Adaptive Energy Efficient Scheduling (AEES) for Fault Tolerant Coverage in Sensor Networks

For many sensor network applications it is necessary to provide full sensing coverage to a security-sensitive area. To actively monitor the set of target the subset of sensors are redundantly deployed. One of the major challenges in devising such network lies in the constrained energy and to tolerate unexpected failure to prolong the life span of the network. In this we rapidly restore the field monitoring, by periodically refreshing and switching the cover to tackle unanticipated failure in an energy efficient manner, because energy is the most critical resource considering the irreplaceable of batteries of the sensor nodes. In the same time it should amenably support more than one sensor at a time with different degree in distributed approach that periodically selects the covers and switch between them to extend coverage time and tolerate unexpected failures at runtime. In this scheme the sensor is an autonomous system that has the authority to decide how to cover its sensing range. It also incorporates a novel technique for offline cover update (OCU) to facilitate asynchronous transition between covers. This approach is robust to failure pattern is no uniform. DOI: 10.17762/ijritcc2321-8169.15013

Fault-tolerant Coverage in Dense Wireless Sensor Networks

In this paper, we present methods to detect and recover from sensor failure in dense wireless sensor networks. In order to extend the lifetime of a sensor network while maintaining coverage, a minimal subset of the deployed sensors are kept active while the other sensors can enter a low power sleep state. Several distributed algorithms for coverage have been proposed in the literature. Faults are of particular concern in coverage algorithms since sensors go into a sleep state in order to conserve battery until woken up by active sensors. If these active sensors were to fail, this could lead to lapses in coverage that are unacceptable in critical applications. Also, most algorithms in the literature rely on an active sensor that is about to run out of battery waking up its neighbors to trigger a reshuffle in the network. However, this would not work in the case of unexpected failures since a sensor cannot predict the occurrence of such an event. We present detection and recovery from sensor failure in dense networks. Our algorithms exploit the density in the recovery scheme to improve coverage by 4-12% in the event of random failures. This fault tolerance comes at a small cost to the network lifetime with observed lifetime being reduced by 6-10% in our simulation studies

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