With the growth of the Internet, crowdsourcing has become a popular way to perform intelligence tasks that hitherto would be either performed internally within an organization or not undertaken due to prohibitive costs and the lack of an appropriate communications infrastructure. In crowdsourcing systems, whereby multiple agents are not under the direct control of a system designer, it cannot be assumed that agents will act in a manner that is consistent with the objectives of the system designer or principal agent. In situations whereby agents’ goals are to maximize their return in crowdsourcing systems that offer financial or other rewards, strategies will be adopted by agents to game the system if appropriate mitigating measures are not put in place. The motivational and incentivization research space is quite large; it incorporates diverse techniques from a variety of different disciplines including behavioural economics, incentive theory, and game theory. This paper specifically focusses on game theoretic approaches to the problem in the crowdsourcing domain and places it in the context of the wider research landscape. It provides a survey of incentive engineering techniques that enable the creation of apt incentive structures in a range of different scenarios.

One of the most important factors to be considered when developing an application for a wireless sensor network (WSN) is its power consumption. Intelligent power management (IPM) for a WSN is crucial in maximizing the operational longevity. An established regime for achieving this is through the opportunistic hibernation of redundant nodes. Redundancy, however, has various definitions within the field of WSNs and indeed multiple protocols, each operating using a different definition, coexist on the same node. In this paper, we advocate the use of an MAS as an appropriate mechanism by which different stake-holders, each desiring to hibernate a node in order to conserve power, can collaborate. The problem of node hibernation for the heterogeneous sensing coverage areas is introduced and the manner by which it can be solved using ADOPT, an algorithm for distributed constraint optimization, is described. We illustrate that the node hibernation strategy discussed here is more useful than the traditional stack-based approach and motivate our discussion using IPM as an exemplar.

With water quality management processes, identifying and interpreting relationships between features, such as location and weather variable tuples, and water quality variables, such as levels of bacteria, is key to gaining insights and identifying areas where interventions should be made. There is a need for a search process to identify the locations and types of phenomena that are influencing water quality and a need to explain how the quality is being affected and which factors are most relevant. This paper addresses both of these issues. A process is developed for collecting data for features that represent a variety of variables over a spatial region and which are used for training models and inference. An analysis of the performance of the features is undertaken using the models and Shapley values. Shapley values originated in cooperative game theory and can be used to aid in the interpretation of machine learning results. Evaluations are performed using several machine learning algorithms and water quality data from the Dublin Grand Canal basin.