Network-aware design-space exploration of a power-efficient embedded application

The paper presents the design and multi-parameter optimization of a networked embedded application for the health-care domain. Several hardware, software, and application parameters, such as clock frequency, sensor sampling rate, data packet rate, are tuned at design- and run-time according to application specifications and operating conditions to optimize hardware requirements, packet loss, power consumption. Experimental results show that further power efficiency can be achieved by considering also communication aspects during design space exploratio

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

SensEH: From Simulation to Deployment of Energy Harvesting Wireless Sensor Networks

Energy autonomy and system lifetime are critical concerns in wireless sensor networks (WSNs), for which energy harvesting (EH) is emerging as a promising solution. Nevertheless,the tools supporting the design of EH-WSN are limited to a few simulators that require developers to re-implement the application with programming languages different from WSN ones. Further, simulators notoriously provide only a rough approximation of the reality of low-power wireless communication. In this paper we present SENSEH, a software framework that allows developers to move back and forth between the power and speed of a simulated approach and the reality and accuracy of in-field experiments. SENSEH relies on COOJA for emulating the actual, deployment-ready code, and provides two modes of operation that allow the reuse of exactly the same code in realworld WSN deployments. We describe the toolchain and software architecture of SENSEH, and demonstrate its practical use and benefits in the context of a case study where we investigate how the lifetime of a WSN used for adaptive lighting in road tunnels can be extended using harvesters based on photovoltaic panels

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors

A method for modeling the battery state of charge in wireless sensor networks

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper we propose a method for obtaining an analytic model of the battery State-of-Charge (SoC) in wireless sensor nodes. The objective is to find simple models that can be used to estimate accurately the real battery state and consequently the node lifetime. Running the model in the network nodes, we can provide the motes with the required information to implement applications that can be considered as battery-aware. The proposed methodology reduces the computational complexity of the model avoiding complicated electrochemical simulations and treating the battery as an unknown system with an output that can be predicted using simple mathematical models. At a first stage, during a setup period, the method starts with the measurement of several battery parameters under different environmental and operational conditions. After that, the method uses the previous collected data for building mathematical models, considering the linear regression or the multilayer perceptron as the most appropriated. Finally, the models are validated experimentally with new measures. Results show the suitability of the method that produces accurate and simple models, capable of being implemented even in low-cost and very constrained real motesThis work was supported by the I+D+i Program, Generalitat Valenciana, through the Research and Development under Project GV05/043 and in part by the Vicerrectorado of Investigation, Development and Innovation, Universidad Politecnica de Valencia, under Grant PAID-06-06-002-61.Lajara Vizcaíno, JR.; Pérez Solano, JJ.; Pelegrí Sebastiá, J. (2015). A method for modeling the battery state of charge in wireless sensor networks. IEEE Sensors Journal. 15(2):1186-1197. https://doi.org/10.1109/JSEN.2014.2361151S1186119715

On battery recovery effect in wireless sensor nodes

With the perennial demand for longer runtime of battery-powered Wireless Sensor Nodes (WSNs), several techniques have been proposed to increase the battery runtime. One such class of techniques exploiting the battery recovery effect phenomenon claims that performing an intermittent discharge instead of a continuous discharge will increase the usable battery capacity. Several works in the areas of embedded systems and wireless sensor networks have assumed the existence of this recovery effect and proposed different power management techniques in the form of power supply architectures (multiple battery setup) and communication protocols (burst mode transmission) in order to exploit it. However, until now, a systematic experimental evaluation of the recovery effect has not been performed with real battery cells, using high accuracy battery testers to confirm the existence of this recovery phenomenon. In this paper, a systematic evaluation procedure is developed to verify the existence of this battery recovery effect. Using our evaluation procedure we investigated Alkaline, Nickel-Metal Hydride (NiMH) and Lithium-Ion (Li-Ion) battery chemistries, which are commonly used as power supplies for WSN applications. Our experimental results do not show any evidence of the aforementioned recovery effect in these battery chemistries. In particular, our results show a significant deviation from the stochastic battery models, which were used by many power management techniques. Therefore, the existing power management approaches that rely on this recovery effect do not hold in practice. Instead of a battery recovery effect, our experimental results show the existence of the rate capacity effect, which is the reduction of usable battery capacity with higher discharge power, to be the dominant electrochemical phenomenon that should be considered for maximizing the runtime of WSN applications. We outline power management techniques that minimize the rate capacity effect in order to obtain a higher energy output from the battery

Mapping SysML to modelica to validate wireless sensor networks non-functional requirements

International audienceWireless Sensor Networks (WSN) have registered a large success in the scientific and industrial communities for their broad application domains. Furthermore, the WSN specification is a complex task considering to their distributed and embedded nature and the strong interactions between their hardware and software parts. Moreover, most of approaches use semi-formal methods to design systems and generally simulation to validate their properties in order to produce models without errors and conform to the system specifications. In this context, we propose a Model Driven Architecture (MDA) approach to improve the verification of the WSN properties. This approach combines the advantages of the System Modeling Language (SysML) and the Modelica language which promote the reusability and improve the development process. In this work, we specify a model transformation from SysML static, dynamic and requirement diagrams to their corresponding elements in Modelica. Thanks to the SysML requirement diagram which is transformed into Modelica properties (constraints), we propose a technique using dynamic tests to verify WSN properties. We have used the Topcased platform to implement our approach 1 and chosen a crossroads monitoring system which is based on wireless sensors to illustrate it. Besides, we have verified and validated some wireless sensors properties of the studied system

SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field