Mobile Crowd Computing (MCC) leverages the collaborative power of nearby devices to solve resource-intensive tasks, offering transformative potential across various fields. However, ensuring device reliability—which directly impacts the trustworthiness of devices in MCC environments—poses a significant challenge. Balancing validation accuracy with performance and energy efficiency is particularly difficult due to MCC’s dynamic and resource-constrained decentralized nature. Existing validation methods are not feasible in MCC, as they can negatively affect speed and energy consumption. This paper introduces the Honeybee-RS framework, a novel approach for validating offloaded computational results in MCC environments. Honeybee-RS provides delegator-based validation mechanisms and derives reliability scores from the validation process. Experiments demonstrate the effectiveness of these mechanisms, significantly enhancing reliability in MCC while maintaining performance.

Mobile devices are ubiquitous, heterogeneous and resource constrained. Execution of complex tasks in mobile devices are resource demanding and time-consuming, forcing developers to offload portions of the complex task to cloud or edge computing resources. Task offloading becomes increasingly challenging due to intermittent Internet connectivity, remote resource unavailability, high costs, latency, and limited energy of the mobile device. A mobile device user is typically surrounded by other mobile devices, which can be leveraged to collaboratively compute a resource-intensive task. With the help of a work sharing framework, it is feasible for devices to communicate and collaborate. However, some mobile devices are incapable of computing complex portions of the task, and some can compute in accelerated mode. In this demonstration, we introduce Honeybee-T a collaborative mobile crowd computing framework that uses a work-stealing algorithm. The algorithm allows work sharing with collaborating devices based on devices' computational ability and task-dependencies. The experiments show that by employing Honeybee-T framework, when compared to monolithic execution of a large compute-intensive task, there is a considerable performance gain, as well as energy savings.