In the next few years, Low Earth Orbit (LEO) constellations will become key enablers for the deployment of global Internet of Things (IoT) services. Due to their proximity to Earth, LEO satellites can directly communicate with ground nodes and, thus, serve as mobile gateways for IoT devices deployed in remote areas lacking terrestrial infrastructure. Within this Direct-to-Satellite IoT (DtS-IoT) context, LoRa (Long Range) technology, capable of providing long range connectivity to power-constrained devices, has received great attention. However, serious scalability issues have been observed in LoRa-based DtS-IoT networks when a high number of LoRa devices perform uplink transmissions driven by the straightforward Aloha protocol during the short visibility periods of the passing-by satellites. In this paper, we evaluate some Aloha-based protocols suitable for this kind of networks and present a new adaptive variant that allows LoRa devices to dynamically adjust their uplink transmission rates in order to make the network work near its optimal operating point. Simulation results show that our proposal is able to significantly improve the network throughput in overloaded scenarios without the need for coordination among LoRa devices, listening to the channel nor gateway support.

The innovative information-based Named-Data Networking (NDN) architecture provides a good opportunity to rethink many of the design decisions that are taken for granted in the Internet today. For example, active queue management (AQM) tasks have been traditionally implemented in the routers to alleviate network congestion before their buffers fill up. However, AQM operations could be performed on an end-to-end basis by taking advantage of NDN features. In this paper, we provide an implementation of an AQM algorithm for the NDN architecture that we use to drive a classical AIMD-based congestion control protocol at the receivers. To accomplish this, we take advantage of the 64-bit Congestion Mark field present in the link layer of NDN packets to encode both rate and delay information about each transmission queue along a network path. In order to make the solution scalable, this information is delivered stochastically, guaranteeing that receivers get accurate and updated information about every pertinent queue. This information is enough to implement the well-known controlled delay (CoDel) AQM algorithm. Simulation results show that our client-located CoDel implementation is able to react to congestion when the bottleneck queuing delay surpasses the 5 ms target set by the usual in-network CoDel implementation and, at the same time, get a fair and efficient share of the available transmission capacity.

With the increasing popularity of multimedia applications, video data represents a large portion of the traffic in modern networks. Consequently, adequate models of video traffic, characterized by a high burstiness and a strong positive correlation, are very important for the performance evaluation of network architectures and protocols. This paper presents a new model for traffic with persistent correlations based on the M/G/infin process. We use a new discrete distribution for the service time of the M/G/infin queueing system, flexible enough to give rise to a wide range of correlation structures of the resulting M/G/infin process in a parsimonious way. The corresponding simulation model is easy to initialize in steady state. Moreover, as the distribution has subexponential decay, we can apply a highly efficient and flexible generator of synthetic traces of the M/G/infin process. As an example, we analyze the capabilities of our model for capturing the empirical distribution and correlation structure of VBR MPEG video traffic.

This paper addresses the provision of proportional differentiated services to an arbitrary number of traffic classes in terms of the class packet loss probability measured between the ingress node and the egress node of an OBS network. Our solution relies on a key idea: OBS networks consist of bufferless nodes and can therefore be regarded as a whole like a one-hop bufferless subnetwork characterized by a collection of loss probabilities computable with simple, approximate circuit switching models. Consequently, we believe it is possible to attain packet loss proportionality merely using a simple stochastic algorithm to assemble two classes of bursts, provided there exists some form of internal relative differentiation such that one of the burst classes has a much lower loss probability than the other. In order to evaluate the algorithm accuracy in attaining the proportionality, we present an analytical study of two idealized scenarios (a single link and a multi-node symmetrical network), including the effect caused by the flow dynamics of the traffic received at the ingress nodes. Our results show that, despite its simplicity, this approach is able to provide the desired proportionality over a wide range of operating conditions.