Pedro Mestre

A ZigBee Sensor Element for Distributed Monitoring of Soil Parameters in Environmental Monitoring

This work describes the development and implementation of a grid of self-powered multi-functional probes (MFPz) for small-scale measurements of different soil properties, as being part of a wireless sensor network. The measurement principle is based on the heat-pulse method for soil moisture and water flux measurements and in a Wenner array for soil electrical conductivity. To promote the deployment of these sensing devices across large areas, such as irrigation fields, the ZigBee standard has been adopted as a multi-hop, ad-hoc network enabler. The core of the MFPz device is a wireless microcontroller (with a built-in ZigBee stack) that builds upon an IEEE 802.15.4 radio device. A 7.2 Ah NiHM battery that is charged by a solar panel powers the MFPz device. Experimental results have proofed the reliability of the MFPz, regarding power consumption, connectivity and data agreement with known soil samples, as a cost-effective solution for environment monitoring.

Hybrid technique for fingerprinting using IEEE802.11 wireless networks

Location based on fingerprinting comprises two distinct phases: the first phase, off-line phase, is related with two tasks, a site survey to collect data for the Fingerprint Map (FM), and its generation based on the collected data; the second phase, on-line phase, is related with the location of a mobile terminal, by doing the comparison of the data acquired from the wireless transceiver with the data stored in FM. Typically the first phase is very time consuming because multiple samples of the wireless signals, received from the multiple references, must be acquired and stored, for every spatial point to be considered in the FM. In this paper a hybrid approach using propagation models for indoor environments and fingerprinting is presented. In this approach the FM is generated using indoor propagation models and the map of the scenario, instead of requiring a site survey. This makes the generation of the FM faster when compared with the traditional approach. Also when changes occur the FM can be easily recalculated. Several Location Estimation Algorithms were used to test the FM. A precision of 3.118m was achieved using k-Nearest Neighbour and a standard deviation of 0.133m was obtained using Fuzzy Logic.

Indoor Location Using Fingerprinting and Fuzzy Logic

Indoor location systems cannot rely on technologies such as GPS (Global Positioning System) to determine the position of a mobile terminal, because its signals are blocked by obstacles such as walls, ceilings, roofs, etc. In such environments the use of alternative techniques, such as the use of wireless networks, should be considered. The location estimation is made by measuring and analysing one of the parameters of the wireless signal, usually the received power. One of the techniques used to estimate the locations using wireless networks is fingerprinting. This technique comprises two phases: in the first phase data is collected from the scenario and stored in a database; the second phase consists in determining the location of the mobile node by comparing the data collected from the wireless transceiver with the data previously stored in the database. In this paper an approach for localisation using fingerprinting based on Fuzzy Logic and pattern searching is presented. The performance of the proposed approach is compared with the performance of classic methods, and it presents an improvement between 10.24% and 49.43%, depending on the mobile node and the Fuzzy Logic parameters.

Derivative-free nonlinear optimization filter simplex

Derivative-free nonlinear optimization filter simplex The filter method is a technique for solving nonlinear programming problems. The filter algorithm has two phases in each iteration. The first one reduces a measure of infeasibility, while in the second the objective function value is reduced. In real optimization problems, usually the objective function is not differentiable or its derivatives are unknown. In these cases it becomes essential to use optimization methods where the calculation of the derivatives or the verification of their existence is not necessary: direct search methods or derivative-free methods are examples of such techniques. In this work we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of simplex and filter methods. This method neither computes nor approximates derivatives, penalty constants or Lagrange multipliers.

Second Life Information Desk System Using Instant Messaging and Short Messaging Service Technologies

Organizations with a presence in the Second Life® world typically only provide direct user interaction with staff at specific schedules, or not at all. We present a system that provides organizations with a simple way to enable constant interaction with users of the Second Life world, by simulating staff presence using automated avatars as communication channels to real-life staff by means of instant messaging and short message service technologies. Staff members are assigned to communication with Second Life avatars based on a hierarchy of information desk staffing priorities, and communication is bidirectional.

Penalty fuzzy function for derivative-free optimization

Penalty and Barrier methods are normally used to solve Nonlinear Optimization Constrained Problems. The problems appear in areas such as engineering and are often characterized by the fact that involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. This means that optimization methods based on derivatives cannot be used. A Java based API was implemented, including only derivative-free optimization methods, to solve both constrained and unconstrained problems, which includes Penalty and Barriers methods. In this work a new penalty function, based on Fuzzy Logic, is presented. This function imposes a progressive penalization to solutions that violate the constraints. This means that the function imposes a low penalization when the violation of the constraints is low and a heavy penalization when the violation is high. The value of the penalization is not known in beforehand, it is the outcome of a fuzzy inference engine. Numerical results comparing the proposed function with two of the classic penalty/barrier functions are presented. Regarding the presented results one can conclude that the proposed penalty function besides being very robust also exhibits a very good performance.

Derivative-free optimization and filter methods to solve nonlinear constrained problems

In real optimization problems, usually the analytical expression of the objective function is not known, nor its derivatives, or they are complex. In these cases it becomes essential to use optimization methods where the calculation of the derivatives, or the verification of their existence, is not necessary: the Direct Search Methods or Derivative-free Methods are one solution. When the problem has constraints, penalty functions are often used. Unfortunately the choice of the penalty parameters is, frequently, very difficult, because most strategies for choosing it are heuristics strategies. As an alternative to penalty function appeared the filter methods. A filter algorithm introduces a function that aggregates the constrained violations and constructs a biobjective problem. In this problem the step is accepted if it either reduces the objective function or the constrained violation. This implies that the filter methods are less parameter dependent than a penalty function. In this work, we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of the simplex method and filter methods. This method does not compute or approximate any derivatives, penalty constants or Lagrange multipliers. The basic idea of simplex filter algorithm is to construct an initial simplex and use the simplex to drive the search. We illustrate the behavior of our algorithm through some examples. The proposed methods were implemented in Java.

A Fuzzy Logic Approach to Indoor Location Using Fingerprinting

From emergency location systems based on mobile networks, such as E911 (Enhanced 911), to the latest concept of applications that are adapted for the end-user and are dynamically delivery based on the user’s location [2], all the Location Based Services (LBS) depend on the correct estimation of the users’ location.

Classification of Some Penalty Methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem.

Calibration Procedures for Indoor Location Using Fingerprinting

Fingerprinting is a location technique, based on the use of wireless networks, where data stored during the offline phase is compared with data collected by the mobile node during the online phase. When this location technique is used in a real-life scenario there is a high probability that the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node being located and the ones previously stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.