The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.

With the rapid advancement of mobile phone technology, crowdsourced mobile applications are gaining importance for cheaper and abundant information gathering source. A couple of benefits of crowdsourcing are better (versatile) and less expensive responses to the requester, source of earning for crowd contributors at the same time. With the emergence of wireless access technologies and the popularity of smart and intelligent mobile terminals like smartphones, the privacy of requesters and contributors in a crowdsourced environment is becoming more crucial. Computation complexity of available privacy preserving tools and verification of outsourced schemes are not feasible for WSNs low power and resource constrained contributing devices. The low-end resource constrained mobile devices to have less capability to compute multiple access policies and the computation cost increases with the complexity of access strategy. To resolve this issue, we proposed a resourceful verifiable outsourced encryption policy based on ciphertext attributed policy (OS-ABE), which is a platform for computing exhaustive computing tasks during private data encryption and decryption. The proposed scheme works without revealing the personal data at outsource and leaves only the minimal calculation to the crowdsource contributor and the requester. To authenticate and overcome known attacks like DoS, false computing results at crowdsourced platforms, anonymity server is embedded to handle contributors privacy issues by providing a secure shell in the framework. This scheme is based on bilinear group of prime order in which two hash functions can verify outsourced computation and ensure integrity of data. The formal security and performance analysis is presented and found to be efficient and secure.