Alberto Garcia-Ortiz

Automatic design of low-power encoders using reversible circuit synthesis

The application of coding strategies is an established methodology to improve the characteristics of on-chip interconnect architectures. Therefore, design methods are required which realize the corresponding encoders and decoders with as small as possible overhead in terms of power and delay. In the past, conventional design methods have been applied for this purpose.

A review on wireless sensor network for water pipeline monitoring applications

Wireless sensor networks (WSNs) have gained a lot of attention from researchers both from academia and industry during the past decade. This key technology enables a wide range of potential applications and services including monitoring of physical environments, enhanced industrial control, surveillance, remote health care and logistics. Real-time monitoring of water pipeline network is one such application where WSN plays significant role. The issue of water is considered to be one of the largest and most serious challenges. It is expected to aggravate over time, given the scarcity of available traditional water resources and the massive costs of providing fresh potable water from non-traditional sources such as desalination plants. Therefore, a robust and reliable WSN technique is required to monitor leaks, bursts and other anomalies in the water pipeline systems. This paper presents a consolidated review on WSN for water pipeline monitoring applications.

Low-Power Coding for Networks-on-Chip with Virtual Channels

Power consumption represents a major concern for Networks-on-Chip (NoC). In order to provide quality-of-service to such NoCs, Virtual Channels are normally used. A drawback of the approach is the increased power consumption because of the suppression of correlation between consecutive flits. This work proposes a low-power coding approach to overcome the aforementioned problem. The technique requires a minimum overhead, while obtaining a significant power reduction. Exhaustive experimental simulations are provided to demonstrate the advantages of the proposed architecture.

Synthesis of approximate coders for on-chip interconnects using reversible logic

On-chip coding provides a remarkable potential to improve the energy efficiency of on-chip interconnects. However, the logic design of the encoder/decoder faces a main challenge: the area and power overhead should be minimal while, at the same time, decodability has to be guaranteed. To address these problems, we propose the concept of approximate coding, where the coding function is partially specified and the synthesis algorithm has a higher flexibility to simplify the circuit. Since conventional synthesis methods are unsuitable here, we propose an alternative synthesis approach based on reversible logic. Experimental evaluations confirm the benefits of both, the proposed concept of approximate codings as well as the proposed design method.

An optimisation algorithm for minimising energy dissipation in NoC-based hard real-time embedded systems

This paper introduces an evolutionary multi-objective optimisation algorithm to facilitate fast and efficient task allocation of hard real-time embedded systems with Networks-On-Chip (NoC) as the interconnection at the early design stage, where evaluating as many as possible solutions is crucial. Our approach uses analytical fitness functions to provide fast evaluation of large number of solutions; a contrast to simulation-based optimisation technique, whereby it tends to be not only impractical when the design space is very large but also unfeasible as far as hard real-time systems are concerned. The proposed algorithm guarantees the predictability in timing behaviour of the systems whilst minimising energy dissipation whenever tasks are reallocated and their packets are rerouted, which differs from the state-of-the-art approaches. In addition, not only it can explore the allocation of tasks but also the encoding of the data packets. The evidence gathered from case studies shows that the proposed algorithm is able to find schedulable allocation of tasks, preserving it whilst further minimising energy dissipation.

Optimization of interconnect architectures through coding: A review

Interconnects are becoming a major bottleneck in the design of modern Systems-on-Chip. Power consumption, propagation delay, statistical variability, and reliability are some of the key challenges that must be addressed to fully optimize the interconnect architecture. This work provides a thorough review and analysis of the advantages and possibilities of coding for addressing the aforementioned challenges during the optimization of the communication architecture of a System-on-Chip. An outlook into future research directions is also presented.

Analysis of PKF: A Communication Cost Reduction Scheme for Wireless Sensor Networks

Energy efficiency is a primary concern for wireless sensor networks (WSNs). One of its most energy-intensive processes is the radio communication. This work uses a predictor combined with a Kalman filter (KF) to reduce the communication energy cost for cluster-based WSNs. The technique, called PKF, is suitable for typical WSN applications with adjustable data quality and tens of picojoule computation cost. However, it is challenging to precisely quantify its underlying process from a mathematical point of view. Through an in-depth mathematical analysis, we formulate the tradeoff between energy efficiency and reconstruction quality of PKF. One of our prominent results for that is the explicit expression for the covariance of the doubly truncated multivariate normal distribution; it improves the previous methods and has generality. The validity and accuracy of the analysis are verified with both artificial and real signals. The simulation results, using real temperature values, demonstrate the efficiency of PKF: without additional data degradation, it reduces the communication cost by more than 88%. Compared to previous works based on KF, PKF requires less computational effort while improving the reconstruction quality; compared with the techniques without KF, the advantages of PKF are even more significant. It reduces the transmission rate of them by at least 29%. Besides, it can be integrated into network level techniques to further extend the whole network lifetime.

Moment-Based Power Estimation in Very Deep Submicron Technologies

The significant power optimization possibilities in the early stagesof the design flow advice the use of energy evaluation techniquesat high levels of abstraction. With this aim, the present work addressesthe estimation of the energy consumption in very deep submicrontechnologies. Using the characterization of the probabilitydensity function with a projection in an orthogonal polynomialbase, and a symbolic propagation mechanism, a technique is presentedto estimate the dynamic and static power consumption indigital systems. The proposed approach has been validated withcircuits and excitations from realistic applications. Comparisonswith reference transistor and bit level simulations are reported inorder to asses the the accuracy of the technique.

EARNPIPE: A Testbed for Smart Water Pipeline Monitoring Using Wireless Sensor Network☆

Abstract Large quantities of water are wasted daily due to leakages in pipelines. In order to decrease this waste and preserve water, advanced systems could be used. In this context, a Wireless Sensor Network (WSN) is increasingly required to optimize the reliability of the inspection and improve the accuracy of the water pipeline monitoring. A WSN solution is proposed in this paper with a view to detecting and locating leaks for long distance pipelines. It combines powerful leak detection and localization algorithms and an efficient wireless sensor node System on Chip (SoC) architecture. In fact, a novel hybrid Water Pipeline Monitoring (WPM) method has been proposed using Leak detection Predictive Kalman Filter (LPKF) and Modified Time Difference of Arrival (TDOA) method based on pressure measurements. The data collected from sensors are filtered, analyzed and compressed with the same Kalman Filter (KF) based algorithm instead of using various algorithms that deeply damage the battery of the node. The local low power pre-processing is efficient to save the power of the sensor nodes. Moreover, a laboratory testbed has been constructed using plumbing components and validated by an ARM-based prototyping platform with pressure sensors.

PKF: A communication cost reduction schema based on Kalman filter and data prediction for Wireless Sensor Networks

The implementation of a wireless sensor mote as a system on chip (SoC) enables the energy consumption to be decreased substantially by using dedicated hardware accelerators. This work combines an efficient prediction model with a Kalman filter (PKF) to reduce the communication cost in Wireless Sensor Networks (WSNs) with a guaranteed data quality. The hardware accelerator requires fewer resources than previous approaches, while achieving higher energy reductions. Exhaustive experimental results based on datasets from a real WSN application confirm the advantages of the proposed mechanism.