Opportunistic Sensing: Security Challenges for the New Paradigm

We study the security challenges that arise in Opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm

Digital processing and data compilation approach for using remotely sensed imagery to identify geological lineaments in hard-rock terrains : an application for groundwater explorations in Nicaragua

Sustainable yields from water wells in hard-rock aquifers are achieved when the well bore intersects fracture networks. Fracture networks are often not readily discernable at the surface. Lineament analysis using remotely sensed satellite imagery has been employed to identify surface expressions of fracturing, and a variety of image-analysis techniques have been successfully applied in “ideal” settings. An ideal setting for lineament detection is where the influences of human development, vegetation, and climatic situations are minimal and hydrogeological conditions and geologic structure are known. There is not yet a well-accepted protocol for mapping lineaments nor have different approaches been compared in non-ideal settings. A new approach for image-processing/synthesis was developed to identify successful satellite imagery types for lineament analysis in non-ideal terrain. Four satellite sensors (ASTER, Landsat7 ETM+, QuickBird, RADARSAT-1) and a digital elevation model were evaluated for lineament analysis in Boaco, Nicaragua, where the landscape is subject to varied vegetative cover, a plethora of anthropogenic features, and frequent cloud cover that limit the availability of optical satellite data. A variety of digital image processing techniques were employed and lineament interpretations were performed to obtain 12 complementary image products that were evaluated subjectively to identify lineaments. The 12 lineament interpretations were synthesized to create a raster image of lineament zone coincidence that shows the level of agreement among the 12 interpretations. A composite lineament interpretation was made using the coincidence raster to restrict lineament observations to areas where multiple interpretations (at least 4) agree. Nine of the 11 previously mapped faults were identified from the coincidence raster. An additional 26 lineaments were identified from the coincidence raster, and the locations of 10 were confirmed by field observation. Four manual pumping tests suggest that well productivity is higher for wells proximal to lineament features. Interpretations from RADARSAT-1 products were superior to interpretations from other sensor products, suggesting that quality lineament interpretation in this region requires anthropogenic features to be minimized and topographic expressions to be maximized. The approach developed in this study has the potential to improve siting wells in non-ideal regions

Location reliability and gamification mechanisms for mobile crowd sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users. This dissertation proposes and evaluates two protocols for location reliability as a step toward achieving data reliability in sensed data, namely, ILR (Improving Location Reliability) and LINK (Location authentication through Immediate Neighbors Knowledge). ILR is a scheme which improves the location reliability of mobile crowd sensed data with minimal human efforts based on location validation using photo tasks and expanding the trust to nearby data points using periodic Bluetooth scanning. LINK is a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each other’s location claims using Bluetooth communication. The results of experiments done on Android phones show that the proposed protocols are capable of detecting a significant percentage of the malicious users claiming false location. Furthermore, simulations with the LINK protocol demonstrate that LINK can effectively thwart a number of colluding user attacks. This dissertation also proposes a mobile sensing game which helps collect crowd sensing data by incentivizing smart phone users to play sensing games on their phones. We design and implement a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. The user study results show that mobile gaming can be a successful alternative to micro-payments for fast and efficient area coverage in crowd sensing. It is observed that the proposed game design succeeds in achieving good player engagement

Pyöräilyn ympäristötekijöiden mittaaminen esineiden internetin sovelluksia varten

Increasing population in cities creates increasing amount of traffic, which leads to emissions and traffic congestion. Smart Cities set out to solve the challenges urban cities face due to the increased population, using Internet of Things as means to monitor the assets as it allows non-traditional devices to connect as a part of global information network. At the same time, cycling has increased its popularity as an environmentally friendly as well as healthy transportation method. To further its usage, infrastructure in cities must support cycling as a serious transportation method. For this purpose, it is important to include bicycles to Smart City with measurements of cycling and its environment. This thesis studies if it is possible to measure factors affecting cycling environment and assess route quality without using sensors built in bicycle frame. Decision to avoid sensors embedded in frame stemmed from incentive to have easily available and inexpensive measuring device, which does not bind the cyclists to use bicycles from specific brand or require them to purchase new bike if they are interested in participating in measuring. For evaluating the feasibility of cycling environment measuring, prototype called BikeBox was built and used during test drives. In addition, an online survey was held, which received answers from 97 cyclists. The survey queried about their cycling habits and preferences to better understand what kind of data they would be interested in. The prototype included accelerometer for measuring road quality, photoresistor to identify poorly lit areas and GPS module for location and timestamps, which are needed for other measurements as well as finding possible stopping points and slow areas on the route. Based on the test drives it is possible to identify quality changes on road surface as well as changes in lighting. Inaccurate GPS positioning does pose a challenge for pinpointing exact locations, though. Using location and timestamps it is possible to calculate the speed along different parts of the route, including areas which cause interruptions for the cyclists. This thesis presents results from 7 different example drives, though during testing phase more test driving was done. To get comprehensive coverage, crowdsourcing should be considered as the data gathering method. Based on the survey fastness and length of the route, amount of stops and interruptions and road condition are one of the most important factors for the cyclists. When queried what kind of information cyclists would like to receive, the road condition related factors were most commonly mentioned.Kaupungistumisen seurauksena väkimäärät kaupungeissa kasvavat, mikä tuo mukanaan kasvavat liikennemäärät, ruuhkat ja liikennepäästöt. Älykkäät kaupungit ovat reaktio kaupungistumisesta seuraaviin haasteisiin. Älykkäät kaupungit pyrkivät seuraamaan ja kontrolloimaan kaupungin infrastruktuuria, apunaan esineiden internet. Esineiden internet mahdollistaa epäperinteisten laitteiden yhdistämisen maailmanlaajuiseen tietoverkkoon. Samaan aikaan pyöräilyn suosio on kasvanut ympäristöystävällisenä ja terveellisenä liikennemuotona. Jos pyöräilyn määrää halutaan jatkossakin kasvattaa, kaupungin infrastruktuurin täytyy tukea pyöräilyä vakavasti otettavana liikennemuotona. Jotta tämä voidaan saavuttaa, on pyöräilijöiden pyöräily-ympäristön ja pyöräilytapojen ymmärtäminen tärkeää. Tässä työssä tutkitaan, onko pyöräily-ympäristöön vaikuttavia tekijöitä mahdollista mitata sensoreilla, joita ei ole istutettu polkupyörän runkoon. Runkoon upotettuja sensoreita haluttiin välttää, jotta mittauslaitteet voisivat olla mahdollisimman suuren joukon saatavilla, eikä pyöräilijä olisi sidottu käyttämään tietyn valmistajan polkupyörää. Lisäksi pyritään selvittämään, minkälaisesta pyöräily-ympäristöön liittyvästä datasta pyöräilijät olisivat kiinnostuneita. Tähän tarkoitukseen rakennettiin prototyyppi PyöräPurkista (BikeBox). Lisäksi toteutettiin internet-kysely, johon vastasi 97 polkupyöräilijää. Kyselyllä selvitettiin pyöräilijöiden pyöräilytapoja ja -mieltymyksiä ja sitä, millainen pyöräily-ympäristöstä kertova data kiinnostaisi heitä. Prototyyppiin sisällytettiin kiihtyvyysanturi tien pinnan laadun mittaamiseen, valoanturi heikosti valaistujen alueiden tunnistamiseen ja GPS-moduuli, jolla saadaan sijantitieto ja kellonaika muita mittauksia varten. Lisäksi sijaintitiedosta ja kellonajasta voidaan laskea ajonopeus ja paikat, missä pyöräilijä on joutunut keskeyttämään ajonsa. Testiajojen perusteella on mahdollista havaita tien pinnanlaadun muutos sekä muutos valaistusolosuhteissa. Epätarkkuudet GPS-paikannuksessa vaikeuttavat kuitenkin ongelmakohtien tarkkaa paikallistamista. Tämä työ käsittelee aiheita 7 erillisen testiajon kautta, vaikka testausvaiheessa ajettiinkin useampia testiajoja. Kattavien mittausten saamiseksi joukkoistamista kannattaisi harkita datankeräysmetodina. Tehdyn kyselyn perusteella reitin nopeus, pituus, reitillä olevien keskeytysten määrä ja tien kunto ovat tärkeimpiä reitin laatuun vaikuttavia tekijöitä. Erilaiset pyöräilyreitin kuntoon liittyvät asiat kiinnostivat eniten kun kysyttiin, minkälaista dataa pyöräilijät haluaisivat saada

UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries. However, production statistics (croplands and yields) are rarely measured, and where they are recorded, accuracy is poor because the statistics are updated through the farm survey method, which is error-prone and is time-consuming, and expensive. There is an urgent need to use affordable, accurate, timely, and readily accessible data collection and spatial analysis tools, including robust data extraction and processing techniques for precise yield forecasting for decision support and early warning systems. Meeting Africa’s rising food demand, which is driven by population growth and low productivity requires doubling the current production of major grain crops like maize by 2050. This requires innovative approaches and mechanisms that support accurate yield forecasting for early warning systems coupled with accelerated crop genetic improvement. Recent advances in remote sensing and geographical information system (GIS) have enabled detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal discrimination, and ultimately grain yield forecasting in the developed world. However, although remote sensing and spatial analysis afforded us unprecedented opportunities for detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge of crop yield forecasting using remote sensing is a daunting task because agriculture is highly fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting and land suitability analysis is not only worrying but catastrophic to food security monitoring and early warning systems in a continent burdened with chronic food shortages. Furthermore, accelerated crop genetic improvement to increase yield and achieve better adaptation to climate change is an issue of increasing urgency in order to satisfy the ever-increasing food demand. Recently, crop improvement programs are exploring the use of remotely sensed data that can be used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited. Therefore, the aim of this study was to model spatial land suitability for maize production using GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV) based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability to estimating maize grain yield in the African agricultural context, including research challenges was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were explored. The results showed that the use of remote sensing data in estimating maize yield in the African agricultural systems is still limited and obtaining accurate and reliable maize yield estimates using remotely sensed data remains a challenge due to the highly fragmented and spatially heterogeneous nature of the cropping systems. Our results underscored the urgent need to use sensors with high spatial, temporal and spectral resolution, coupled with appropriate classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal dynamics in heterogeneous African agricultural landscapes for designing appropriate food security interventions. In addition, using modern spatial analysis tools is effective in assessing land suitability for targeting location-specific interventions and can serve as a decision support tool for policymakers and land-use planners regarding maize production and varietal placement. Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput phenotyping, and yield forecasting. Using proximal sensing, our study showed that maize varietal discrimination is possible at certain phenological growth stages at the field level, which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition, the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability of partial least square discriminant analysis, and identify optimal spectral bands for maize varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties but also identified the ideal phenological stage for varietal discrimination. Flowering and onset of senescence appeared to be the most ideal stages for accurate varietal discrimination using our data. In this study, we also demonstrated the potential use of UAV-based remotely sensed data in maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge), and the Red band as the most important variables for classification. The results demonstrated that spectral bands and vegetation indices measured at the vegetative stage are the most important for the classification of maize varietal response to MSV. Further analysis to predict MSV disease and grain yield using UAV-derived multispectral imaging data using multiple models showed that Red and NIR bands were frequently selected in most of the models that gave the highest prediction precision for grain yield. Combining the NIR band with Red band improved the explanatory power of the prediction models. This was also true with the selected indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop productivity, and combining them increased the joint predictive power, consequently increased complementarity. Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability analysis for maize production and the utility of remotely sensed data in maize varietal discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific interventions for varietal placement and integrating UAV-based high-throughput phenotyping systems in crop genetic improvement to address continental food security, especially as climate change accelerates