Alejandro Correa

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.

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.

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.

Indoor Pedestrian Tracking by On-Body Multiple Receivers

During the past years, the development of indoor localization systems has been a hot topic in research, because the global navigation satellite systems suffer from a significant performance degradation due to the fact that the line of sight to the satellites is not available. The proposed system employs the received signal strength indicator from multiple anchor nodes from an operating wireless sensor network (WSN). In addition, we place multiple receivers around the users body and thanks to machine learning techniques; we are able to estimate the distance and angle between the user and any of the anchor nodes of the WSN. This allows us to estimate the heading of the user without the use of inertial sensors or magnetometers. Finally, the users position estimation is refined using an extended Kalman filter that considers the constant velocity kinematic model. The system has been validated in multiple real scenarios obtaining a root mean squared error around the meter for the different tests performed.

Advanced pedestrian positioning system to smartphones and smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user’s position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2.

A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications.

Joint routing and transmission power control for Collection Tree Protocol in WSN

Wireless sensor networks (WSNs) have strict energy consumption requirements. Moreover, the complexity constraints of the nodes and the wireless dynamics must be considered in real life implementations. CTP (Collection Tree Protocol) is a state-of-the-art routing protocol that considers the main issues that arise in practical WSN applications. Nowadays, commercial wireless sensors can adjust their transmission power to reduce the energy consumption and the collision probability of the network. Since nodes in CTP transmit at a predefined power, the reliability and the lifetime of the network may be reduced. To solve this, this paper proposes an alternative routing metric for CTP, referred to as MaxPDR, that includes a transmission power control in the routing process. With this strategy, the incompatibility issues and the additional signaling that may arise with the combination of individual techniques are avoided. MaxPDR has been implemented in commercial motes to evaluate its performance and compare it with the routing metrics used by the original CTP and ZigBee.

Autonomous Car Parking System through a Cooperative Vehicular Positioning Network

The increasing development of the automotive industry towards a fully autonomous car has motivated the design of new value-added services in Vehicular Sensor Networks (VSNs). Within the context of VSNs, the autonomous car, with an increasing number of on-board sensors, is a mobile node that exchanges sensed and state information within the VSN. Among all the value added services for VSNs, the design of new intelligent parking management architectures where the autonomous car will coexist with traditional cars is mandatory in order to profit from all the opportunities associated with the increasing intelligence of the new generation of cars. In this work, we design a new smart parking system on top of a VSN that takes into account the heterogeneity of cars and provides guidance to the best parking place for the autonomous car based on a collaborative approach that searches for the common good of all of them measured by the accessibility rate, which is the ratio of the free parking places accessible for an autonomous car. Then, we simulate a real parking lot and the results show that the performance of our system is close to the optimum considering different communication ranges and penetration rates for the autonomous car.

Data Aggregation and Principal Component Analysis in WSNs

Data aggregation plays an important role in wireless sensor networks (WSNs) as far as it reduces power consumption and boosts the scalability of the network, especially in topologies that are prone to bottlenecks (e.g. cluster-trees). Existing works in the literature use clustering approaches, principal component analysis (PCA) and/or compressed sensing (CS) strategies. Our contribution is aligned with PCA and explores whether a projection basis that is not the eigenvectors basis may be valid to sustain a normalized mean squared error (NMSE) threshold in signal reconstruction and reduce the energy consumption. We derivate first the NSME achieved with the new basis and elaborate then on the Jacobi eigenvalue decomposition ideas to propose a new subspace-based data aggregation method. The proposed solution reduces transmissions among the sink and one or more data aggregation nodes (DANs) in the network. In our simulations, we consider without loss of generality a single cluster network and results show that the new technique succeeds in satisfying the NMSE requirement and gets close in terms of energy consumption to the best possible solution employing subspace representations. Additionally, the proposed method alleviates the computational load with respect to an eigenvector-based strategy (by a factor of six in our simulations).

Cooperative interaction among multiple RPL instances in wireless sensor networks

Advanced Wireless Sensor Networks (WSNs) applications may need to develop multiple tasks that involve sensing, processing and gathering data from different sensing units. This heterogeneous data may have multiple and sometimes opposite sets of requirements. In these scenarios, different networking strategies must be combined, and therefore traditional single-tree routing approaches are not efficient. On the contrary, the well-known RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol virtually splits the network into multiple RPL Instances, that transport each kind of data according to its particular objective function. However, this protocol does not define any mechanism to decide the nodes that must belong to each instance, and this decision has a strong impact in the network energy consumption and performance. With this in mind, in this paper we introduce C-RPL (Cooperative-RPL). This creates multiple instances following a cooperative strategy among nodes with different sensing tasks. As a result, the energy consumption, the complexity and the cost of the nodes is reduced compared to RPL, since they are active less time, perform fewer tasks and are equipped with less sensing hardware. In this paper we also propose a novel fairness analysis for networks with multiple instances, showing that C-RPL achieves a better tradeoff, in terms of performance and energy consumption, than RPL with non-cooperative instances.