Javier Vales-Alonso

Technical Communication: Angle-of-arrival localization based on antenna arrays for wireless sensor networks

Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices. A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.

Simulation scalability issues in wireless sensor networks

The formidable growth of WSN research has opened challenging issues about their performance evaluation. Despite the steady increase in mathematical analysis and experimental deployments, most of the community has chosen simulation for their study. Although it seems straightforward, this approach becomes quite a delicate matter. Complexity is caused by several issues. First, the large number of nodes heavily impacts simulation performance and scalability. Second, credible results demand an accurate characterization of the sensor radio channel. New aspects inherent in WSN must be included in simulators (e.g., a physical environment and an energy model), leading to different degrees of accuracy vs. performance. Moreover, many necessary models are in the continuous time domain (e.g., heat transmission, battery discharge), and thus complex to integrate into discrete event network simulators. These issues result in exponential growth of overall network state information. Through this survey we review these problems both quantitatively and qualitatively while depicting a common suitable simulation model. We also briefly describe the most significant simulation frameworks available.

An accurate radio channel model for wireless sensor networks simulation

Simulations are currently an essential tool to develop and test wireless sensor networks (WSNs) protocols and to analyze future WSNs applications performance. Researchers often simulate their proposals rather than deploying high-cost test-beds or develop complex mathematical analysis. However, simulation results rely on physical layer assumptions, which are not usually accurate enough to capture the real behavior of a WSN. Such an issue can lead to mistaken or questionable results. Besides, most of the envisioned applications for WSNs consider the nodes to be at the ground level. However, there is a lack of radio propagation characterization and validation by measurements with nodes at ground level for actual sensor hardware. In this paper, we propose to use a low-computational cost, two slope, log-normal path-loss near ground outdoor channel model at 868 MHz in WSN simulations. The model is validated by extensive real hardware measurements obtained in different scenarios. In addition, accurate model parameters are provided. This model is compared with the well-known one slope path-loss model. We demonstrate that the two slope log-normal model provides more accurate WSN simulations at almost the same computational cost as the single slope one. It is also shown that the radio propagation characterization heavily depends on the adjusted model parameters for a target deployment scenario: The model parameters have a considerable impact on the average number of neighbors and on the network connectivity.

Wireless communications deployment in industry: a review of issues, options and technologies

Present basis of knowledge management is the efficient share of information. The challenges that modern industrial processes have to face are multimedia information gathering and system integration, through large investments and adopting new technologies. Driven by a notable commercial interest, wireless networks like GSM or IEEE 802.11 are now the focus of industrial attention, because they provide numerous benefits, such as low cost, fast deployment and the ability to develop new applications. However, wireless nets must satisfy industrial requisites: scalability, flexibility, high availability, immunity to interference, security and many others that are crucial in hazardous and noisy environments. This paper presents a thorough survey of all this requirements, reviews the existing wireless solutions, and explores possible matching between industry and the current existing wireless standards.

Multiframe Maximum-Likelihood Tag Estimation for RFID Anticollision Protocols

Automatic identification based on radio frequency identification (RFID) is progressively being introduced into industrial environments, enabling new applications and processes. In the context of communications, RFID rely mostly on Frame Slotted Aloha (FSA) anticollision protocols. Their goal is to reduce the time required to detect all the tags within range (identification time). Using FSA, the maximum identification rate is achieved when the number of contending tags equals the number of contention slots available in the frame. Therefore, the reader must estimate the number of contenders and allocate that number of slots for the next frame. This paper introduces the new MFML-DFSA anticollision protocol. It estimates the number of contenders by means of a maximum-likelihood estimator, which uses the statistical information from several frames (multiframe estimation) to improve the accuracy of the estimate. Based on this expected number of tags, the algorithm determines the best frame length for the next reading frame, taking into account the constraints of the EPCglobal Class-1 Gen-2 standard. The MFML-DFSA algorithm is compared with previous proposals and found to outperform these in terms of (lower) average identification time and computational cost, which makes it suitable for implementation in commercial RFID readers.

Analysis of DFSA anti-collision protocols in passive RFID environments

Frame slotted Aloha (FSA) protocols are promising anti-collision protocols for passive RFID systems. They aim at decreasing the time to detect all the tags in range (identification delay). In FSA, the maximum identification rate (average number of tags identified per slot) is achieved when the number of contending tags matches the cycle length (number of slots in a frame). Therefore, the reader should ideally know the actual number of competing tags. However, in RFID scenarios this figure varies randomly, and the reader has to guess the number of contenders somehow. This paper analyzes the most relevant anti-collision algorithms; taking into account the limitations imposed by the world-wide de-facto standard EPCglobal Class-1 Gen-2 for passive RFID systems.

Control-based scheduling with QoS support for vehicle to infrastructure communications

This article is focused on data transmission scheduling in V2I communications, where a central station, the roadside beacon, decides how to allocate system resources among the vehicles under coverage. We consider non-safety applications whose commercial appeal is expected to accelerate the deployment of VANETs. In this case the main objective is to deliver as much information as possible during the connection lifetime of the vehicles, which is limited by their speed and the length of the road sections under coverage. In this environment the contention free poll-based access mechanism of the 802.11e standard included in current VANET specifications is especially suitable. The design of a scheduling mechanism is addressed in this article from a control theory point of view with the additional novelty of using an optimal control formulation comprising resource constraints. This design strategy allows QoS differentiation, assuring a fixed amount of bandwidth for each QoS class. The resulting algorithm not only maximizes the amount of data delivered, but also reduces performance differences between users traveling along different roads.

Performance evaluation of MAC transmission power control in wireless sensor networks

In this paper we provide a method to analytically compute the energy saving provided by the use of transmission power control (TPC) at the MAC layer in wireless sensor networks (WSN). We consider a classical TPC mechanism: data packets are transmitted with the minimum power required to achieve a given packet error probability, whereas the additional MAC control packets are transmitted with the nominal (maximum) power. This scheme has been chosen because it does not modify the network topology, since control packet transmission range does not change. This property also allows us to analytically compute the expected energy savings. Besides, this type of TPC can be implemented in the current sensor hardware, and it can be directly applied to several MAC protocols already proposed for WSN. The foundation of our analysis is the evaluation of L ratio, defined as the total energy consumed by the network using the original MAC protocol divided by the total energy consumed if the TPC mechanism is employed. In the L computation we emphasize the basic properties of sensor networks. Namely, the savings are calculated for a network that is active for a very long time, and where the number of sensors is supposed to be very large. The nodes position is assumed to be random - a normal bivariate distribution is assumed in the paper - and no node mobility is considered. In the analysis we stress the radio propagation and the distribution of the nodes in the network, which will ultimately determine the performance of the TPC. Under these conditions we compute the mean value of L. Finally, we have applied the method to evaluate the benefits of TPC for TDMA and CSMA with two representative protocols, L-MAC and S-MAC using their implementation reference parameters. The conclusion is that, while S-MAC does not achieve a significant improvement, L-MAC may reach energy savings up to 10-20%.

Condor grid computing from mobile handheld devices

In this paper, we propose a hierarchical design methodology for grid access from handheld devices. After determining all user interactions required and technologies available, they are arranged in layers. All functions in a layer are also supported by all underlying layers. By doing so, the designer is less conditioned by the constraints of a specific, out-of-context platform. Additionally, in a stratified modular design, many software components can be re-used. We present a prototype to access Condor from two neighbor layers: PDAs and cell phones.

Ambient Intelligence Systems for Personalized Sport Training

Several research programs are tackling the use of Wireless Sensor Networks (WSN) at specific fields, such as e-Health, e-Inclusion or e-Sport. This is the case of the project “Ambient Intelligence Systems Support for Athletes with Specific Profiles”, which intends to assist athletes in their training. In this paper, the main developments and outcomes from this project are described. The architecture of the system comprises a WSN deployed in the training area which provides communication with athletes’ mobile equipments, performs location tasks, and harvests environmental data (wind speed, temperature, etc.). Athletes are equipped with a monitoring unit which obtains data from their training (pulse, speed, etc.). Besides, a decision engine combines these real-time data together with static information about the training field, and from the athlete, to direct athletes’ training to fulfill some specific goal. A prototype is presented in this work for a cross country running scenario, where the objective is to maintain the heart rate (HR) of the runner in a target range. For each track, the environmental conditions (temperature of the next track), the current athlete condition (HR), and the intrinsic difficulty of the track (slopes) influence the performance of the athlete. The decision engine, implemented by means of (m, s)-splines interpolation, estimates the future HR and selects the best track in each fork of the circuit. This method achieves a success ratio in the order of 80%. Indeed, results demonstrate that if environmental information is not take into account to derive training orders, the success ratio is reduced notably.