters such as power, modulation and coding schemes. So the wireless SDN abstractions should allow for such adaptations to happen closer to the air interface. In this paper, we present high level abstractions for channel quality, interference and network reconfiguration; the latter permits operations differing in timescales to be carried out at different controller entities. The proposed concepts have been implemented and evaluated over a WiFi-based WLAN. Empirical measurements show that the proposed platform can be used to implement typical WiFi network management tasks such as channel assignment and interference monitoring

Interference Management in Software-Defined Mobile Networks

Software-Defined Networking promises to deliver more flexible and manageable networks by providing a clear decoupling between control plane and data plane and by implementing the latter in a logically centralized controller. However, if such principles are to be applied also to wireless networks, new primitives and abstractions capable of providing programmers with a global view of the network capturing channel quality and interference must be devised. Moreover, the dynamic radio environment necessitates fast adaptation of physical parameters such as power, modulation and coding schemes. So the wireless SDN abstractions should allow for such adaptations to happen closer to the air interface. In this paper, we present high level abstractions for channel quality, interference and network reconfiguration; the latter permits operations differing in timescales to be carried out at different controller entities. The proposed concepts have been implemented and evaluated over a WiFi-based WLAN. Empirical measurements show that the proposed platform can be used to implement typical WiFi network management tasks such as channel assignment and interference monitoring