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Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications  [PDF]
Mohammad Abu Alsheikh,Shaowei Lin,Dusit Niyato,Hwee-Pink Tan
Computer Science , 2014, DOI: 10.1109/COMST.2014.2320099
Abstract: 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.
Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor  [PDF]
Robert LeMoyne, Timothy Mastroianni, Donald Whiting, Nestor Tomycz
Advances in Parkinson's Disease (APD) , 2019, DOI: 10.4236/apd.2019.84007
Abstract: The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop
Detecting Unknown Attacks in Wireless Sensor Networks That Contain Mobile Nodes  [PDF]
Zorana Bankovi?,David Fraga,José M. Moya,Juan Carlos Vallejo
Sensors , 2012, DOI: 10.3390/s120810834
Abstract: As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.
Distributed Learning in Wireless Sensor Networks  [PDF]
Joel B. Predd,Sanjeev R. Kulkarni,H. Vincent Poor
Mathematics , 2005, DOI: 10.1109/MSP.2006.1657817
Abstract: The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted and systems designed from these models show promise. However, in other scenarios, prior knowledge is at best vague and translating such knowledge into a statistical model is undesirable. Applications such as these pave the way for a nonparametric study of distributed detection and estimation. In this paper, we review recent work of the authors in which some elementary models for distributed learning are considered. These models are in the spirit of classical work in nonparametric statistics and are applicable to wireless sensor networks.
A Review of Machine Learning based Anomaly Detection Techniques  [PDF]
Harjinder Kaur,Gurpreet Singh,Jaspreet Minhas
Computer Science , 2013,
Abstract: Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is discussed.
Machine Learning: A Crucial Tool for Sensor Design  [PDF]
Weixiang Zhao,Abhinav Bhushan,Anthony D. Santamaria,Melinda G. Simon,Cristina E. Davis
Algorithms , 2008, DOI: 10.3390/a1020130
Abstract: Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.
Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning  [PDF]
Xue Wang,Sheng Wang,Daowei Bi,Liang Ding
Sensors , 2007, DOI: 10.3390/s7112693
Abstract: Wireless multimedia sensor networks (WMSN) have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM) based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.
Supporting Learning with Wireless Sensor Data  [PDF]
Jari Multisilta,Arttu Perttula
Future Internet , 2013, DOI: 10.3390/fi5010095
Abstract: In this article, learning is studied in in situ applications that involve sensors. The main questions are how to conceptualize experiential learning involving sensors and what kinds of learning applications using sensors already exist or could be designed. It is claimed that experiential learning, context information and sensor data supports twenty first century learning. The concepts of context, technology-mediated experiences, shared felt experiences and experiential learning theory will be used to describe a framework for sensor-based mobile learning environments. Several scenarios and case examples using sensors and sensor data will be presented, and they will be analyzed using the framework. Finally, the article contributes to the discussion concerning the role of technology-mediated learning experiences and collective sensor data in developing twenty first century learning by characterizing what kinds of skills and competences are supported in learning situations that involve sensors.
Application of Machine-Learning Based Prediction Techniques in Wireless Networks  [PDF]
Gitanjali Bhutani
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2014, DOI: 10.4236/ijcns.2014.75015

Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.

Combining Harmony Search and Learning Automata for Topology Control in Wireless Sensor Networks  [PDF]
Mohamad Nikravan,Seyed Mahdi Jameii
International Journal of Wireless & Mobile Networks , 2013,
Abstract: One of the critical challenges in wireless sensor networks (WSNs) is energy conservation. Topology control is a vital process to minimize energy consumption and maximize the network lifetime. In this paper, we propose a distributed topology protocol with transmission power adjustment based on harmony search and learning automata algorithms called HSLATC (Harmony Search and Learning Automata based Topology Control protocol). In HSLATC, proper transition radius of the sensor nodes can be determined. Because of the intelligent determining the transition radiuses of the nodes in the proposed protocol, it is able to provide the full connectivity in sparse deployment and decreases the energy consumption of the sensor network and prolongs the network lifetime. The simulation results show high efficiency of the proposed protocol.
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