Task Planning Considering Location Familiarity in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

Crowdsourcing to identify social innovation initiatives in health in low- and middle-income countries

Background Crowdsourcing is a distributed problem-solving and production mechanism that leverages the collective intelligence of non-expert individuals and networked communities for specific goals. Social innovation (SI) initiatives aim to address health challenges in a sustainable manner, with a potential to strengthen health systems. They are developed by actors from different backgrounds and disciplines. This paper describes the application of crowdsourcing as a research method to explore SI initiatives in health. Methods The study explored crowdsourcing as a method to identify SI initiatives implemented in Africa, Asia and Latin America. While crowdsourcing has been used in high-income country settings, there is limited knowledge on its use, benefits and challenges in low- and middle-income country (LMIC) settings. From 2014 to 2018, six crowdsourcing contests were conducted at global, regional and national levels. Results A total of 305 eligible projects were identified; of these 38 SI initiatives in health were identified. We describe the process used to perform a crowdsourcing contest for SI, the outcome of the contests, and the challenges and opportunities when using this mechanism in LMICs. Conclusions We demonstrate that crowdsourcing is a participatory method, that is able to identify bottom-up or grassroots SI initiatives developed by non-traditional actors.

A Clustering Approach Based on Cooperative Agents to Improve Decision Support in ERP

Multi-agent systems (MAS) are a powerful technology for the design and implementation of autonomous intelligent systems that can handle distributed problem solving in a complex environment. This technology has played an important role in the development of data mining systems in the last decade, the purpose of which is to promote the extraction of information and knowledge from a large database and to make these systems more scalable. In this chapter, the authors present a clustering system based on cooperative agents through a centralized and common ERP database to improve decision support in ERP systems. To achieve this, they use multi-agent system paradigm to distribute the complexity of k-means algorithm in several autonomous entities called agents, whose goal is to group records or observations on similar objects classes. This will help business decision makers to make good decisions and provide a very good response time by the use of the multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and have agents comply with the specifications FIPA.

Making for humanity

There is a growing interest in makerspaces (collaborative workspaces for information and communications technology-powered design and production), maker events and maker culture as drivers of participative creation. Further, maker communities have a lot to offer as channels of fast, affordable and adaptable solutions. In need of a new business model and technological tools, some humanitarian organizations have demonstrated interest in these communities. However, no studies have empirically investigated maker’s motivations for participating in humanitarian projects. To address this research gap, a qualitative semi-structured interview (N = 3) and a quantitative online questionnaire (N = 118) were implemented. As part of this study, a model of motivations to participate in distributed problem-solving was developed. The most significant motives for maker participation in humanitarian projects were found to be interest and enjoyment followed by relatedness, symbolic affinity and competence. The findings are discussed in terms of participation and motivation theories in a context of distributed problem-solving practices.

Design and Implementation of Swam Robotics using Flood Fill Algorithm

<p>Swam Intelligence provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. This paper presents design and implementation of swam robotics in a multi-agent environment. At the beginning, robot agents are ignorant of the maze. The robots are programmed with Flood fill algorithm to solve maze. The robot scans maze and stores the values in EEPROM. The robot agent shares the information to other robot agents through wireless communication. The proposed flood fill algorithm is found to be effective tool for solving maze of moderate size.</p>