Implementation of a herd management system with wireless sensor networks

This paper investigates an adaptation of Wireless Sensor Networks (WSNs) to cattle monitoring applications. The proposed solution facilitates the requirement for continuously assessing the condition of individual animals, aggregating and reporting this data to the farm manager. There are several existing approaches to achieving animal monitoring, ranging from using a store and forward mechanism to employing GSM-based techniques; these approaches only provide sporadic information and introduce a considerable cost in staffing and physical hardware. The core of this study is to overcome the aforementioned drawbacks by using alternative cheap, low power consumption sensor nodes capable of providing real-time communication at a reasonable hardware cost. In this paper, both the hardware and software has been designed to provide a solution which can obtain real-time data from dairy cattle whilst conforming to the limitations associated with WSNs implementations

Constraint-based Self-adaptation of Wireless Sensor Networks

International audienceIn recent years, the Wireless Sensor Networks (WSNs) have become a useful mechanism to monitor physical phenomena in environments. The sensors that make part of these long-lived networks have to be reconfigured according to context changes in order to preserve the operation of the network. Such reconfigurations require to consider the distributed nature of the sensor nodes as well as their resource scarceness. Therefore, self-adaptations for WSNs have special requirements comparing with traditional information systems. In particular, the reconfiguration of the WSN requires a trade-off between critical dimensions for this kind of networks and devices, such as resource consumption or reconfiguration cost. Thus, in this paper, we propose to exploit Constraint-Satisfaction Problem (CSP) techniques in order to find a suitable configuration for self-adapting WSNs, modelled using a Dynamic Software Product Line (DSPL), when the context changes. We exploit CSP modeling to find a compromise between contradictory dimensions. To illustrate our approach, we use an Intelligent Transportation System scenario. This case study enables us to show the advantages of obtaining suitable and optimized configurations for self-adapting WSNs

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

A Lightweight Policy System for Body Sensor Networks

Body sensor networks (BSNs) for healthcare have more stringent security and context adaptation requirements than required in large-scale sensor networks for environment monitoring. Policy-based management enables flexible adaptive behavior by supporting dynamic loading, enabling and disabling of policies without shutting down nodes. This overcomes many of the limitations of sensor operating systems, such as TinyOS, which do not support dynamic modification of code. Alternative schemes for adaptation, such as network programming, have a high communication cost and suffer from operational interruption. In addition, a policy-driven approach enables finegrained access control through specifying authorization policies. This paper presents the design, implementation and evaluation of an efficient policy system called Finger which enables policy interpretation and enforcement on distributed sensors to support sensor level adaptation and fine-grained access control. It features support for dynamic management of policies, minimization of resources usage, high responsiveness and node autonomy. The policy system is integrated as a TinyOS component, exposing simple, well-defined interfaces which can easily be used by application developers. The system performance in terms of processing latency and resource usage is evaluated. © 2009 IEEE.Published versio

Blade root moment sensor failure detection based on multibeam LIDAR for fault-tolerant individual pitch control of wind turbines

Detection of blade root moment sensor failures is an important problem for fault-tolerant individual pitch control, which plays a key role in reduction of uneven blade loads of large wind turbines. A new method for detection of blade root moment sensor failures which is based on variations induced by a vertical wind shear is described in this paper. The detection is associated with monitoring of statistical properties of the difference between amplitudes of the first harmonic of the blade load, which is calculated in two different ways. The first method is based on processing of the load sensor signal, which contains a number of harmonics. The first harmonic is recovered via least squares estimation of the blade load signal with harmonic regressor and strictly diagonally dominant (SDD) information matrix. The second method is a model-based method of estimation of the first harmonic, which relies on the blade load model and upwind speed measurements provided by multibeam Light Detection and Ranging (LIDAR). This is a new application for future LIDAR-enabled wind turbine technologies. Moreover, adaptation of the load model in a uniform wind field is proposed. This adaptation improves accuracy of the load estimation and hence the performance of the blade load sensor failure detection method

Towards an Efficient Context-Aware System: Problems and Suggestions to Reduce Energy Consumption in Mobile Devices

Looking for optimizing the battery consumption is an open issue, and we think it is feasible if we analyze the battery consumption behavior of a typical context-aware application to reduce context-aware operations at runtime. This analysis is based on different context sensors configurations. Actually existing context-aware approaches are mainly based on collecting and sending context data to external components, without taking into account how expensive are these operations in terms of energy consumption. As a first result of our work in progress, we are proposing a way for reducing the context data publishing. We have designed a testing battery consumption architecture supported by Nokia Energy Profiler tool to verify consumption in different scenarios