Energy harvesting methods for wireless sensor nodes in heavy-duty vehicles

The number of autonomous wireless sensor and control nodes has been increasing rapidly during the last decade. Until recently, these wireless nodes have been powered with batteries, which have lead to a short life cycle and high maintenance need. Due to these battery-related problems, new energy sources have been studied to power wireless nodes. One solution is energy harvesting, i.e. extracting energy from the ambient environment. Energy harvesting can provide a long-lasting power source for sensor nodes, with no need for maintenance. In this thesis, various energy harvesting technologies are studied whilst focusing on the theory of each technology and the state-of-the-art solutions of published studies and commercial solutions. In addition to energy harvesting, energy storage and energy management solutions are also studied as a subsystem of a whole energy source solution. Wireless nodes are also used in heavy-duty vehicles. Therefore a reliable, long-lasting and maintenance-free power source is also needed in this kind of environment. A forestry harvester has been used as a case study to study the feasibility of energy harvesting in a forestry harvester’s sliding boom. The energy harvester should be able to produce few milliwatts to power the target system, an independent limit switch.

Metsäalan yrittäjien tyytyväisyys urakanantajaan

The aim of this thesis is to clarify, how satisfied the harvesting and transport entrepreneurs of Stora Enso Metsä are with the partnership relationship between Stora Enso Metsä and entrepreneurs. The aim is also to found the main areas of development in relation to forest entrepreneur. 161 companies answered to quantitative survey. Based on the results gathered, it would appear that the entrepreneurs are fairly satisfied with the functioning of Stora Enso Metsä. Changes of operations during the past two years is experienced a relatively neutral, although an entrepreneurial model is significantly changed and the model of transportation also. Most obvious targets for development according to the responses are information sharing, feedback, development of systems and IT applications.

Energiapuuta Etelä-Savosta

Energiapuuta Etelä-Savosta hankkeessa tutkittiin ja kehitettiin energiapienpuun mahdollisuuksia osana suurimittakaavaista alueellista hankintaa. Energiapienpuun potentiaali suurimittakaavaisena polttoaineena on lupaava johtuen hyvästä biomassan saatavuudesta ja tarjontahalukkuudesta, korjuun teknologisesta edistyksestä ja logististen ratkaisujen monipuolisuudesta sekä käyttömäärien kasvusta. Hanke kokonaisuus sisälsi seuraavia osatutkimuksia: Etelä-Savon energiatase, metsänomistajakysely, asiantuntijahaastattelu, metsänkasvatussimulointi, metsäpolttoaineiden saatavuuslaskenta, puun kosteuden seurantatutkimus, logistiikan demonstraatioita sekä energiapuun hankintalogistiikan kustannusvertailuja. Hankkeessa käsiteltiin energiapienpuun arvoketjua metsänkasvatuksesta lopputuotteen käyttöön asti. Tutkimuksen tulokset osoittavat, että energiapienpuulla on mahdollisuudet suurimittakaavaisessa hankinnassa, kunhan toimitusketjussa panostetaan tehokkuuden ja laadun jatkuvaan parantamiseen.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.