Incentive mechanism design for mobile crowd sensing systems

The recent proliferation of increasingly capable and affordable mobile devices with a plethora of on-board and portable sensors that pervade every corner of the world has given rise to the fast development and wide deployment of mobile crowd sensing (MCS) systems. Nowadays, applications of MCS systems have covered almost every aspect of people's everyday living and working, such as ambient environment monitoring, healthcare, floor plan reconstruction, smart transportation, indoor localization, and many others. Despite their tremendous benefits, MCS systems pose great new research challenges, of which, this thesis targets one important facet, that is, to effectively incentivize (crowd) workers to achieve maximum participation in MCS systems. Participating in crowd sensing tasks is usually a costly procedure for individual workers. On one hand, it consumes workers' resources, such as computing power, battery, and so forth. On the other hand, a considerable portion of sensing tasks require the submission of workers' sensitive and private information, which causes privacy leakage for participants. Clearly, the power of crowd sensing could not be fully unleashed, unless workers are properly incentivized to participate via satisfactory rewards that effectively compensate their participation costs. Targeting the above challenge, in this thesis, I present a series of novel incentive mechanisms, which can be utilized to effectively incentivize worker participation in MCS systems. The proposed mechanisms not only incorporate workers' quality of information in order to selectively recruit relatively more reliable workers for sensing, but also preserve workers' privacy so as to prevent workers from being disincentivized by excessive privacy leakage. I demonstrate through rigorous theoretical analyses and extensive simulations that the proposed incentive mechanisms bear many desirable properties theoretically, and have great potential to be practically applied

Incentive Mechanism Design in Mobile Crowdsensing Systems

In the past few years, the popularity of Mobile Crowdsensing Systems (MCSs) has been greatly prompted, in which sensory data can be ubiquitously collected and shared by mobile devices in a distributed fashion. Typically, a MCS consists of a cloud platform, sensing tasks, and mobile users equipped with mobile devices, in which the mobile users carry out sensing tasks and receive monetary rewards as compensation for resource consumption ( e.g., energy, bandwidth, and computation) and risk of privacy leakage ( e.g., location exposure). Compared with traditional mote-class sensor networks, MCSs can reduce the cost of deploying specialized sensing infrastructures and enable many applications that require resources and sensing modalities beyond the current mote-class sensor processes as today’s mobile devices (smartphones (iPhones, Sumsung Galaxy), tablets (iPad) and vehicle-embedded sensing devices (GPS)) integrate more computing, communication, and storage resources than traditional mote-class sensors. The current applications of MCSs include traffic congestion detection, wireless indoor localization, pollution monitoring, etc . There is no doubt that one of the most significant characteristics of MCSs is the active involvement of mobile users to collect and share sensory data. In this dissertation, we study the incentive mechanism design in mobile crowdsensing system with consideration of economic properties. Firstly, we investigate the problem of joining sensing task assignment and scheduling in MCSs with the following three considerations: i) partial fulfillment, ii) attribute diversity, and iii) price diversity. Then, we design a distributed auction framework to allow each task owner to independently process its local auction without collecting global information in a MCS, reducing communication cost. Next, we propose a cost-preferred auction scheme (CPAS) to assign each winning mobile user one or more sub- working time durations and a time schedule-preferred auction scheme (TPAS) to allocate each winning mobile user a continuous working time duration. Secondly, we focus on the design of an incentive mechanism for an MCS to minimize the social cost. The social cost represents the total cost of mobile devices when all tasks published by the MCS are finished. We first present the working process of a MCS, and then build an auction market for the MCS where the MCS platform acts as an auctioneer and users with mobile devices act as bidders. Depending on the different requirements of the MCS platform, we design a Vickrey-Clarke-Groves (VCG)-based auction mechanism for the continuous working pattern and a suboptimal auction mechanism for the discontinuous working pattern. Both of them can ensure that the bidding of users are processed in a truthful way and the utilities of users are maximized. Through rigorous theoretical analysis and comprehensive simulations, we can prove that these incentive mechanisms satisfy economic properties and can be implemented in reasonable time complexcity. Next, we discuss the importance of fairness and unconsciousness of MCS surveillance applications. Then, we propose offline and online incentive mechanisms with fair task scheduling based on the proportional share allocation rules. Furthermore, to have more sensing tasks done over time dimension, we relax the truthfulness and unconsciousness property requirements and design a (ε, μ)-unconsciousness online incentive mechanism. Real map data are used to validate these proposed incentive mechanisms through extensive simulations. Finally, future research topics are proposed to complete the dissertation

Integrated Framework for Wildfire Risk Mitigation Planning at the Wildland/Urban Interface

Past suppression-based wildfire management practices have increased the frequency and intensity of wildfires. Advocates for the re-introduction of natural wildfire regimes must also prioritize wildfire damage protection, especially for vulnerable communities located near forests. Areas where urban and forest lands interdigitate are called the Wildland Urban Interfaces (WUIs). In the United States, the area of the WUIs is increasing, making more people vulnerable to wildfires. By responding to four research objectives, this dissertation proposed and tested an integrated framework for wildfire risk mitigation decision making at WUIs. Decision makers who could benefit from the results of this dissertation include WUI homeowners, community planners, insurance companies, and agencies that provide financial resources for managing wildfire. The first objective investigated the complex relationship between wildfire and property values in a WUI community affected by a catastrophic wildfire event. The analysis focused on evaluating whether the damage from a previous wildfire, and the risk from a potential future wildfire are negatively capitalized in the housing market of a WUI community. A Hedonic Pricing Method (HPM) was applied on homes in Los Alamos County located in Northern New Mexico. Los Alamos is the home of a highly educated and high income community which experienced the Cerro Grande fire in 2000. Results showed that wildfire damage has a negative impact on the housing price, whereas future wildfire risk is a positive driver in the Los Alamos housing market. These findings support the wildfire mitigation paradox that states that WUI homeowners tend to underinvest for mitigating wildfire risk on their properties. The second objective investigated the optimal investment required for mitigating the vulnerability of residential buildings to wildfire. The optimal retrofit plan for individual homes was estimated using an integer programming method. The evaluation function for this optimization is based on a multi-attribute vulnerability assessment system that yields a wildfire vulnerability rating for all properties in the study area. A feasible solution to this optimization problem is one that decreases the vulnerability rating of the house to an acceptable rating. Additional data included: (i) vulnerability assessment cards of the properties, (ii) building and site characteristics of the properties, and (iii) unit costs of implanting appropriate retrofit measure on each element of the property. These datasets were collected for 389 properties in Santa Fe County’s WUIs. Using an integer programing model, the total cost of reducing the vulnerability ratings from “high” and “very high” to “moderate” vulnerability level was estimated for each property. To account for uncertainties in the costs of implementing a specific retrofit measure, a Monte-Carlo sampler was used to generate 2,400 cost scenarios from cost probability distributions. Using a regression analysis on the property data, a cost function for vulnerability mitigation through retrofitting was derived. The cost function allows estimation of the retrofitting cost per area of the house and considering the initial vulnerability rating of the house. The third objective was to investigate wildfire optimal mitigation investment schedules for homeowners. Two types of investments for mitigation were analyzed, namely self-insurance and market insurance. Self-insurance is represented financially as the amount homeowners spend to implement retrofit measures to reduce their property’s vulnerability to wildfires. Market insurance is the transfer of wildfire damage liability to a third party or insurance company. The investment decision of homeowners over a multi-year investment plan considering the effects of budget and market insurance policy constraints was formulated. The effectiveness of self-insurance improvements was modeled as a damage probability function. Using a mixed-integer programming model, the optimal annual investment for market and self-insurance was estimated. The case study in this chapter demonstrated the effect of various parameters on the investment schedule of honeowners. This case study considered the time value of money and insurance companies’ contingency policies and budget constraints. The results showed that in the absence of budget constraints and mandates on mitigation, the homeowner’s optimal choice would be to fully invest on insurance and to purchase the broadest wildfire hazard insurance coverage. When a minimum mitigating retrofit effort is required by insurance companies, homeowners would invest more at the beginning of the period and decrease their investment through time. In this case results showed that a homeowner would achieve a higher expected value of investment than a homeowner with whose investments increase through time. In the fourth objective, an Agent Based Model (ABM) is proposed to account for heterogeneity in homeowners’ attributes and behaviors when confronting wildfire risk hazard. The success of the community to reduce wildfire risk was evaluated by aggregating the impact of each individual agent’s behavior. The investment behavior of each homeowner for a five-year planning period was retrieved from the optimization model proposed in the third objective. A neighborhood of six homeowners was used to test the proposed ABM. When a wildfire occurs, the wildfire may or may not damage the property. Therefore, the loss accrued by each homeowner was stochastically simulated for each year in the simulation. The probability of loss was formulated as a function of the initial vulnerability rating of the property and the homeowners’ cumulative investment on mitigation. The analyzed scenarios considered different types of homeowners (i.e. mitigating or non-mitigating). The spatial impact of neighboring properties on the loss potential of a homeowner was modeled using a conceptual fire spread model based on a Cellular Automata propagation model. Results suggest that (i) the location of the property in combination with (ii) the investment behavior of the homeowner influences the neighborhood’s aggregate loss to wildfire. Policy-makers can better mitigate aggregate loss to wildfire by prioritizing certain locations over others

Fertilizer and Soil Health in Africa The Role of Fertilizer in Building Soil Health to Sustain Farming and Address Climate Change

Summary Soil health is commonly defined as the ability to generate sufficient crop yields while maintaining the future productive capacity of soils and the ecosystem services soils regulate and deliver. However, less consensus exists on indicators to assess soil health and its changes over time and space, although soil organic carbon (SOC) is generally acknowledged as a key indicator. In the context of this paper, soil health status is equated with SOC status. Current SOC conditions are influenced by soil properties and climate. Under smallholder farming conditions, SOC is variable and affected by past crop and soil management practices, which are influenced by farmer typology. Although SOC content under cropland is a maximum of 60-70% of that under natural vegetation, there is substantial scope to increase it in smallholder farming conditions. A conceptual framework relating to fertilizer, crop productivity, and soil health is presented here. While fertilizer application commonly results in a substantial increase in crop yield at various scales, a key indicator of fertilizer use, agronomic efficiency (AE), is often observed to be lower than relatively easily achievable values under well-managed conditions, caused by a diversity of factors. Low AE values do not necessarily result in greater greenhouse gas (GHG) emissions because of the low fertilizer application rates in sub-Saharan Africa (SSA), though increases in GHG emissions are likely with increases in fertilizer use. Crop response to organic inputs is substantially lower although organic inputs increase SOC content, which usually results in greater AE values relative to sole application of fertilizer. Increases in crop productivity are associated with increases in SOC, though the relationship is weak and efforts besides fertilizer application itself are required. That said, N(PK) fertilizer has had a positive effect on SOC in most parts of the world except SSA, an observation corroborated by an analysis of past and ongoing long-term experiments, likely related to the low and erratic use of fertilizer in the region. While fertilizer use can be an entry point to increasing soil health, this will not likely happen on degraded soils where responses to fertilizer are limited. In such cases, investments to rehabilitate degraded soils should come first. Several approaches can be followed to determine best fertilizer recommendations, while recognizing nutrients needs by crops and soil-specific properties. Site-specificity commonly requires an assessment of the soil fertility status of a particular field, and analytical tools now allow for the development of locally relevant recommendations at scale with some early successes. While organic inputs do positively impact SOC, attractive options to increase organic inputs in smallholder farming systems are limited and mostly related to in-situ production, with an important emphasis on multi-purpose legumes. Climate adaptation is facilitated by healthy Fertilizer and Soil Health in Africa 2 soils and requires fertilizer to be combined with other crop, soil, and water management practices (Wortmann and Stewart, 2021). While low yields are linked to the ecological yield gap, whereby the potential productivity of crops is set by biological factors, input and output prices determine the economic yield gap, which is usually quite lower than the former because of unfavourable ratio of fertilizer prices to crop product prices. Even though profitability is a key driver of impact, many other factors affect the adoption of appropriate fertilizer and soil health recommendations, including farmers’ production objectives, resource endowment, land tenure, and access to markets. A main bottleneck in engaging smallholder farmers in soil health-restoring practices is the relatively large amount of time such practices take to deliver benefits that are visible to farmers. In the absence of incentive programs, farmers require short-term benefits, generated within their farming systems. Furthermore, associated advice on complementary practices to fertilizer use increases the complexity of information to be conveyed to farmers. Scaling models have moved toward the delivery of bundled services, often digitally enabled, to address challenges with communicating complex information and the necessary complementary crop and soil management practices. Targeted policy interventions can support the delivery of broad digitally enabled fertilizer management recommendations and the creation of conditions that enable smallholder farmers to implement these recommendations at scale. A number of recommendations have been generated from the scientific information, covered under the following headings: (1) key elements of a Fertilizer and Soil Health Action Plan; (2) development of quantitative indicators and targets of soil health; (3) addressing climate change requires choices; (4) incentivizing farmers; (5) soil health investments, which require localized actions (think global, act local); and (6) not only fertilizers, but also auxiliary interventions, as defined by the Integrated Soil Fertility Management (ISFM) approach. Action is needed today to reverse the downward spiral of low and inefficient fertilizer use, resulting in low yields and declining soil health

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above