A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. OTHER CHALLENGES <s> In recent years crowdsourcing has increased in popularity as a method for gathering ideas for new innovations and providing solutions to existing problems. This means that firms apply the wisdom of crowds to certain tasks and challenges. Various crowdsourcing initiatives and platforms seem to provide new channels and ways to enable this in practice. The purpose of this paper is to examine how business-to-business (B2B) firms can interact with different groups of contributors in order to receive new ideas, feedback and solutions for improving their products and services. Based on theoretical conceptualization, combined with empirical evidence, we propose a layered framework for approaching crowdsourcing in a B2B context. The empirical results of this paper reveal benefits but also practical challenges to overcome before crowdsourcing can be effectively utilized in the B2B sector. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. OTHER CHALLENGES <s> Mobile crowdsensing is becoming a vital technique for environment monitoring, infrastructure management, and social computing. However, deploying mobile crowdsensing applications in large-scale environments is not a trivial task. It creates a tremendous burden on application developers as well as mobile users. In this paper we try to reveal the barriers hampering the scale-up of mobile crowdsensing applications, and to offer our initial thoughts on the potential solutions to lowering the barriers. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> E. OTHER CHALLENGES <s> Abstract Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowdsourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to consider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a S ocial A ware C rowdsourcing with R eputation M anagement (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for participant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports. <s> BIB003
Wireless connectivity indoors is still unpredictable using low-energy consumption radio frequency transceivers, particularly due to interference from electromagnetic fields produced by elevators, cordless phones at home, machinery, and computers among others. Another challenge is the programmability and re-programmability (re-configuration) necessary for energy conservation and node-to-node communication. Researchers in BIB003 , BIB002 , and BIB001 are still faced with major obstacles to widely perform experiments, in spite of the huge demand for MCS applications for smartphones. These obstacles include the 1) the time and energy costs of resolving a robust, scalable, and visually attractive application and infrastructure, 2) the limited retention of users after using the applications for few weeks, and finally 3) human related issues such as privacy, incentives, and quality of data. Although, the idea of scaling to millions of devices is attractive, the widespread computing community still relies on expensive and short-term user contribution with small numbers of users. The work in BIB001 discussed a business-to-business model to limit the effect of obstacles facing mobile crowd-sensing. In BIB003 the battery size was shown to be the main factor limiting the use of mobile phones. Although energy consumption is a limiting factor in WSNs in general, an additional factor in MCS consists of the human user decision: Some users tend to refrain from participation in order to save battery energy. The challenges related to MCS are summarized in Table 7 .