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

Dynamic Recofiguration Techniques for Wireless Sensor Networks

The need to achieve extended service life by battery powered Wireless Sensor Networks (WSNs) requires new concepts and technqiues beyond the state-of-the-art low-power designs based on fixed hardware platforms or energy-efficient protocols. This thesis investigates reconfiguration techniques that enable sensor hardware to adapt its energy consumption to external dynamics, by means of Dynamic Voltage Scaling (DVS), Dynamic Modulation Scaling (DMS), and other related concepts. For sensor node-level reconfiguration, an integration of DVS and DMS techniques was proposed to minimize the total energy consumption. A dynamic time allocation algorithm was developed, demonstrating an average of 55% energy reduction. For network-level reconfiguration, a node activation technique was presented to reduce the cost of recharging energy-depleted sensor nodes. Network operation combined with node activation was modeled as a stochastic decision process, where the activation decisions directly affected the energy efficiency of the network. An experimental test bed based on the Imote2 sensor node platform was realized, which demonstrated energy reduction of up to 50%. Such energy saving can be effectively translated into prolonged service life of the sensor network

International Climate Agreements, Cost Reductions and Convergence of Partisan Politics

In recent years, differences between traditional and green parties have been leveled with respect to climate protection. We show that this partial convergence in party platforms can be explained by international climate agreements, effectively reducing greenhouse gas emissions. We set up a voting model in which political parties differ in their preferences for climate protection and in which (national) climate protection causes both resource costs and distortions in the international allocation of production. International agreements, which reduce greenhouse gas emissions, decrease effective abatement costs. This affects traditional parties in a different way than green parties, since a lower preference for climate protection implies a higher price (cost) elasticity of demand. Thus, climate agreements can lead to more political consensus within countries, even if politicians are partisans. We also point out that increasing flexibility and efficiency in abatement mechanisms is preferable to forming a climate coalition that focuses directly on emission reduction commitments.climate protection, political economy, green parties, platform convergence