Spatial crowdsourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge; the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features of tasks and workers. The algorithm simultaneously adapts the worker assignment strategy based on the observed task acceptance behavior of workers. Finally, we present an evaluation methodology based on a real world dataset, and evaluate the performance of the proposed algorithm against the baseline algorithms. The results demonstrate that the proposed algorithm performs better in terms of the number of successful assignments.

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Smart environments can engage a wide range of end users with different interests and priorities, from corporate managers looking to improve the performance of their business to school children who want to explore and learn more about the world around them. Creating an effective user experience within a smart environment (from smart buildings to smart cities) is an important factor to success. In this article, we reflect on our experience of developing Internet-of-Things-enabled applications within a smart home, school, office building, university, and airport, where the goal has been to engage a wide range of users (from building managers to business travellers) to increase water and energy awareness, management, and conservation.Driven by the idea of using ICT to more effectively and efficiently manage resources, smart environments have emerged in the form of smart cities, smart buildings, smart grids, smart water, and smart mobility. 1 Mark Weiser and colleagues define a smart environment as "a physical world that is richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives, and connected through a continuous network." 2 A key driver in the development of smart environments is the convergence of technologies such as the Internet of Things (IoT) and big data, which are driving the