Context-Capture Multi-Valued Decision Fusion With Fault Tolerant Capability For Wireless Sensor Networks

Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in practice, many applications need to process multi-valued data, such as temperature and reflection level used for lightening prediction. To address these challenges, the Markov modulated Poisson process (MMPP) and multi-valued logic are introduced into WSNs to perform context-capture multi-valued decision fusion. The overall decision fusion is decomposed into two parts. The first part is the context-capture model for WSNs using superposition MMPP. Through this procedure, the fusion center has a higher probability to get useful local decisions from sensors. The second one is focused on multi-valued decision fusion. Fault detection can also be performed based on MVL. Once the fusion center detects the faulty nodes, all their local decisions are removed from the computation of the likelihood ratios. Finally, we evaluate the capability of context-capture and fault tolerant. The result supports the usefulness of our scheme.Comment: 13 pages, 7 figure

Time constrained fault tolerance and management framework for k-connected distributed wireless sensor networks based on composite event detection

Wireless sensor nodes themselves are exceptionally complex systems where a variety of components interact in a complex way. In enterprise scenarios it becomes highly important to hide the details of the underlying sensor networks from the applications and to guarantee a minimum level of reliability of the system. One of the challenges faced to achieve this level of reliability is to overcome the failures frequently faced by sensor networks due to their tight integration with the environment. Failures can generate false information, which may trigger incorrect business processes, resulting in additional costs. Sensor networks are inherently fault prone due to the shared wireless communication medium. Thus, sensor nodes can lose synchrony and their programs can reach arbitrary states. Since on-site maintenance is not feasible, sensor network applications should be local and communication-efficient self-healing. Also, as per my knowledge, no such general framework exist that addresses all the fault issues one may encounter in a WSN, based on the extensive, exhaustive and comprehensive literature survey in the related areas of research. As one of the main goals of enterprise applications is to reduce the costs of business processes, a complete and more general Fault Tolerance and management framework for a general WSN, irrespective of the node types and deployment conditions is proposed which would help to mitigate the propagation of failures in a business environment, reduce the installation and maintenance costs and to gain deployment flexibility to allow for unobtrusive installation

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A New Method for Node Fault Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node's failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly

The Distributed Convergence Classifier Using the Finite Difference

The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Increasing communication reliability in manufacturing environments

This paper is concerned with low cost mechanisms that can increase reliability of machine to machine and machine to cloud communications in increasingly complex manufacturing environments that are prone to disconnections and faults. We propose a novel distributed and cooperative sensing framework that supports localized real time predictive analytics of connectivity patterns and detection of a range of faults together with issuing of notifications and responding on demand queries. We show that our Fault and Disconnection Aware Smart Sensing (FDASS) framework achieves significantly lower packet loss rates and communication delays in the face of unreliable nodes and networks when compared to the state of the art and benchmark approaches