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

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

都市の持続可能性に向けた旅行行動と知的移動データ統合に関する包括的研究

過去数十年にわたり世界中で都市の持続可能性がトレンドとなり研究対象となっている．人々は，非効率な天然資源の消費や社会経済活動による環境破壊など，地球環境に有害な活動を行い，これには都市計画や交通計画を始め，多くの分野が密接に関係している．現在では，これらを解決する新技術の開発や応用が広範囲な研究分野で日々取り組まれている．本研究では観光に関する問題を，交通と都市の研究の観点からさまざまなビックデータを使用し，持続可能な都市開発を目標とした具体的な解決策を示した．本研究では都市や地域の持続可能性に資するデータの活用方法として，Wi-Fiパケットセンサーを使用した旅行者にとって魅力的な観光目的地マネジメントに関する研究，およびETCプローブデータを使用した旅行時間の信頼性の観測における天候の影響に関する分析を組み合わせて示した．本論文では，都市の移動性の認知に対して以下に示す3つの研究から，特徴的な結果と有効な分析手法を確立した．1）Wi-Fiパッケージセンシング調査を使用した，広域観光エリアでの周遊パターンのマイニングベースの関連法則の調査，2）Wi-Fi追跡データでの大規模な観光地の持続可能な開発に向けた魅力的な目的地の抽出，3）ETC2.0プローブデータを使用して，様々な道路タイプを考慮した旅行の信頼性に対する降雪の影響の評価．以上の研究から，複数視点の考察を積み重ね，包括的な評価と提案を行い，いくつかの重要な結果が得られた．この論文の貢献は，より良い社会への問題解決への糸口となり，今後の政策立案者にとって有意義な内容となるだろう．According to sustainability, the trend is spreading out around the world for past decades. There are many area subjects involved, such as city planning, transportation planning, and so on, because people realized human activities harmful to the environment by consuming natural resources with less efficiency process or damage environment by social and economic movements. Currently, emerging technologies considered for the proactive procedure in extensive study areas regarding new technology application and knowledge based. In term of transport and urban study, including tourism concerns, we used intelligent data from deferent sources to be demonstrating the possible solutions which involve sustainable urban development concept. In this study, as a method of utilizing data that contributes to the sustainability of cities and regions, consideration of attractive destination management for tourists by using wireless probe data, and the weather impact on travel time reliability observation by using electronic toll collection probe data, it represented as combination experiments throughout comprehensive study. This dissertation addressed three contribution studies to the composed acknowledgment of urban mobility, and it obtained the intelligent data and specific method of research-based. It consists of; 1) an association rule mining-based exploration of travel patterns in wide tourism areas using a Wi-Fi package sensing survey, 2) Attractive destinations mining towards massive tourism area sustainable development on Wi-Fi tracking data, and 3) Assessment of the impact of snowfall on travel reliability considering different road types using ETC2.0 probe data. Hence, a stack of varying viewpoints researches provided a comprehensive review and suggestion throughout significant results. The contribution of this dissertation could be an advantage substance for strategy and policies planner to recognize alternative solutions leading to a better society.室蘭工業大学 (Muroran Institute of Technology)博士（工学

CCTV Surveillance System, Attacks and Design Goals

Closed Circuit Tele-Vision surveillance systems are frequently the subject of debate. Some parties seek to promote their benefits such as their use in criminal investigations and providing a feeling of safety to the public. They have also been on the receiving end of bad press when some consider intrusiveness has outweighed the benefits. The correct design and use of such systems is paramount to ensure a CCTV surveillance system meets the needs of the user, provides a tangible benefit and provides safety and security for the wider law-abiding public. In focusing on the normative aspects of CCTV, the paper raises questions concerning the efficiency of understanding contemporary forms of ‘social ordering practices’ primarily in terms of technical rationalities while neglecting other, more material and ideological processes involved in the construction of social order. In this paper, a 360-degree view presented on the assessment of the diverse CCTV video surveillance systems (VSS) of recent past and present in accordance with technology. Further, an attempt been made to compare different VSS with their operational strengths and their attacks. Finally, the paper concludes with a number of future research directions in the design and implementation of VSS

Requirements for Topology in 3D GIS

Topology and its various benefits are well understood within the context of 2D Geographical Information Systems. However, requirements in three-dimensional (3D) applications have yet to be defined, with factors such as lack of users' familiarity with the potential of such systems impeding this process. In this paper, we identify and review a number of requirements for topology in 3D applications. The review utilises existing topological frameworks and data models as a starting point. Three key areas were studied for the purposes of requirements identification, namely existing 2D topological systems, requirements for visualisation in 3D and requirements for 3D analysis supported by topology. This was followed by analysis of application areas such as earth sciences and urban modelling which are traditionally associated with GIS, as well as others including medical, biological and chemical science. Requirements for topological functionality in 3D were then grouped and categorised. The paper concludes by suggesting that these requirements can be used as a basis for the implementation of topology in 3D. It is the aim of this review to serve as a focus for further discussion and identification of additional applications that would benefit from 3D topology. © 2006 The Authors. Journal compilation © 2006 Blackwell Publishing Ltd

Optimal Resource Allocation Using Deep Learning-Based Adaptive Compression For Mhealth Applications

In the last few years the number of patients with chronic diseases that require constant monitoring increases rapidly; which motivates the researchers to develop scalable remote health applications. Nevertheless, transmitting big real-time data through a dynamic network limited by the bandwidth, end-to-end delay and transmission energy; will be an obstacle against having an efficient transmission of the data. The problem can be resolved by applying data reduction techniques on the vital signs at the transmitter side and reconstructing the data at the receiver side (i.e. the m-Health center). However, a new problem will be introduced which is the ability to receive the vital signs at the server side with an acceptable distortion rate (i.e. deformation of vital signs because of inefficient data reduction). In this thesis, we integrate efficient data reduction with wireless networking to deliver an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. A Deep Learning (DL) approach was used to implement an adaptive compression technique to compress and reconstruct the vital signs in general and specifically the Electroencephalogram Signal (EEG) with the minimum distortion. Then, a resource allocation framework was introduced to minimize the transmission energy along with the distortion of the reconstructed signa

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe