Air pollution poses a major environmental challenge, raising concerns about human health in urban environments. It leads to diseases such as asthma, exacerbates pulmonary conditions, and creates murky skies, lowering inhabitants’ quality of life. To quantify air pollution, cost-effective IoT (Internet of Things) devices are being deployed in cities, making air quality monitoring available for a wide range of end-users, including public administrations. However, full urban coverage is unfeasible, and awareness of the carbon footprint of IoT deployments is increasing. Therefore, new techniques are needed to maximize the value of IoT networks with reduced infrastructure. To address these challenges, this paper presents an air pollution analytical forecasting solution based on deep-learning/machine-learning techniques to estimate air quality in locations without deployed devices. Different combinations of well-known deep-learning models are compared with machine-learning techniques to determine the best approach for monitoring polluting gases and airborne particles based on well-defined evaluation metrics. Additionally, two new deep-learning techniques, Multipath-CNN-LSTM (M-CNN-LSTM) and Multipath-CNN-BiLSTM (M-CNN-BiLSTM), are proposed to conduct a more exhaustive comparison. Combinations of LSTM (Long Short-Term Memory) techniques give the best results, with different models working best for each pollutant. Specifically, LSTM was optimal for O 3 , and combinations of CNN (Convolutional Neural Networks) and BiLSTM (Bidirectional LSTM) worked best for NO 2 . GRU (Gated Recurrent Unit) was more efficient for PM 2.5 , and BiLSTM performed best for PM 10 . This demonstrates that the best strategy to accurately predict the time evolution of each pollutant’s behavior depends on the selection of the most suitable machine-learning or deep-learning technique. • M-CNN-LSTM and M-CNN-BiLSTM models are proposed for air pollution forecasting. • Forecasting is performed in locations without deployed sensor devices. • LSTM is optimal for O 3 , M-CNN-BiLSTM for NO 2 , GRU for PM 2 . 5 , and BiLSTM for PM 10 .

The rapid expansion of the Internet of Things (IoT) landscape, notably in smart cities, smart metering, environmental monitoring, and smart agriculture, highlights the critical need for seamless and efficient device roaming within Long Range Wide Area Network (LoRaWAN) infrastructures. Although LoRaWAN shows great potential, its deployment faces significant challenges, especially under the constraints of the version 1.0 specifications, to enable efficient device mobility. This study introduces a comprehensive approach to enhance LoRaWAN-supported mobility, using three key advancements: the decoupling of Gateways (GWs) from the core network infrastructures to facilitate flexible deployments, the adoption of an adaptive GW-to-network connection protocol responsive to real-time traffic demands, and the enhancement of GW interoperability to provide dynamic and efficient network configurations. A notable innovation of this research is developing a mechanism that enables packet forwarding through an unidentified GW, eliminating the need for pre-established network agreements. The empirical evaluations of the article detail the effectiveness of the architecture in IoT applications, showcasing significant improvements in LoRaWAN’s roaming capabilities and reporting average latencies of up to 0.39 s in experimental tests. Extensive simulations with up to 100 roaming devices and 100 stationary devices further confirmed scalability, keeping the average roaming-to-stationary latency gap below 0.16 s and the worst-case end-to-end delay below 1.2 s even in highly congested scenarios. This contribution addresses pivotal challenges within the current LoRaWAN networks and sets the background for future advancements in IoT network technologies.

Cardiovascular events occurring in the bloodstream are responsible for about 40% of human deaths in developed countries. Motivated by this fact, we present a new global network architecture for a system for the diagnosis and treatment of cardiovascular events, focusing on problems related to pulmonary artery occlusion, i.e., situations of artery blockage by a blood clot. The proposed system is based on bio-sensors for detection of artery blockage and bio-actuators for releasing appropriate medicines, both types of devices being implanted in pulmonary arteries. The system can be used by a person leading an active life and provides bidirectional communication with medical personnel via nano-nodes circulating in the bloodstream constituting an in-body area network. We derive an analytical model for calculating the required number of nano-nodes to detect artery blockage and the probability of activating a bio-actuator. We also analyze the performance of the body area component of the system in terms of path loss and of wireless links budget. Results show that the system can diagnose a blocked artery in about 3 h and that after another 3-h medicines can be released in the exact spot of the artery occlusion, while with current medical practices the average time for diagnosis varies between 5 and 9 days.

The development of Low-Power Wide-Area Networks (LPWAN) has significantly boosted the uptake of Internet of Things technologies, fostering their adoption in such domains as Smart Cities and Industry 4.0. In the context of LPWANs, LoRa is one of the most promising developments, experiencing massive growth in recent years. This situation has led to the steadily growing trend of redundant deployments across locations, or scenarios, where it would be more natural to be able to leverage (e.g., rent) LoRa infrastructure belonging to a third party. Key to reversing this unsustainable trend is roaming approaches. Although the LoRa specifications define mechanisms for roaming, gaps and challenges make roaming difficult to deploy and operate in real-world scenarios. This paper analyzes these concerns and proposes a novel architecture compliant with the LoRa specification to design and accommodate roaming services in a consistent, efficient, and scalable way.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Air quality is a topic that arouses increasing interest, especially in the field of smart cities. In fact, most modern cities have, to a greater or lesser extent, air quality measurement stations. These systems are useful for measuring representative values but not for locating specific pollution problems. One solution to this problem is to measure in more places, but the high cost of these stations is a limiting factor. Thus, an alternative solution is to deploy a low-cost sensor network to monitor multiple points simultaneously. However, there are many challenges that must be solved when setting up a network of this type. In this presentation, the process of creating a network of this type from the beginning and how it has evolved from the initial concepts will be discussed.

Flow-guided electromagnetic nanonetworks will enable innovative medical applications for monitoring, information gathering, and data transmission inside the human body. These nanonetworks will have to operate under extreme computational and powering-related constraints, and in very hostile environments inside human vascular systems. Under these circumstances, successful transmissions between in-body nanonodes and an on-body nanorouter rarely occur, thus requiring new approaches to improve the network throughput in this scenario. Along this view, in classical flow-guided nanonetworks the nanonodes are envisioned to transmit packets if they have enough energy for the transmission, regardless of their vicinity to the nanorouter. In this paper, we propose a nanorouter awareness model that can provide significant throughput gains compared to the baseline based on blind transmissions, facilitating the roll-out of nanocommunication-supported medical applications.

Background Computer Generated Animations (CGA), when applied to three-dimensional (3D) city models (3DCM), can be used as powerful tools to support urban decision-making. This leads to a new paradigm, based on procedural modeling, that allows the integration of known urban structures. This paper introduces a new workflow for the development of high-quality approximations of urban models in a short time and enables facilities to be imported from other cities into a given city model, following specific generation rules. Thus, this workflow provides a very simple approach to observe, study, and simulate the implementation of models already developed in other cities, in a city where they are not yet adopted. Examples of these models include all types of mobility systems and urban infrastructure. This allows us to perceive the environmental impact of certain decisions in the real world, as well as to carry out simple simulations to determine the changes that can occur in the flows of people, traffic, and other city activities.

In this paper, we propose a flow-guided nano-network deployed inside of a human vascular system. The network consists of bio-sensors reporting medical events, small nano-nodes working as information carriers, and nano-routers communicating with external systems. In order to evaluate the performance and feasibility, a preliminary analytical model for this type of flow-guided nano-network is derived and validated through simulations. Results reveal that the network can meet medical requirements in terms of reported events per minute with a feasible number of nano-nodes.

Implicit Error Correction (IEC) is a near Video-on-Demand (nVoD) scheme that trades bandwidth utilization for initial playback delay to potentially support an infinite number of users. Additionally, it provides error protection without any further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. However, IEC packet loss protection is weaker at the beginning of the playback due to the lack of implicit redundancy and lower decoding efficiency, resulting in worse subjective playback quality. In tackling this issue, this paper contributes with an extension of the original nVoD architecture, enhancing its performance by adding a new element namely, subchannels. These subdivisions of the original channels do not provide further packet loss protection but significantly improve the decoding efficiency, which in turn increases playback quality, especially at the beginning. Even for very high packet loss probabilities, subchannels are designed to obtain higher decoding efficiency which results in greater packet loss protection than that provided by IEC. The proposed scheme is especially useful in wireless cooperative networks using techniques such as network coding, as content transmissions can be split into different subchannels in order to maximize network efficiency.

Poor air quality in urban areas is becoming an important problem that needs to be addressed. This paper presents the system architecture of project Urb@nEcoLife for compilation of environmental data using low-cost data sensors. Although there are several proposals dealing with sensor networks, most of them focus on solving partial problems. Urb@nEcoLife is a comprehensive proposal that develops a low-cost hardware platform together with a software architecture and a custom network topology. In addition, its main contribution is the introduction of crowdsourcing to get a really useful system for users by providing them with alerts, advises, reports and forecasts. Urb@nEcoLife is in its initial stages of development. Currently, work is underway on the development of the network architecture and the integration of the sensing devices.

This paper studies mutual information in a novel scenario combining cooperative diversity and cognitive radio based on spectrum leasing. In the scenario there is a primary transmitter subject to random channel fading, with full spectrum rights available, and a secondary transmitter without any spectrum rights, which offers its cooperation to the primary transmitter in exchange for a share of the resulting resource gains. Two decision-making schemes, based on different levels of channel knowledge, are considered in the primary transmitter. These are knowledge of the statistics of random fading (averages) and full knowledge of instantaneous channel state. The contributions of this paper are the radio leasing scenario itself, which, unlike previous approaches in cognitive radio, is not based on game theory, and the statistical characterization of the resource gains achieved in this scenario using cooperative diversity for spectrum leasing under diverse channel conditions and the aforementioned decision-making schemes. Analytical expressions are obtained for the probability of cooperation, the mutual information probability density function and its average, and the proportion of resource gains achieved for the statistical and instantaneous channel knowledge schemes. We identify the conditions for resource gains: for statistical channel knowledge to suffice, the primary link must be of low quality, whereas for instantaneous channel knowledge, although resource gains are achieved in any situation, they increase as the primary channel gets worse.

This paper studies the Implicit Error Correction (IEC) near-Video on Demand (nVoD) schema, which relies on the intrinsic redundancy of nVoD protocols, in channels that deliver damaged data. The results rather than discard show that IEC provides good protection against errors without increasing the bandwidth requirements of other schemas for the same error probability.

Distributed network protocols operate similar to periodic state machines, utilizing internal states and timers for network coordination, which creates opportunities for carefully engineered radio jamming to target the protocol operating periods and disrupt network communications. Such periodic attacks targeting specific protocol period/frequency of operation is referred to as Null Frequency Jamming (NFJ). Our hypothesis is that NFJ is a pervasive phenomenon in dynamic systems, including wireless ad-hoc networks. This paper aims to test the hypothesis by investigating NFJ targeted at the on-demand routing protocols for ad-hoc networks. Our mathematical analysis and simulation results show substantial degradation in end-to-end network throughput at certain null periods/frequencies, where the jamming periodicity self-synchronizes with the route-recovery cycle. We also study an effective countermeasure, randomized route-recovery periods, for eliminating the presence of predictable null frequencies and mitigating the impact of NFJ. Our analytical model and simulation results validate the effectiveness of randomized route recovery with appropriately chosen randomization ranges.

Distributed network protocols operate similar to periodic state machines, utilizing internal states and timers, for network coordination. This creates opportunities for carefully engineered radio jamming to target the protocol operating periods and disrupt network communications. Such periodic attacks targeting specific protocol period/frequency of operation is referred to as Null Frequency Jamming (NFJ). In this paper, we investigate NFJ targeted at the on-demand route recovery procedure, which is a crucial functionality for ad-hoc network operation. We use DSR as the example routing protocol. Our mathematical analysis and simulation results show substantial degradation in network throughput at certain null frequencies, where the jamming periodicity self-synchronizes with the route recovery cycle. Using simulations, we also demonstrate an effective countermeasure, randomized route-recovery periods, for eliminating the presence of predictable null frequencies and mitigating the impact of NFJ.

In the nVoD (near video-on-demand) paradigm, video is distributed through broadcast channels with constrained bandwidth, so that at reception there is a bounded playback start time that is independent of the number of users. In other words, there is unlimited scalability for low bandwidth cost, at the expense of a bounded start time. Thus, regarding bandwidth, nVoD schemas are well suited for satellite channels, so packet losses have not been considered in their context for a long time. Recent research based on digital fountains and intrinsic redundancy of nVoD transmissions has produced nVoD protocols with correction capabilities, which have little or zero impact on bandwidth usage. The authors analyse the most recent proposals using satellite channel models and compare them in terms of error correction performance and client and server resources.

As wireless mesh networks become more widely deployed, media streaming in such networks poses a pressing research issue because of the increasing importance of multimedia applications. The challenge with wireless mesh networks is the time-varying wireless channel compounded by multi-hop transmission. However, wireless mesh networks also present an opportunity, i.e., the redundancy provided by broadcast wireless media and mesh connectivity. In this paper, we present a joint interchannel and network coding schema to take advantage of this opportunity and to overcome the challenge of wireless mesh networks. We evaluate of the proposed joint coding through both mathematical analysis and computer simulation. The results demonstrate significant benefits in terms of protection against packet losses and bandwidth utilization of the joint coding schema.

In this paper we present a novel multicast near-Video on Demand (nVoD) scheme. It relies on the intrinsic redundancy of nVoD protocols to provideimplicit error correction, by employing content segments themselves as blocks for coding operations. The results in this paper show that our proposal outperforms previous schemes in terms of transmission bandwidth, for the same packet loss probability and response time.

In this paper we present a novel multicast near- video on demand (nVoD) schema which relies on the intrinsic redundancy of nVoD protocols to provide implicit error correction, by employing content segments as blocks for coding operations. The results in this paper show that our proposal outperforms previous approaches involving explicit error correction (error protection within content segments) in terms of transmission bandwidth for the same packet loss probability.

The parallel hierarchical matching (PHM) algorithm is a distributed maximal size matching scheduler for virtual output-queued switches. In a previous letter, we formulated an upper bound on the maximum number of iterations PHM requires to achieve a maximal size matching in any traffic scenario. In this letter, we follow an analytical approach to find the average number of iterations for PHM to achieve a maximal size matching under diverse traffic models. The estimated number of iterations is O(log2N), as in the case of iSLIP-like algorithms

HomePlug powerline communications networks link individual computers or network segments via low-voltage power lines. The powerline medium is a harsh channel, since it varies in time and has a high attenuation. This makes difficult to communicate different devices in distant places in a building. A possible solution is the HomePlug repeater we propose in this paper. We show that it is impossible to develop a repeater at the physical layer, so we have developed it at medium access control (MAC) level. Its main distinctive features are transparency (no planning is necessary) and availability (it is built from off-the-shelf hardware).

In this paper, we propose a real-time transcoding system to generate contents for mobile networks. Video transcoding is widely used to change storage formats. However, as far as the authors know, it has not been proposed to generate real-time contents. We implement and evaluate an indoor video service that delivers transcoded contents to wireless pocket PCs via IEEE 802.11b multicast. We propose a generic methodology for mobile video planning.

Input-queued packet switches are more scalable than output-queued ones. However, due to HOL blocking, their throughput is poor. The virtual output queueing (VOQ) switch architecture and several buffer schedulers have been proposed to overcome this problem. Among them, the class of iterative maximal matching algorithms, with the first example being parallel iterative matching (PIM), which uses random selection, and iSLIP that uses round-robin selection, and has become a de facto standard in switching research. iSLIP admit efficient practical implementations, and has several variants with different pointer updating strategies - like FIRM, DRRM and RDSRR - that improve performance. In previous work, we formulated a new scheduler, parallel hierarchical matching (PHM), which compare favorably to iSLIP-like algorithms. PHM can be considered a parallelization of previous high-performance sequential hierarchical matching algorithms, like 2DRR or WWFA. In this paper, we compare their delay performance for same decision response-time, determined from ASIC implementations, and for different traffic models.

iSLIP and parallel hierarchical matching (PHM) are distributed maximal size matching schedulers for input-buffered switches. Previous research has analyzed the hardware cost of those schedulers and their performance after a small number of iterations. In this paper, we formulate an upper bound for the number of iterations required by PHM to converge. Then, we compare the number of iterations required by iSLIP and PHM to achieve a maximal throughput under uniform Bernoulli traffic, by means of simulation. Finally, we obtain the corresponding delay performances, which are similar. The results suggest that PHM has both the advantages of previous hierarchical matching algorithms (low hardware complexity) and iSLIP (low number of iterations).