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

Evaluation of e-learning web sites using fuzzy axiomatic design based approach

High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered

Organizing otherwise: translating anarchism in a voluntary sector organization

Although foundational texts in Critical Management Studies (CMS) pointed to the empirical significance of anarchism as an inspiration for alternative ways of organizing (Burrell, 1992), relatively little work of substance has been undertaken within CMS to explore how anarchists organize or how anarchist principles of organization might fare in other contexts. This paper addresses this gap by reporting on the experiences of a UK Voluntary Sector Organization (VSO) seeking to adopt non-hierarchical working practices inspired by anarchism. The paper analyses this process of organizational change by examining how ideas and practices are translated and transformed as they travel from one context (direct action anarchism) to another (the voluntary sector). Whilst the onset of austerity and funding cuts created the conditions of possibility for this change, it was the discursive translation of 'anarchism' into 'non-hierarchical organizing' that enabled these ideas to take hold. The concept of 'non-hierarchical' organization constituted an open space that was defined by negation and therefore capable of containing a multiplicity of meanings. Rather than having to explicitly embrace anarchism, members were able to find common ground on what they did not want (hierarchy) and create a discursive space for democratically determining what might replace it

Mean-Field Theory of Meta-Learning

We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents, that acquire and process incoming information using various types, or different versions of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. Therefore, the probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents.Comment: 23 page

Consensus-based approach to peer-to-peer electricity markets with product differentiation

With the sustained deployment of distributed generation capacities and the more proactive role of consumers, power systems and their operation are drifting away from a conventional top-down hierarchical structure. Electricity market structures, however, have not yet embraced that evolution. Respecting the high-dimensional, distributed and dynamic nature of modern power systems would translate to designing peer-to-peer markets or, at least, to using such an underlying decentralized structure to enable a bottom-up approach to future electricity markets. A peer-to-peer market structure based on a Multi-Bilateral Economic Dispatch (MBED) formulation is introduced, allowing for multi-bilateral trading with product differentiation, for instance based on consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is described to solve the MBED in fully decentralized manner. A set of realistic case studies and their analysis allow us showing that such peer-to-peer market structures can effectively yield market outcomes that are different from centralized market structures and optimal in terms of respecting consumers preferences while maximizing social welfare. Additionally, the RCI solving approach allows for a fully decentralized market clearing which converges with a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System

Zurich Consensus: German Expert Opinion on the St. Gallen Votes on 15 March 2009 (11th International Conference at St. Gallen: Primary Therapy of Early Breast Cancer)

A German working group of 23 breast cancer experts discussed the results from the vote at this year's St. Gallen Consensus Conference on Primary Therapy for Early Breast Cancer ( March 11-14, 2009) and came up with some concrete recommendations for day-to-day therapeutic decisions in Germany. Due the fact that the concept of the St. Gallen Consensus Conference merely allows for a minimal consensus, the objective of the working group was to provide practice-related recommendations for day-to-day clinical decisions in Germany. One area of emphasis at St. Gallen was tumor biology as a starting point for reaching individual therapeutic decisions. Intensive discussion was necessary with respect to the clinical relevance of predictive and prognostic factors. A new addition to the area of systemic therapy was a first-ever discussion of the adjuvant administration of bisphosponates and the fact that therapy with trastuzumab in HER2 overexpressing breast cancer has been defined as the standard for neoadjuvant therapy. The value of taxanes as a component of (neo) adjuvant chemotherapy as well as the value of aromatase inhibitors for the endocrine adjuvant treatment of postmenopausal patients were affirmed

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers