Abstract-The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

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Science is the systematic classification of experience. -George Henry LewesThis chapter covers details of the support vector machine (SVM) technique, a sparse kernel decision machine that avoids computing posterior probabilities when building its learning model. SVM offers a principled approach to machine learning problems because of its mathematical foundation in statistical learning theory. SVM constructs its solution in terms of a subset of the training input. SVM has been extensively used for classification, regression, novelty detection tasks, and feature reduction. This chapter focuses on SVM for supervised classification tasks only, providing SVM formulations for when the input space is linearly separable or linearly nonseparable and when the data are unbalanced, along with examples. The chapter also presents recent improvements to and extensions of the original SVM formulation. A case study concludes the chapter.

The emergence of the Internet of things and the widespread deployment of diverse computing systems have led to the formation of heterogeneous multi-agent systems (MAS) to complete a variety of tasks. Motivated to highlight the state of the art on existing MAS while identifying their limitations, remaining challenges, and possible future directions, we survey recent contributions to the field. We focus on robot agents and emphasize the challenges of MAS sub-fields including task decomposition, coalition formation, task allocation, perception, and multi-agent planning and control. While some components have seen more advancements than others, more research is required before effective autonomous MAS can be deployed in real smart city settings that are less restrictive than the assumed validation environments of MAS. Specifically, more autonomous end-to-end solutions need to be experimentally tested and developed while incorporating natural language ontology and dictionaries to automate complex task decomposition and leveraging big data advancements to improve perception algorithms for robotics.

Machine LearningNature is a self-made machine, more perfectly automated than any automated machine. To create something in the image of nature is to create a machine, and it was by learning the inner working of nature that man became a builder of machines.-Eric Hoffer, Reflections on the Human Condition Machine learning (ML) is a branch of artificial intelligence that systematically applies algorithms to synthesize the underlying relationships among data and information. For example, ML systems can be trained on automatic speech recognition systems (such as iPhone's Siri) to convert acoustic information in a sequence of speech data into semantic structure expressed in the form of a string of words.ML is already finding widespread uses in web search, ad placement, credit scoring, stock market prediction, gene sequence analysis, behavior analysis, smart coupons, drug development, weather forecasting, big data analytics, and many more applications. ML will play a decisive role in the development of a host of user-centric innovations.ML owes its burgeoning adoption to its ability to characterize underlying relationships within large arrays of data in ways that solve problems in big data analytics, behavioral pattern recognition, and information evolution. ML systems can moreover be trained to categorize the changing conditions of a process so as to model variations in operating behavior. As bodies of knowledge evolve under the influence of new ideas and technologies, ML systems can identify disruptions to the existing models and redesign and retrain themselves to adapt to and coevolve with the new knowledge.The computational characteristic of ML is to generalize the training experience (or examples) and output a hypothesis that estimates the target function. The generalization attribute of ML allows the system to perform well on unseen data instances by accurately predicting the future data. Unlike other optimization problems, ML does not have a well-defined function that can be optimized. Instead, training errors serve as a catalyst to test learning errors. The process of generalization requires classifiers that input discrete or continuous feature vectors and output a class.The goal of ML is to predict future events or scenarios that are unknown to the computer. In 1959, Arthur Samuel described ML as the "field of study that gives computers the ability to learn without being explicitly programmed" (Samuel 1959). He concluded that programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort. According to Tom M. Mitchell's definition of ML: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." Alan Turing's seminal paper (Turing 1950) introduced a benchmark standard for demonstrating machine intelligence, such that a machine has to be intelligent and responsive in a manner that cannot be differentiated fro...

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Nature is a self-made machine, more perfectly automated than any automated machine. To create something in the image of nature is to create a machine, and it was by learning the inner working of nature that man became a builder of machines. -Eric Hoffer, Reflections on the Human ConditionMachine learning (ML) is a branch of artificial intelligence that systematically applies algorithms to synthesize the underlying relationships among data and information. For example, ML systems can be trained on automatic speech recognition systems (such as iPhone's Siri) to convert acoustic information in a sequence of speech data into semantic structure expressed in the form of a string of words.ML is already finding widespread uses in web search, ad placement, credit scoring, stock market prediction, gene sequence analysis, behavior analysis, smart coupons, drug development, weather forecasting, big data analytics, and many more applications. ML will play a decisive role in the development of a host of user-centric innovations.ML owes its burgeoning adoption to its ability to characterize underlying relationships within large arrays of data in ways that solve problems in big data analytics, behavioral pattern recognition, and information evolution. ML systems can moreover be trained to categorize the changing conditions of a process so as to model variations in operating behavior. As bodies of knowledge evolve under the influence of new ideas and technologies, ML systems can identify disruptions to the existing models and redesign and retrain themselves to adapt to and coevolve with the new knowledge.The computational characteristic of ML is to generalize the training experience (or examples) and output a hypothesis that estimates the target function. The generalization attribute of ML allows the system to perform well on unseen data instances by accurately predicting the future data. Unlike other optimization problems, ML does not have a well-defined function that can be optimized. Instead, training errors serve as a catalyst to test learning errors. The process of generalization requires classifiers that input discrete or continuous feature vectors and output a class.The goal of ML is to predict future events or scenarios that are unknown to the computer. In 1959, Arthur Samuel described ML as the "field of study that gives computers the ability to learn without being explicitly programmed" (Samuel 1959). He concluded that programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort. According to Tom M. Mitchell's definition of ML: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." Alan Turing's seminal paper (Turing 1950) introduced a benchmark standard for demonstrating machine intelligence, such that a machine has to be intelligent and responsive in a manner that cannot be differentiated from that of a hum...