  Incorporating mobile edge computing (MEC) in Internet of Things (IoT) enables
resource-limited IoT devices to offload their computation tasks to a nearby
edge server. In this paper, we investigate an IoT system assisted by the MEC
technique with its computation task subjected to sequential task dependency,
which is critical for video stream processing and other intelligent
applications. To minimize energy consumption per IoT device while limiting task
processing delay, task offloading strategy, communication resource, and
computation resource are optimized jointly under both slow and fast fading
channels. In slow fading channels, an optimization problem is formulated, which
is non-convex and involves one integer variable. To solve this challenging
problem, we decompose it as a one-dimensional search of task offloading
decision problem and a non-convex optimization problem with task offloading
decision given. Through mathematical manipulations, the non-convex problem is
transformed to be a convex one, which is shown to be solvable only with the
simple Golden search method. In fast fading channels, optimal online policies
depending on instant channel state are derived even though they are entangled.
In addition, it is proved that the derived policy will converge to the offline
policy when channel coherence time is low, which can help to save extra
computation complexity. Numerical results verify the correctness of our
analysis and the effectiveness of our proposed strategies over existing
methods.
