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.

Compelling features such as low power consumption and low complexity make LoRaWAN one of the most promising technologies to provide long-range connectivity to resource-constrained devices. However, LoRaWAN suffers from limited scalability since it uses an Aloha-based protocol for accessing the channel that causes a huge amount of frame collisions when the number of devices (or the network load) is high. This paper presents LFS-CSMA, a new medium access control mechanism that enhances the scalability of LoRaWAN networks by just combining the well-known slotted Aloha and CSMA schemes in a novel manner. With LFS-CSMA, longer frames are transmitted earlier within a given timeslot. Thus, devices with short frames to be transmitted can check the channel availability before sending them and avoid collisions if they detect an ongoing transmission. Performance results show that LFS-CSMA causes far less collisions than traditional MAC mechanisms, thus improving the scalability of LoRaWAN networks significantly.

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.

In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer non-linear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of r transmitters) and/or transmission through s different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. In addition, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90% of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed.

In underlay device-to-device multicast communication (D2MD), a group of users can communicate directly by reusing cellular resources blocks (RB) to share common content. This paradigm brings great benefits to cellular networks in term of energy and spectral efficiency. However, D2MD or cellular communication quality may degrade or blocked due to harmful mutual interference between cellular, D2MD users sharing the same communication resources. Therefore, to fully achieve the advantages of D2MD communication, resource management and power control became critical. In this paper, we model the join power and resource allocation as a mixed integer nonlinear problem (MINLP) to maximize network global energy efficiency (GEE). To coup with NP-hard nature of the problem, we investigate a two stages solution. At first, we propose a scheme based on matching theory to solve resource allocation sub-problem. Here, we introduce a reuse and a split factor to control the aggregated interference on CUE from D2MD and the number of RB used by each group. Second, we apply the framework of fractional programming to optimally solve the power control sub-problem subject to QoS constraints. Finally, GEE metric is analysed via extensive numerical simulations with a spatial Poisson process for the users' locations and applying different clustering algorithms as K-nearest neighbour, distance limit.

In this paper, we address the problem of joint resource and power allocation in underlay device-to-device multi-cast communication for short (D2MD) to maximize system global energy efficiency (GEE). For this, we propose a two-stage semi-distributed solution. First, we model the resource sharing sub-problem as a transferable overlapping coalition formation game. Here, a D2MD group can participate in s coalitions and it can decide to join or leave a coalition based on specific split and merge rules. Similarly, a resource block can be shared among r D2MD groups where s, r are reuse and split factors. After that, the transmission power is centrally controlled by the The base station (BS) via a fractional programming framework. Finally, GEE is analysed via extensive numerical simulations with a spatial Poisson process for the users' locations and applying two different clustering algorithms: K-nearest neighbour, and distance-limited.

The IEEE 802.3bj standard defines two potential low power operating modes for high speed energy efficient ethernet (EEE) physical interfaces working at 40 and 100 Gb/s: a not-so-efficient low power mode that requires very short transition times to restore normal operation (Fast-Wake) and a highly efficient low power mode with longer transition times (Deep-Sleep). In this paper, we present a new frame coalescing mechanism that dynamically adjusts the coalescing queue threshold in order to minimize the energy consumption of dual-mode EEE interfaces and maintains, at the same time, the average frame delay close to a target value. The proposed mechanism has been validated through simulation under different types of traffic (Poisson, self-similar, and real Internet traffic). In addition, we show that, with the current transition times and efficiency profiles of the standardized low power modes, our proposal renders the Fast-Wake mode unnecessary in most practical scenarios.

The widespread use of computing and communications technologies has enabled the popularity of social networks oriented to learn. In this work, we study the nature and strength of associations between students using an online social network embedded in a learning management system. With datasets from two offerings of the same course, we mined the sequences of questions and answers posted by the students to identify structural properties of the social graph, patterns of collaboration among students and factors influencing the final achievements. The results show some hints to pursue and investigate deep user analytics in online social learning systems, e.g., to build accurate prediction models for the success of effectiveness of the learning tasks based on the patterns of students' participation in the platform.

It is commonly accepted that contemporary cohorts of students witness and experience the benefits of information technologies in their learning processes. The so-called “digital natives” acquire, as a consequence of their early exposure to these technologies, different patterns of work, distinct attention conducts, new learning preferences and, generally, better skills for learning and working within rich online social contexts. So, it seems reasonable that the traditional education systems evolve, and shape their practice to leverage those new patterns. Though online social networks (OSNs) stand as a powerful tool to extend existing online learning management systems (LMSs) by using them as a gateway to OSNs, LMSs do not exploit fully the advantages of an active social environment for reinforcing the learning experience. We report in this paper the design, development and use of a software platform which enlarges and adapts the basic features of an OSN in order to be useful for very general learning environments. The software allows the creation, assessment and reporting of a range of collaborative activities based on social interactions among the students, and offers a reward mechanism by means of ranking and reputation. We argue that this approach is helpful in increasing the students' motivation, besides improving the learning experience and performance. The software has been tested in an undergraduate course about computer networks. Different tests confirm that the impact on learning success is statistically significant and positive.

The discontinuous reception (DRX) mechanism is commonly employed in current LTE networks to improve energy efficiency of user equipment (UE). DRX allows UEs to monitor the physical downlink control channel (PDCCH) discontinuously when there is no downlink traffic for them, thus reducing their energy consumption. However, DRX power savings are achieved at the expense of some increase in packet delay since downlink traffic transmission must be deferred until the UEs resume listening to the PDCCH. In this paper, we present a promising mechanism that reduces energy consumption of UEs using DRX while simultaneously maintaining average packet delay around a desired target. Furthermore, our proposal is able to achieve significant power savings without either increasing signaling overhead or requiring any changes to deployed wireless protocols.

Social networks are being massively utilized for targeting consumers, as entertainment channels and in personal communications, but also offer an appealing potential as a tool for learning in higher education, largely unexplored yet. This paper describes the design and implementation of a social network platform aimed at integrating informal learning processes (games, questions and answers, group ranking) in the university curricula. We present the design principles, the major software modules and the workflow of our tool, which is based on open-source software.

The IEEE 802.3az standard provides a mechanism to build energy efficient Ethernet interfaces via a low power idle mode that they can enter when there is no data to transmit. Several competing algorithms have appeared that make use of this mode to minimize energy consumption with little disruption to the traffic. Two algorithms stand out among those because of their simplicity and performance: frame transmission and burst transmission. Although these algorithms have been shown to be very efficient in simulated scenarios, there is a lack of general analytical models for their behavior. In fact, to this date, the only analyzed traffic patterns have been variants of Poisson traffic. In this paper we provide a general GI/G/1 model for energy consumption and traffic delay for both algorithms. We then develop specializations of the general model for Poisson and deterministic traffic. Finally, we validate the model with the help of both synthetic traffic and real Internet traffic traces.

In this paper, we research the feasibility of adapting the packet coalescing algorithm, used successfully in IEEE 802.3az Ethernet cards, to upstream EPON channels. Our simulation experiments show that, using this algorithm, great power savings are feasible without requiring any changes to the deployed access network infrastructure nor to protocols.

Network interfaces in most LAN computing devices are usually severely under-utilized, wasting energy while waiting for new packets to arrive. In this paper, we present two algorithms for opportunistically powering down unused network interfaces in order to save some of that wasted energy. We compare our proposals to the best known opportunistic method, and show that they provide much greater power savings inflicting even lower delays to Internet traffic.

We propose a new hybrid congestion control protocol for multimedia streaming. While dominant proposals in the field have generally dealt with either pure single-rate or multiple-rate proposals, these either lack the ability to adapt to heterogeneous receivers or are too complex to be fully understood and safely deployed. Recent advances have suggested the use of hybrid approaches, but so far these approaches fail to take into account the real capabilities of the receivers. Our proposal works by sampling the allowed transmission rate of the set of receivers and adjusting the minimum transmission rate of each multicast group according to these data. Then a single-rate congestion control protocol guarantees in each group the TCP-fairness of the approach.

One of the more challenging research issues in the context of assured forwarding (AF) is the fair distribution of bandwidth among aggregates sharing the same AF class. Several studies have shown that the number of microflows in aggregates, the round trip time, the mean packet size and the TCP/UDP interaction are the key factors in the throughput obtained by the competing aggregates. Dynamic RIO (DRIO) is an interesting RIO technique suggested to improve assurances among heterogeneous flows in AF-based services. In this paper, we propose applying DRIO to aggregated traffic instead of the individual flows. Working at the aggregate level not only makes DRIO more appropriate to be used in AF networks, but also improves scalability substantially. In addition, we also mitigate some unfairness issues found in the original scheme. Several simulation experiments have been conducted to verify that the proposed improvements allow DRIO to fulfill fairness requirements more satisfactorily.

Congestion control of multicast real-time flows in the Internet has different requirements and poses further difficulties than common end-to-end unicast congestion control. In this paper we present a novel congestion control protocol, the layered datagram protocol (LDP), aimed at solving such problems. LDP has been designed to be fair against competing TCP traffic and, at the same time, exhibit smoothness in the instantaneous throughput, making it attractive for real-time multimedia transmission. Furthermore, the open-loop approach makes the protocol highly scalable, as the server load is not affected by the number of clients present in the transmission. Our simulations show that the protocol exhibits the expected TCP fairness while still being highly tunable to smooth the instantaneous throughput.