Resource Constrained Offloading in Fog Computing

When focusing on the Internet of Things (IoT), communicating and coordinating sensor--actuator data via the cloud involves inefficient overheads and reduces autonomous behavior. The Fog Computing paradigm essentially moves the compute nodes closer to sensing entities by exploiting peers and intermediary network devices. This reduces centralized communication with the cloud and entails increased coordination between sensing entities and (possibly available) smart network gateway devices. In this paper, we analyze the utility of offloading computation among peers when working in fog based deployments. It is important to study the trade-offs involved with such computation offloading, as we deal with resource (energy, computation capacity) limited devices. Devices computing in a distributed environment may choose to locally compute part of their data and communicate the remainder to their peers. An optimization formulation is presented that is applied to various deployment scenarios, taking the computation and communication overheads into account. Our technique is demonstrated on a network of robotic sensor--actuators developed on the ROS (Robot Operating System) platform, that coordinate over the fog to complete a task. We demonstrate 77.8% latency and 54% battery usage improvements over large computation tasks, by applying this optimal offloading.