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

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Probabilistic approaches to the design of wireless ad hoc and sensor networks

The emerging wireless technologies has made ubiquitous wireless access a reality and enabled wireless systems to support a large variety of applications. Since the wireless self-configuring networks do not require infrastructure and promise greater flexibility and better coverage, wireless ad hoc and sensor networks have been under intensive research. It is believed that wireless ad hoc and sensor networks can become as important as the Internet. Just as the Internet allows access to digital information anywhere, ad hoc and sensor networks will provide remote interaction with the physical world. Dynamics of the object distribution is one of the most important features of the wireless ad hoc and sensor networks. This dissertation deals with several interesting estimation and optimization problems on the dynamical features of ad hoc and sensor networks. Many demands in application, such as reliability, power efficiency and sensor deployment, of wireless ad hoc and sensor network can be improved by mobility estimation and/or prediction. In this dissertation, we study several random mobility models, present a mobility prediction methodology, which relies on the analysis of the moving patterns of the mobile objects. Through estimating the future movement of objects and analyzing the tradeoff between the estimation cost and the quality of reliability, the optimization of tracking interval for sensor networks is presented. Based on the observation on the location and movement of objects, an optimal sensor placement algorithm is proposed by adaptively learn the dynamical object distribution. Moreover, dynamical boundary of mass objects monitored in a sensor network can be estimated based on the unsupervised learning of the distribution density of objects. In order to provide an accurate estimation of mobile objects, we first study several popular mobility models. Based on these models, we present some mobility prediction algorithms accordingly, which are capable of predicting the moving trajectory of objects in the future. In wireless self-configuring networks, an accurate estimation algorithm allows for improving the link reliability, power efficiency, reducing the traffic delay and optimizing the sensor deployment. The effects of estimation accuracy on the reliability and the power consumption have been studied and analyzed. A new methodology is proposed to optimize the reliability and power efficiency by balancing the trade-off between the quality of performance and estimation cost. By estimating and predicting the mass objects\u27 location and movement, the proposed sensor placement algorithm demonstrates a siguificant improvement on the detection of mass objects with nearmaximal detection accuracy. Quantitative analysis on the effects of mobility estimation and prediction on the accuracy of detection by sensor networks can be conducted with recursive EM algorithms. The future work includes the deployment of the proposed concepts and algorithms into real-world ad hoc and sensor networks

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

Design and analysis of a beacon-less routing protocol for large volume content dissemination in vehicular ad hoc networks

Largevolumecontentdisseminationispursuedbythegrowingnumberofhighquality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well