Antoni Morell

Label switching over IEEE802.15.4e networks

An open issue still to be addressed in low-power lossy networks (LLNs) is how the application requirements, the available transport services, the network layer routes, and the data link-layer resources are mapped efficiently. This can be explained by the fact that, in most LLNs, link-layer resources cannot be easily managed; this results in a best effort IP layer, and traffic engineering performed solely through flow control at the transport layer. The new IEEE802.15.4e standard defines a link-layer mechanism by which motes in the network synchronise and communicate by following a schedule. Each slot in that schedule can be seen as an atomic link-layer resource, which can be allocated to any arbitrary link in the network. The schedule can be built to match the bandwidth, latency and power requirements of each mote in the network. Managing that schedule is performed centrally in IEEE802.15.4e networks today. This paper explores a solution to achieve the same goal in a distributed manner. Specifically, we argue that this problem is very similar to traffic engineering on todays Internet. We show how multiprotocol label switching can be mapped to LLNs to manage the networks schedule. By using the completely fair distributed scheduler, we show by simulation how this novel link-layer resource allocation scheme yields a proper distribution of end-to-end delays among the motes and an average throughput that achieves the 70% of the maximum possible throughput in the worst conditions tested. Copyright

Enhanced Inertial-Aided Indoor Tracking System for Wireless Sensor Networks: A Review

In recent years, there has been a growing interest in localization algorithms for indoor environments. In this paper, we have developed an enhanced filtering method for indoor positioning and tracking applications using a wireless sensor network. The method combines position, speed, and heading measurements with the aim of achieving more accurate position estimates both in the short and the long term. Using as a base, the well-known extended Kalman filter, we have incorporated two novel measurement covariance matrix tuning methods. The power threshold covariance matrix tuning method and the distance statistics covariance matrix tuning method, both based on the statistical characteristics of the distance estimations. In addition, we take into account the inertial measurements obtained from a nine-degrees of freedom inertial measurement unit. The system has been validated in real scenarios and results show that it provides long-term accuracy, that is, the accuracy remains below 1 m during a 20-min test. In summary, our methods benefit from the reduced observation error of the inertial sensors in the short term and extend it over a long period of time.

Amplify-and-Forward Compressed Sensing as a Physical-Layer Secrecy Solution in Wireless Sensor Networks

In this paper, we assess the physical-layer secrecy performance of the amplify-and-forward compressed sensing (AF-CS) framework when malicious eavesdropping nodes are listening. In particular, we investigate the robustness of the AF-CS scheme in the presence of a group of coordinated eavesdropping nodes under the assumption that they have corrupted channel state information. In order to fulfil this assumption, we propose a channel estimation technique based on pseudorandom pilots. This technique introduces extra uncertainty only in the channel estimation of the eavesdroppers. Our simulation results evaluate the physical-layer protection as a function of the total number of coordinated eavesdroppers and the level of channel estimation distortion of the eavesdroppers. We demonstrate that a small number of eavesdroppers (small being defined later on) has a zero probability of recovering the intended signal. We also show that a very large number of eavesdropping nodes are required to perfectly recover the signal in comparison with other distributed compressed sensing schemes in the literature.

Outage probability versus fairness trade-off in opportunistic relay selection with outdated CSI

We analyze the existing trade-offs in terms of system performance versus fairness of a cooperative system based on opportunistic relay selection (ORS) and with outdated channel state information (CSI). In particular, system performance is analytically evaluated in terms of outage probability, and the fairness behavior is assessed based on the power consumption at the different relays. In order to improve the fairness behavior of ORS while keeping the selection diversity gain, we propose a relay selection mechanism where the relay with the highest normalized signal-to-noise ratio (SNR) is selected for relaying the sources information. The proposed strategy is compared with existing relay selection strategies by adopting a novel graphical representation inspired by expected profit versus risk plots used in modern portfolio theory. As shown in the paper, this strategy allows operating the system in more favorable points of the outage versus fairness region.

Amplify-and-Forward Compressed Sensing as an Energy-Efficient Solution in Wireless Sensor Networks

In this paper, we propose a novel distributed compressed sensing transmission scheme, which is referred to as amplify-and-forward compressed sensing (AF-CS), in order to improve the existing tradeoff among reconstruction error, energy consumption, and resource utilization. The goal is twofold. First, to take advantage of the time correlation in order to produce sparse versions of the signal vector, which collects the transmitted signals of all the sensors. Second, to benefit from the nature of the multiple access channel in order to perform random measurements of the signal vector. Additionally, a simple model that accurately approximates the distortion introduced by the proposed scheme is presented. This model is then used to select the number of active nodes and relays based on a cost function that controls the tradeoff between reconstruction error and energy consumption. Simulation results show that the AF-CS outperforms other techniques in terms of distortion and number of transmissions, providing simultaneously, energy savings and a significant reduction in the number of channel uses.

IoT-Cloud Service Optimization in Next Generation Smart Environments

The impact of the Internet of Things (IoT) on the evolution toward next generation smart environments (e.g., smart homes, buildings, and cities) will largely depend on the efficient integration of IoT and cloud computing technologies. With the predicted explosion in the number of connected devices and IoT services, current centralized cloud architectures, which tend to consolidate computing and storage resources into a few large data centers, will inevitably lead to excessive network load, end-to-end service latencies, and overall power consumption. Thanks to recent advances in network virtualization and programmability, highly distributed cloud networking architectures are a promising solution to efficiently host, manage, and optimize next generation IoT services in smart environments. In this paper, we mathematically formulate the service distribution problem (SDP) in IoT-Cloud networks, referred to as the IoT-CSDP, as a minimum cost mixed-cast flow problem that can be efficiently solved via linear programming. We focus on energy consumption as the major driver of todays network and cloud operational costs and characterize the heterogeneous set of IoT-Cloud network resources according to their associated sensing, computing, and transport capacity and energy efficiency. Our results show that, when properly optimized, the flexibility of IoT-Cloud networks can be efficiently exploited to deliver a wide range of IoT services in the context of next generation smart environments, while significantly reducing overall power consumption.

A Robust Relay Selection Strategy for Cooperative Systems with Outdated CSI

In this paper, we consider a cooperative system based on relay selection in a scenario where the available channel state information (CSI) is subject to delays. In order to exploit the selection diversity gains of the system while providing robustness against CSI inaccuracy, we propose a robust relay selection strategy based on a minimum mean square error (MMSE) Bayesian estimator. As shown in the paper, the proposed robust strategy provides significant gains in scenarios with different levels of CSI inaccuracy.

Addressing Mobility in RPL With Position Assisted Metrics

Mobility is still an open challenge in wireless sensor networks (WSNs). Energy efficient routing strategies designed for static WSNs, such as routing protocol for low-power and lossy networks (RPL), generally have a slow response to topology changes. Moreover, their high signalling cost to keep up-to-date routes in the presence of mobile nodes makes them inefficient in these scenarios. In this paper, we introduce Kalman positioning RPL (KP-RPL), a novel routing strategy for WSNs with both static and mobile nodes, based on RPL. The objective of KP-RPL is to provide robust and reliable routing, considering the positioning inaccuracies and node disconnections that arise in real-life WSNs. This considers the original RPL for the communication among static nodes and position-based routing for mobile nodes, which use a novel RPL metric that combines Kalman positioning and blacklisting. The simulation results show that the reliability and the robustness of the network in harsh conditions are enhanced compared with geographical routing. Moreover, KP-RPL reduces the density and the number of simultaneously active anchor nodes for positioning. As a result, the infrastructure cost is lower, and the network lifetime is extended.

SAT03-3: Joint Time Slot Optimization and Fair Bandwidth Allocation for DVB-RCS Systems

This paper introduces a novel operational framework for the problem of time slot assignment in a digital video broadcast-return channel via satellite (DVB-RCS) system. The approach is compliant with the latest technical specifications emitted by the European telecommunications standards institute (ETSI) about quality of service (QoS) in satellite earth stations and systems (SES). It is a cross-layer MAC-PHY optimization approach sustained by the powerful framework of convex optimization. The paper proposes a hierarchical dynamic bandwidth allocation approach, which is motivated by the computational complexity of the single-step solution. More specifically, we obtain and analyze the optimal time duration of the time slots and jointly, we make a fair allocation of slots to areas, which is the highest level in the bandwidth allocation hierarchy. Results show up to a 10% increase in transported capacity.

Fuzzy inference based robust beamforming

This paper presents a new approach to robust beamforming based on fuzzy logic theory that is suitable for both point and scattered sources. The presented technique is based on the optimum beamformer and makes it robust to an imperfect estimate of the direction of arrival (DOA) even when powerful interferences are within the uncertainty range of the desired source. This robust approach solves the deficiencies of the classical non-robust space reference beamformers (SRB) in which the real DOA and the presumed one are taken as equal, although they are not. At low signal-to-noise ratio (SNR) the fuzzy inference-based beamformer relies on the fuzzy description of the DOA estimation, and at high SNR it places more emphasis on the estimated DOA. Interference rejection is well achieved for Interference to noise ratios (INRs) over the SNR. When the number of antennas is large, the fuzzy inference based beamformer can be implemented by means of a general side lobe canceller (GSLC) architecture and stability improves while the beamformer is still capable of suppressing weak interferences. The proposed schemes are compared with existing techniques, showing that the fuzzy inference beamformer can be an alternative when considering scenarios with DOA uncertainty and interferences. The main goals of the proposed scheme are: DOA robustness, adjustability, and numerical stability, which shortens the distance between theory and implementation.