Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

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

RSSI-based Localization Algorithms using Spatial Diversity in Wireless Sensor Networks

Accepted for publication in International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC)International audienceMany localization algorithms in Wireless Sensor Networks (WSNs) are based on received signal strength indication (RSSI). Although they present some advantages in terms of complexity and energy consumption, RSSI values, especially in indoor environments, are very unstable due to fading induced by shadowing effect and multipath propagation. In this paper, we propose a comparative study of RSSI-based localization algorithms using spatial diversity in WSNs. We consider different kinds of single / multiple antenna systems: Single Input Single Output (SISO) system, Single Input Multiple Output (SIMO) system, Multiple Input Single Output (MISO) system and Multiple Input Multiple Output (MIMO) system. We focus on the well known trilateration and multilateration localization algorithms to evaluate and compare different antenna systems. Exploiting spatial diversity by using multiple antenna systems improve significantly the accuracy of the location estimation. We use three diversity combining techniques at the receiver: Maximal Ratio Combiner (MRC), Equal Gain Combining (EGC) and Selection Combining (SC). The obtained results show that the localization performance in terms of position accuracy is improved when using multiple antennas. Specifically, using multiple antennas at the both sides present better performance than using multiple antennas at the transmitter as well as the receiver side. We also conclude that MRC diversity combining technique outperforms EGC that as well outperforms SC

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Smart FRP Composite Sandwich Bridge Decks in Cold Regions

INE/AUTC 12.0