In this paper, we model carrier shutdown in multi-carrier mobile networks as a deep reinforcement learning problem. Our model takes energy-saving actions by turning off carriers and reallocating their users while in addition to maintaining connectivity guarantees a novel user experience metric. Leveraging real and recent datasets, we train and evaluate our model over realistic network scenarios. Our results show more than 15% energy saving with the fulfillment of the user experience constraints, outperforming currently deployed solutions and researched approaches in the literature by almost 50%. More interestingly our approach exhibits generalization properties, a very promising characteristic for its adoption in real mobile networks deployment.

To address rising Quality of Service (QoS) requirements in beyond 5G systems, this paper proposes a data-centric approach for user plane QoS control in beyond 5G systems. Building upon the PSBA-XG platform, previously designed for control plane optimization, we extend its capabilities to monitor and manage the User Plane Function (UPF) performance. By aggregating standard-compliant usage reports using a publish/subscribe architecture, our solution enables rapid detection of QoS degradation and reconfiguration of user sessions. Experimental results on an open-source 5G testbed demonstrate that our approach allows for more flexible QoS management, notably rerouting user traffic to edge resources in case of congestion, than a plain 5G system, without introducing additional complexity. These results highlight the potential of data-centric architectures to enhance QoS in future mobile network systems.

In this paper, we present ELoRa, an emulation tool that generates Long Range Wide Area Network (LoRaWAN) traffic for an arbitrary number of LoRa devices and in an end-to-end virtualized LoRaWAN setting. Using ns-3, we improve an existing radio access network simulator to produce traffic compatible with ChirpStack, an open-source, cloud-native, LoRaWAN network functions stack. Our tool can be used to create realistic traffic and anomalies in order to test orchestration techniques on a real, distributed infrastructure. Moreover, the LoRaWAN core network functions (bridges, network server) are agnostic to the simulation of the radio access and can change parameters of simulated devices using native LoRaWAN protocol primitives, therefore enabling live-testing of resource allocation techniques to manage the radio access network. Multiple ELoRa instances can be connected to the same LoRaWAN core, each instance being able to support 50000 devices and 7 gateways.

Motivated by the rapid deployment of applications based on connected objects, we propose in this paper an approach to differentiating Packet Delivery Ratios (PDRs) in LoRa Wide Area Networks (LoRaWAN). This type of network is simple to deploy and operate at the expense of loose commitments in terms of quality. To overcome this shortcoming, we propose an access control method for isolating clusters of devices and meeting differentiated PDR targets. Results show that our method outperforms known one in terms of PDR via improved parameter allocation and achieves high level of intra-cluster fairness, at the expense however of decreasing the maximum cell range.

Service quality differentiation is gaining popularity in IoT networks, notably in LoRaWAN, with the rapid widespread of applications on connected devices. There is clearly a business demand for quality in IoT in the context of smart cities and network operators are urged by application designer to offer quality differentiation. However, those types of networks have been designed on the basis of a best effort service model. In particular, Packet Delivery Ratio (PDR) can dramatically decrease in dense scenarios. In this paper, we propose and evaluate traffic control and channel assignment solutions for PDR differentiation in dense deployments. Several performance criteria are defined in order to analyze the gain achieved by a network operator as well as end users. Numerical results show that both players can benefit from quality differentiation with ad-hoc pricing. This proves to be effective if penalizing low requirement devices, as they can create a bottleneck in the system. Namely, we show that in high density settings we can reach a 20% better PDR with one of the proposed policies, improving mean device servicing rate by 10% and the operator gain by 7.5%.

Novel network infrastructures require the distribution of computing and network resource control to meet stringent requirements in terms of latency, reliability and bitrate. 5G systems bring a key novelty in systems design that it the ‘network slice’as a new resource provisioning entity. A network slice is meant to serve end-to-end services as a composition of different network and system resources as radio, link, storage and computing resources. Conventionally, each resource is managed by a distinct decision-maker, platform, provider, orchestrator or controller. Naturally, centralized slice orchestration approaches are proposed in the literature, where a multi-domain orchestrator allocates the resources, for instance using a multi-resource allocation rule. Nonetheless, while simplifying the algorithmic approach, centralization can come at the expense of scalability and performance. In this article, we propose new ways to distribute the slice multi-resource resource allocation problem, using cascade and parallel resource allocations that are functionally compatible with novel software platforms. We also show how to adapt the proposed algorithms to make them able to guarantee service level agreements on the minimum resource needed, and to take into account deadline priority policy scheduling. We provide an exhaustive analysis of the advantages and disadvantages of the different approaches, including a numerical analysis for a realistic setting.

The 5G infrastructure brings a key novelty in networked systems design that is a new resource provisioning entity, the so-called "network slice". A network slice is meant to serve end-to-end services as a composition of different network and system resources as the radio, the link and a variety of computing resources (CPU, RAM, storage), generally each managed by a distinct decision-maker (platform, provider, orchestrator or controller). Naturally, centralized slice orchestration approaches have been proposed, where a multi-domain orchestrator allocates the resources, using a multi-resource allocation rule. Nonetheless, while simplifying the algorithmic approach, centralization can come at the expense of scalability and performance. In this paper, we propose new ways to decentralize the slice resource allocation problem, using cascade or parallel resource allocations. We provide an exhaustive analysis of the advantages and disadvantages of the different approaches together with a numerical analysis in a realistic environment.

Wireless sensor networks have been widely deployed in the last decades to provide various services, like environmental monitoring or object tracking. Such a network is composed of a set of sensor nodes which are used to sense and transmit collected information to a base station. To achieve this goal, two properties have to be guaranteed: (i) the sensor nodes must be placed such that all the environment of interest is covered, and (ii) every sensor node can transmit its data to the base station (through other sensor nodes). In this paper, we consider the Minimum Connected Coverage (MCC) problem. We propose two mathematical programming formulations for the MCC problem on square grid graphs. We compare them to a recent model proposed by Rebai et al [1]. Our mathematical programming formulations yield a better LP-bound at the root of the branch-and-cut process than the model of Rebai et al. Moreover, the presented formulations outperform the proportion of solved instances in their work as well as the CPU computation time and the number of nodes explored in the tree search.

In wireless sensor networks, network lifetime is among the most important criteria. Network lifetime mainly depends on the link scheduling established at the Medium Access Control layer. Indeed, the avoidance of transmission conflicts enables energy savings since there are no message retransmissions. We are interested in deterministic allocation of the wireless medium for data collection in tree based sensor networks. In this paper, we consider a generalization of the distance 2-edge coloring problem, in which transmission and interference edges are taken into account. We propose a distributed algorithm for this problem, called D2EC, which ensures that conflicts are avoided. We also carry out simulations to compare D2EC with a random allocation strategy of the wireless medium. The simulation results show that there is a significant reduction on packet loss by using D2EC. Moreover, D2EC extends the lifetime of 250% in the best case regarding the random allocation of the wireless medium.

In the past few years, the Internet of Things is driving the need for extending the Internet to constrained devices, including sensors and actuators. The IPv6 Routing Protocol for Low power and lossy networks (RPL) is appearing as an emerging IETF standard especially tailored for Low Power Area Networks (6LoWPAN). RPL constructs a Directed Acyclic Graph (DAG) according to an objective function that governs the routing according to some metric(s) and constraint(s). In the last decade, several metrics and constraints have been proposed. In this paper, we survey RPL energy-aware routing metrics and we present to the best of our knowledge the first comparative evaluation considering grid and random topologies. Moreover, we consider in this evaluation two models for the exchange of messages: a model with no packet loss and a second one with 40% of packet loss. Our experiments show that multi-criteria metrics outperform other metrics.

The minimum spanning tree (MST) construction is a classical problem in Distributed Computing for creating a globally minimized structure distributedly. Self-stabilization is versatile technique for forward recovery that permits to handle any kind of transient faults in a unified manner. The loop-free property provides interesting safety assurance in dynamic networks where edge-cost changes during operation of the protocol. We present a new self-stabilizing MST protocol that improves on previous known approaches in several ways. First, it makes fewer system hypotheses as the size of the network (or an upper bound on the size) need not be known to the participants. Secondly, it is loop-free in the sense that it guarantees that a spanning tree structure is always preserved while edge costs change dynamically and the protocol adjusts to a new MST. Finally, time complexity matches the best known results, while space complexity results show that this protocol is the most efficient to date.

This paper is dedicated to the connection, by a provider, of multiple groups of nodes spread over a network. The role of the provider is to interconnect the members of every group. For this purpose, it must distribute the available links of the network between the groups. The general aim then is to allocate these links in such a way that the communications latencies in the allocated structure are equivalent to the ones in the original (full) network for each group. We study two approaches constructing structures preserving the maximum latency (called the diameter). Unfortunately we show that the associated optimization graph problems are difficult (one cannot be approximated by a constant and the other is NP-complete). Due to these difficulties we relax the constraint on the diameter and propose to construct a unique tree connecting all the groups together. We give a heuristic to treat this problem and we propose several analytical results on its maximum and average latencies performance.

We propose a self-stabilizing algorithm for constructing a Minimum-Degree Spanning Tree (MDST) in undirected networks. Starting from an arbitrary state, our algorithm is guaranteed to converge to a legitimate state describing a spanning tree whose maximum node degree is at most Delta*+ 1, where Delta* is the minimum possible maximum degree of a spanning tree of the network. To the best of our knowledge our algorithm is the first self-stabilizing solution for the construction of a minimum-degree spanning tree in undirected graphs. The algorithm uses only local communications (nodes interact only with the neighbors at one hop distance). Moreover, the algorithm is designed to work in any asynchronous message passing network with reliable FIFO channels. Additionally, we use a fine grained atomicity model (i.e. the send/receive atomicity). The time complexity of our solution is O(mn2log n) where m is the number of edges and n is the number of nodes. The memory complexity is O(delta log n) in the send-receive atomicity model (delta is the maximal degree of the network).