Section: New Results
Participants : Imad Alawe, Gerardo Rubino, Yassine Hadjadj-Aoul, Patrick Maillé.
Congestion control. The explosive growth of connected objects is certainly one of the most important challenges facing operators' network infrastructures. Although it has been foreseen for a very long time, it is still not clear how to support such huge number of devices efficiently.
A smarter planning of dedicated access slots would certainly limit the burden, at the access network, but remains insufficient since some equipments react to events which cannot be timed. Moreover, barring some Internet of Things (IoT) devices from accessing the network is very efficient; nevertheless, efficiency is generally linked to precise knowledge of the number of contending devices. Though, before connection establishment, the terminals are invisible to access points and, therefore, it is very difficult to estimate their number. A lower bound of backlogged devices can be determined. However, underestimating this number may lead to a congestion collapse whereas an overestimation implies underutilization of resources. In , we propose a lightweight change to the standard to accurately reveal the state of network congestion by overloading connections' requests with the number of access attempts (number of times the device has been barred as well as number of attempts). Using such information, we propose an accurate recursive estimator of the number of devices. The obtained results demonstrated that the proposed solution not only makes it possible to estimate the number of equipments much better than existing techniques, but also allows determining precisely the number of blocked equipments.
Even if the support of IoT objects represents a real challenge to the access, as mentioned above, it is nevertheless important to support them effectively in the core network, this is one of the requirements of 5G networks. While this represents an interesting opportunity for operators to grow their business, it will need new mechanisms to scale and manage the envisioned high number of devices and their generated traffic. Particularity, the signaling traffic, which will overload the 5G core Network Function (NF) in charge of authentication and mobility, namely Access and Mobility Management Function (AMF). The objective of  is to provide an algorithm based on Control Theory allowing: (i) to equilibrate the load on the AMF instances in order to maintain an optimal response time with limited computing latency; (ii) to scale out or in the AMF instance (using NFV techniques) depending on the network load to save energy and avoid wasting resources. Obtained results via computer system indicate the superiority of our algorithm in ensuring fair load balancing while scaling dynamically with the traffic load.
Energy efficiency. The use of Low Power Wide Area Networks (LPWANs) is growing due to their advantages in terms of low cost, energy efficiency and range. Although LPWANs attract the interest of industry and network operators, it faces certain constraints related to energy consumption, network coverage and quality of service. We demonstrate in  the possibility to optimize the performance of the LoRaWAN (Long Range Wide Area Network) technology, one of the most widely used LPWAN technology. We suggest that nodes use light-weight learning methods, namely, multi-armed bandit algorithms, to select the communication parameters (spreading factor and emission power). Extensive simulations show that such learning methods allow to manage the trade-off between energy consumption and packet loss much better than an Adaptive Data Rate (ADR) algorithm adapting spreading factors and transmission powers on the basis of Signal to Interference and Noise Ratio (SINR) values.
Vehicular networks. According to recent forecasts, constant population growth and urbanization will bring an additional load of 2.9 billion vehicles to road networks by 2050. This will certainly lead to increased air pollution concerns, highly congested roads putting more strain on an already deteriorated infrastructure, and may increase the risk of accidents on the roads as well. Therefore, to face these issues we need not only to promote the usage of smarter and greener means of transportation but also to design advanced solutions that leverage the capabilities of these means along with modern cities' road infrastructure to maximize its utility. To this end, we propose, in , an original Cognitive Radio inspired algorithm, named CRITIC, that aims to mimic the principle of Cognitive Radio technology used in wireless networks on road networks. The key idea behind CRITIC is to temporarily grant regular vehicles access to priority (e.g., bus or carpool) lanes whenever they are underutilized in order to reduce road traffic congestion. In , we explore novel ways of utilizing inter-vehicle and vehicle to infrastructure communication technology to achieve a safe and efficient lane change manoeuvre for Connected and Autonomous Vehicles (CAVs). The need for such new protocols is due to the risk that every lane change manoeuvre brings to drivers and passengers lives in addition to its negative impact on congestion level and resulting air pollution, if not performed at the right time and using the appropriate speed. To avoid this risk, we design two new protocols, one is built upon and extends an existing protocol, and it aims to ensure safe and efficient lane change manoeuvre, while the second is an original solution inspired from mutual exclusion concept used in operating systems. This latter complements the former by exclusively granting lane change permission in a way that avoids any risk of collision.
Adaptive Allocation for Virtual Network Functions in Wireless Access Networks Network Function Virtualization (NFV) is deemed as a mean to simplify deployment and management of network and telecommunication services. With wireless access networks, NFV has to take into account the radio resources at wireless nodes in order to provide an end-to-end optimal virtual network function (VNF) allocation. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In , we provide a model of the adaptive and dynamic VNF allocation problem considering VNF migration. Then, we formulate the optimisation problem as an Integer Linear Programming (ILP) problem and provide a heuristic algorithm for allocating multiple service function chains (SFCs).The proposed approach allows SFCs to be reallocated so as to obtain the optimal solution over time. The results confirm that the proposed algorithm is able to optimize the network utilization while limiting the reallocation of VNFs which could interrupt services.