Data redundancy reduction for energy-efficiency in wireless sensor networks: a comprehensive review

Wireless Sensor Networks (WSNs) play a significant role in providing an extraordinary infrastructure for monitoring environmental variations such as climate change, volcanoes, and other natural disasters. In a hostile environment, sensors' energy is one of the crucial concerns in collecting and analyzing accurate data. However, various environmental conditions, short-distance adjacent devices, and extreme usage of resources, i.e., battery power in WSNs, lead to a high possibility of redundant data. Accordingly, the reduction in redundant data is required for both resources and accurate information. In this context, this paper presents a comprehensive review of the existing energy-efficient data redundancy reduction schemes with their benefits and limitations for WSNs. The entire concept of data redundancy reduction is classified into three levels, which are node, cluster head, and sink. Additionally, this paper highlights existing key issues and challenges and suggested future work in reducing data redundancy for future research

Adaptive Strategy and Decision Making Model for Sensing-Based Network Applications

International audienceEnergy conservation and decision making are at the heart of wireless sensor network (WSN) research. Indeed, the limited power of sensors, which is mostly not rechargeable, accompanied to the dense deployment of sensors, which collect a huge amount of data, complicate the makers' decision. In this paper, we propose an energy-efficient adaptive strategy and decision making technique based on a grid architecture of WSN, where a Grid-Leader (GL) is assigned for each grid. Our technique works on the three tiers of WSN: sensors, grid-leader (GL) and sink. At the first tier, each sensor applies a divide-and-conquer algorithm in order to send a reduced set of data to its appropriate GL; At the second tier, the GL combines data coming from sensors and sends useful information, i.e. satisfying a confidence threshold, about the monitored grid to the sink. The last tier, e.g. sink node, introduces two decision tables (score and early decisions) in order to make a real-time decision for each grid in the network. Extensive simulations on real sensor data demonstrated that our technique can be efficiently saving the network energy and helping in taking decisions, while maintaining an acceptable data accuracy level