With the ubiquitous distribution of mobile devices (smartphones, tablets, and so on equipped with a variety of sensory and wearable devices), users are able to generate different types of real-time information related to ambient air measurements, fire detection and natural disasters, global positioning system (GPS) location recording, streaming video, and more. Taking into account the availability of several billion mobile users in the world, this is one of the main sources for generating sensor data processed in the cloud environment. The great potential of IT-mediated users is a prerequisite for the rapid development of the crowdsourcing paradigm in the last decade. In crowdsourcing, a large crowd of people is engaged via open call to perform a job or solve one or more tasks using their own devices for work and communication on the Internet. When the crowd’s activity is directly related to the time and/or the location of the task, the crowdsourcing is classified as spatio-temporal.

The development of new solutions to the design and management of crowdsourcing through the creation of efficient cloud services is a highly topical theme. This book presents new approaches in this direction. The main goal of the authors is to develop a crowdsourced sensor cloud framework, including a service framework for cloud-based sensor data, quality of service (QoS)-aware spatio-temporal composition of sensor cloud services, a crowdsourcing platform for real-time and adaptive service provisioning, and a framework for incentivizing crowd participation.

The book is structured in six chapters. Chapter 1 presents a brief outline of the issues, concepts, approaches, and results related to the crowdsourcing of sensor cloud services. The second chapter is a nice overview of the current state of research in sensor cloud architectures, types of service compositions, spatio-temporal crowdsourced services, and incentive models.

Chapter 3 describes the authors’ work based on an abstract approach using “spatio-temporal travel planning [algorithms], spatio-temporal index methods, dynamic service reconfiguration, and replanning algorithms.” A spatial map is assumed, consisting of line segments as line segment services and related functional attributes, QoS, and time parameters. A spatio-temporal indexing model is developed based on line segment representation using a 3D R-tree, thus introducing the time parameter, a spatio-temporal selection algorithm, a new quality model using dynamic sensor features for optimizing the selection and composition of the sensor cloud services, and a failure-proof composition algorithm. Examples include the building of sensor clouds for journey planning using public transport. Experiments are carried out and discussed.

Based on the results of chapter 3, chapter 4 develops a two-level algorithm for efficiently selecting an optimal composition plan and multiple QoS criteria. Crowdsourced coverage as a service (CaaS), a new term, is introduced to consider each coverage (for example, a Wi-Fi hotspot provided by the crowd) as a crowdsourced sensor cloud service. The two-level composition service includes a sensor cloud data management layer (first level) and a sensor cloud service layer (second level). Examples for optimizing a Wi-Fi-covered travel plan are provided to evaluate the performance of the proposed algorithms. Experimental results for “one path at a time” and “one segment at a time” approaches are presented.

Chapter 5 addresses the issue of motivating crowds to provide the necessary sensory data and services. It is assumed that crowd participants will be compensated on a credit-based principle. For the travel planning scenario, an algorithm is developed that takes into account the current need for sufficient crowdsourced data at a given time and region of the city. This way crowd participants are incentivized according to the given area’s demands. A new redistribution algorithm is presented to assure balanced crowdsourced service coverage from the crowd. For the redistribution of hotspot providers, the authors develop a “greedy network flow algorithm for crowdsourced service coverage balancing using the incentive model.” Experiments are carried out.

The last chapter contains an overview of the results and future work directions.

The book is suitable for scientists and programmers, both for research and for developing practical applications. Although the sample scenarios used are from city-based travel planning and crowd-provided sensor data, the proposed approaches, models, and algorithms could be useful for managing similar space-intensive and/or time-intensive crowdsourced cloud services.