Entity localization and tracking: a sensor fusion-based mechanism in WSNs

Knowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.

On the implementation of real-time slotbased task-splitting scheduling algorithms for multiprocessor systems

In this paper we discuss challenges and design principles of an implementation of slot-based tasksplitting algorithms into the Linux 2.6.34 version. We show that this kernel version is provided with the required features for implementing such scheduling algorithms. We show that the real behavior of the scheduling algorithm is very close to the theoretical. We run and discuss experiments on 4-core and 24-core machines.

How a cyber-physical system can efficiently obtain a snapshot of physical information even in the presence of sensor faults

We present a distributed algorithm for cyber-physical systems to obtain a snapshot of sensor data. The snapshot is an approximate representation of sensor data; it is an interpolation as a function of space coordinates. The new algorithm exploits a prioritized medium access control (MAC) protocol to efficiently transmit information of the sensor data. It scales to a very large number of sensors and it is able to operate in the presence of sensor faults.

Monitoring chlorophyll-a with remote sensing techniques in the Tagus Estuary

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Gestão e Sistemas Ambientais

The utilization bound of static-priority preemptive partitioned multiprocessor scheduling is 50%

This paper studies static-priority preemptive scheduling on a multiprocessor using partitioned scheduling. We propose a new scheduling algorithm and prove that if the proposed algorithm is used and if less than 50% of the capacity is requested then all deadlines are met. It is known that for every static-priority multiprocessor scheduling algorithm, there is a task set that misses a deadline although the requested capacity is arbitrary close to 50%.

Improving the IEEE 802.15.4 Slotted CSMA/CA MAC for time-critical events in wireless sensor networks

In beacon-enabled mode, IEEE 802.15.4 is ruled by the slotted CSMA/CA Medium Access Control (MAC) protocol. The standard slotted CSMA/CA mechanism does not provide any means of differentiated services to improve the quality of service for timecritical events (such as alarms, time slot reservation, PAN management messages etc.). In this paper, we present and discuss practical service differentiation mechanisms to improve the performance of slotted CSMA/CA for time-critical events, with only minor add-ons to the protocol. The contribution of our proposal is more practical than theoretical since our initial requirement is to leave the original algorithm of the slotted CSMA/CA unchanged, but rather tuning its parameters adequately according to the criticality of the messages. We present a simulation study based on an accurate model of the IEEE 802.15.4 MAC protocol, to evaluate the differentiated service strategies. Four scenarios with different settings of the slotted CSMA/CA parameters are defined. Each scenario is evaluated for FIFO and Priority Queuing. The impact of the hiddennode problem is also analyzed, and a solution to mitigate it is proposed.

Practical simulation of GNSS signals in the presence of ionospheric scintillaton

We propose a low complexity technique to generate amplitude correlated time-series with Nakagami-m distribution and phase correlated Gaussian-distributed time-series, which is useful in the simulation of ionospheric scintillation effects during the transmission of GNSS signals. The method requires only the knowledge of parameters S4 (scintillation index) and σΦ (phase standard deviation) besides the definition of models for the amplitude and phase power spectra. The Zhang algorithm is used to produce Nakagami-distributed signals from a set of Gaussian autoregressive processes.

Single-objective spreading algorithm

This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design is proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and ε-dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and a function obtained from practical real-world engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed.

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

Approximating fractional derivatives through the generalized mean

This paper addresses the calculation of fractional order expressions through rational fractions. The article starts by analyzing the techniques adopted in the continuous to discrete time conversion. The problem is re-evaluated in an optimization perspective by tacking advantage of the degree of freedom provided by the generalized mean formula. The results demonstrate the superior performance of the new algorithm.

Digital-domain self-calibration technique for video-rate pipeline A/D converters using Gaussian white noise

Electronics Letters Vol.38, nº 19

An optimization approach for the job shop scheduling problem

This paper presents an optimization approach for the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.

Using fractional derivatives for the joint ontrol of hexapod robots

This article studies several Fractional Order Control algorithms used for joint control of a hexapod robot. Both Padé and series approximations to the fractional derivative are considered for the control algorithm. The walking performance is evaluated through two indices: The mean absolute density of energy used per unit distance travelled, and the control effort. A set of simulation experiments reveals the influence of the different approximations upon the proposed indices. The results show that the fractional proportional and derivative algorithm, implemented using the Padé approximation with a small number of terms, gives the best results.