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Incorporating mobile edge computing (MEC) in Internet of Things (IoT) enablesresource-limited IoT devices to offload their computation tasks to a nearbyedge server. In this paper, we investigate an IoT system assisted by the MECtechnique with its computation task subjected to sequential task dependency,which is critical for video stream processing and other intelligentapplications. To minimize energy consumption per IoT device while limiting taskprocessing delay, task offloading strategy, communication resource, andcomputation resource are optimized jointly under both slow and fast fadingchannels. In slow fading channels, an optimization problem is formulated, whichis non-convex and involves one integer variable. To solve this challengingproblem, we decompose it as a one-dimensional search of task offloadingdecision problem and a non-convex optimization problem with task offloadingdecision given. Through mathematical manipulations, the non-convex problem istransformed to be a convex one, which is shown to be solvable only with thesimple Golden search method. In fast fading channels, optimal online policiesdepending on instant channel state are derived even though they are entangled.In addition, it is proved that the derived policy will converge to the offlinepolicy when channel coherence time is low, which can help to save extracomputation complexity. Numerical results verify the correctness of ouranalysis and the effectiveness of our proposed strategies over existingmethods.

Joint Task Offloading and Resource Allocation for IoT Edge Computing with Sequential Task Dependency