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

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Reconfiguring Household Management in Times of Discontinuity as an Open System: The Case of Agro-food Chains

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article is based upon a heterodox approach to economics that rejects the oversimplification made by closed economic models and the mainstream concept of ‘externality.’ This approach re-imagines economics as a holistic evaluation of resources versus human needs, which requires judgement based on understanding of the complexity generated by the dynamic relations between different systems. One re-imagining of the economic model is as a holistic and systemic evaluation of agri-food systems’ sustainability that was performed through the multi-dimensional Governance Assessment Matrix Exercise (GAME). This is based on the five capitals model of sustainability, and the translation of qualitative evaluations into quantitative scores. This is based on the triangulation of big data from a variety of sources. To represent quantitative interactions, this article proposes a provisional translation of GAME’s qualitative evaluation into a quantitative form through the identification of measurement units that can reflect the different capital dimensions. For instance, a post-normal, ecological accounting method, Emergy is proposed to evaluate the natural capital. The revised GAME re-imagines economics not as the ‘dismal science,’ but as one that has potential leverage for positive, adaptive and sustainable ecosystemic analyses and global ‘household’ management. This article proposes an explicit recognition of economics nested within the social spheres of human and social capital which are in turn nested within the ecological capital upon which all life rests and is truly the bottom line. In this article, the authors make reference to an on-line retailer of local food and drink to illustrate the methods for evaluation of the five capitals model

Managing Water under Uncertainty and Risk: The United Nations World Water Development Report 4

This report introduces new aspects of water issues: 1) it reintroduces the 12 challenge area reports that provided the foundation for the first two World Water Development Reports (WWDR); 2) 4 new reports on water quality, groundwater, gender, and desertification, land degradation and drought; 3) in recognition that the global challenges of water can vary considerably across countries and regions, a series of 5 regional reports have been included; 4) a deeper analysis of the main external forces of freshwater resources and possibilities for their future evolution; 5) managing water under uncertainty and risk