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

Energy-efficient adaptive wireless network design

Energy efficiency is an important issue for mobile computers since they must rely on their batteries. We present an energy-efficient highly adaptive architecture of a network interface and novel data link layer protocol for wireless networks that provides quality of service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and an acceptable quality of service. The paper provides a review of ideas and techniques relevant to the design of an energy efficient adaptive wireless networ

Context-aware QoS provisioning for an M-health service platform

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient's vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current supporting mobile service platforms, m-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). In this paper, we argue that the use of context information in an m-health service platform improves the delivered QoS. We give a first attempt to merge context information with a QoS-aware mobile service platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

Effect of Video Streaming Space–Time Characteristics on Quality of Transmission over Wireless Telecommunication Networks

The spate in popularity of multimedia applications has led to the need for optimization of bandwidth allocation and usage in telecommunication networks. Modern telecommunication networks should by their definition be able to maintain the quality of different applications with different Quality of Service (QoS) levels. QoS requirements are generally dependent on the parameters of network and application layers of the OSI model. At the application layer QoS depends on factors such as resolution, bit rate, frame rate, video type, audio codecs, etc. At the network layer, distortions such as delay, jitter, packet loss, etc. are introduced. This paper presents simulation results of modeling video streaming over wireless communications networks. The differences in spatial and time characteristics of the different subject groups were taken into account. Analysis of the influence of bit error rate (BER) and bit rate for video quality is also presented. Simulation showed that different video subject groups affect the perceived quality differently when transmitted over networks. We show conclusively that in a transmission network with a small error probabilities (BER = 10-6, BER = 10-5), the minimum bit rate (128 kbps) guarantees an acceptable video quality, corresponding to MOS > 3 for all types of frames

Evaluation of quality of service in fourth generation wireless and mobile networks

Communication networks extend network capacity and coverage by leveraging network and resource architecture in a dynamic way. However, because of the different communication technologies and quality of service (QoS, managing and monitoring these networks are too difficult. All communication technology has its own characteristics while the applications you use have their own QoS requirements. The methods are based on the QoS analysis for each application or access network separately. However, these methods do not combine all performance and wireless access networks while reporting QoS quality to the group Arrangement. Therefore, it is difficult to obtain any aggregate performance results using these methods. In this project, a methodical method is applied for the QoS analysis of these types of networks. The method uses a fuzzy logic (FL), artificial neural network (ANN) and Adaptive Neuro-fuzzy Interference System (ANFIS) to evaluate and predict the performance QoS of networks. The proposed methods consider the significance of QoS-related parameters, the available network-based applications, and the available Radio Access Networks (RANs) to characterize the network performance with a set of three integrated QoS metrics. The first metric denotes the performance of each available application on the network, the second one represents the performance of each active RAN on the network, and the third one characterizes the QoS level of the entire network configuration. The obtained predicting output were compared to the actual data and to each other to test which system the best for this study. The results ANN model were the closed to the real data than outcome ANFIS model

Statistical Delay Bound for WirelessHART Networks

In this paper we provide a performance analysis framework for wireless industrial networks by deriving a service curve and a bound on the delay violation probability. For this purpose we use the (min,x) stochastic network calculus as well as a recently presented recursive formula for an end-to-end delay bound of wireless heterogeneous networks. The derived results are mapped to WirelessHART networks used in process automation and were validated via simulations. In addition to WirelessHART, our results can be applied to any wireless network whose physical layer conforms the IEEE 802.15.4 standard, while its MAC protocol incorporates TDMA and channel hopping, like e.g. ISA100.11a or TSCH-based networks. The provided delay analysis is especially useful during the network design phase, offering further research potential towards optimal routing and power management in QoS-constrained wireless industrial networks.Comment: Accepted at PE-WASUN 201