  Crowdsourcing with the intelligent agents carrying smart devices is becoming
increasingly popular in recent years. It has opened up meeting an extensive
list of real life applications such as measuring air pollution level, road
traffic information, and so on. In literature this is known as mobile
crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up
consists of multiple task requesters (or task providers) and multiple IoT
devices (as task executors), where each of the task providers is having
multiple homogeneous sensing tasks. Each of the task requesters report bid
along with the number of homogeneous sensing tasks to the platform. On the
other side, we have multiple IoT devices that reports the ask (charge for
imparting its services) and the number of sensing tasks that it can execute.
The valuations of task requesters and IoT devices are private information, and
both might act strategically. One assumption that is made in this paper is that
the bids and asks of the agents (task providers and IoT devices) follow
decreasing marginal returns criteria. In this paper, a truthful mechanism is
proposed for allocating the IoT devices to the sensing tasks carried by task
requesters, that also keeps into account the quality of IoT devices. The
mechanism is truthful, budget balance, individual rational, computationally
efficient, and prior-free. The simulations are carried out to measure the
performance of the proposed mechanism against the benchmark mechanisms. The
code and the synthetic data are available at
\textbf{https://github.com/Samhitha-Jasti/QUAD-Implementation}.
