A Comprehensive Experimental Comparison of Event Driven and Multi-Threaded Sensor Node Operating Systems

The capabilities of a sensor network are strongly influenced by the operating system used on the sensor nodes. In general, two different sensor network operating system types are currently considered: event driven and multi-threaded. It is commonly assumed that event driven operating systems are more suited to sensor networks as they use less memory and processing resources. However, if factors other than resource usage are considered important, a multi-threaded system might be preferred. This paper compares the resource needs of multi-threaded and event driven sensor network operating systems. The resources considered are memory usage and power consumption. Additionally, the event handling capabilities of event driven and multi-threaded operating systems are analyzed and compared. The results presented in this paper show that for a number of application areas a thread-based sensor network operating system is feasible and preferable

SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence

Currently, Wireless Sensor Networks (WSNs) are formed by hundreds of low energy and low cost micro-electro-mechanical systems. Routing and low power consumption have become important research issues to interconnect this kind of networks. However, conventional Quality of Service routing models, are not suitable for ad hoc sensor networks, due to the dynamic nature of such systems. This paper introduces a new QoS-driven routing algorithm, named SIR: Sensor Intelligence Routing. We have designed an artificial neural network based on Kohonen self organizing features map. Every node implements this artificial neural network forming a distributed intelligence and ubiquitous computing system

Improved Fair-Zone technique using Mobility Prediction in WSN

The self-organizational ability of ad-hoc Wireless Sensor Networks (WSNs) has led them to be the most popular choice in ubiquitous computing. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. It has some limitation in energy and mobility of nodes. In this paper we propose a mobility prediction technique which tries overcoming above mentioned problems and improves the life time of the network. The technique used here is Exponential Moving Average for online updates of nodal contact probability in cluster based network.Comment: 10 pages, 7 figures, Published in International Journal Of Advanced Smart Sensor Network Systems (IJASSN

Integrating an agent-based wireless sensor network within an existing multi-agent condition monitoring system

The use of wireless sensor networks for condition monitoring is gaining ground across all sectors of industry, and while their use for power engineering applications has yet been limited, they represent a viable platform for next-generation substation condition monitoring systems. For engineers to fully benefit from this new approach to condition monitoring, new sensor data must be incorporated into a single integrated system. This paper proposes the integration of an agent-based wireless sensor network with an existing agent-based condition monitoring system. It demonstrates that multi-agent systems can be extended down to the sensor level while considering the reduced energy availability of low-power embedded devices. A novel agent-based approach to data translation is presented, which is demonstrated through two case studies: a lab-based temperature and vibration monitoring system, and a proposal to integrate a wireless sensor network to an existing technology demonstrator deployed in a substation in the UK

Opportunistic Sensing in Train Safety Systems

Train safety systems are complex and expensive, and changing them requires huge investments. Changes are evolutionary and small. Current developments, like faster - high speed - trains and a higher train density on the railway network, have initiated research on safety systems that can cope with the new requirements. This paper presents a novel approach for a safety subsystem that checks the composition of a train, based on opportunistic sensing with a wireless sensor network. Opportunistic sensing systems consist of changing constellations sensors that, for a limited amount of time, work together to achieve a common goal. Such constellations are selforganizing and come into being spontaneously. The proposed opportunistic sensing system selects a subset of sensor nodes from a larger set based on a common context.We show that it is possible to use a wireless sensor network to make a distinction between carriages from different trains. The common context is acceleration, which is used to select the subset of carriages that belong to the same train out of all the carriages from several trains in close proximity. Simulations based on a realistic set of sensor data show that the method is valid, but that the algorithm is too complex for implementation on simple wireless sensor nodes. Downscaling the algorithm reduces the number of processor execution cycles as well as memory usage, and makes it suitable for implementation on a wireless sensor node with acceptable loss of precision. Actual implementation on wireless sensor nodes confirms the results obtained with the simulations

Pheromone-based In-Network Processing for wireless sensor network monitoring systems

Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.Fil: Riva, Guillermo Gaston. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; Argentin

Distributed data fusion algorithms for inertial network systems

New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and new distributed data fusion algorithms are developed for inertial network systems. The distributed data fusion algorithm comprises two steps: inertial measurement fusion and state fusion. The inertial measurement fusion allows each node to assimilate all the inertial measurements from an inertial network system, which can improve the performance of inertial sensor failure detection and isolation algorithms by providing more information. The state fusion further increases the accuracy and enhances the integrity of the local inertial states and navigation state estimates. The simulation results show that the two-step fusion procedure overcomes the disadvantages of traditional inertial sensor alignment procedures. The slave inertial nodes can be accurately aligned to the master node

Real-Time Analysis of Correlations Between On-Body Sensor Nodes

The topology of a body sensor network has, until recently, often been overlooked; either because the layout of the network is deemed to be sufficiently static (”we always know well enough where sensors are”), we always know exactly where the nodes are or because the location of the sensor is not inherently required (”as long as the node stays where it is, we do not need its location, just its data”). We argue in this paper that, especially as the sensor nodes become more numerous and densely interconnected, an analysis on the correlations between the data streams can be valuable for a variety of purposes. Two systems illustrate how a mapping of the network’s sensor data to a topology of the sensor nodes’ correlations can be applied to reveal more about the physical structure of body sensor networks