Exact and Approximate Tasks Computation in IoT Networks

In future Internet of Thing (IoT) networks, devices can be leveraged to compute tasks or services. To this end, this article addresses a novel problem that requires devices to collaboratively execute tasks with dependencies. A key consideration is that in order to conserve energy, devices may execute a task in approximate mode, which generate errors. To optimize their operation mode, we outline a novel chance-constrained program that aims to execute as many tasks as possible in approximate mode subject to a probabilistic constraint relating to the said errors. We also outline two novel solutions to determine task execution modes: 1) a sample average approximation (SAA) method and 2) a heuristic solution called minimum communication cost (MinC). We have studied the performance of SAA and MinC with round robin (RR), which assigns tasks to devices in an RR manner. Specifically, we find that the maximum energy consumption of devices when using MinC and RR is, respectively, around 14.2% and 23.1% higher than SAA, which yields the optimal solution. Further, MinC results in approximately 27.9% lower energy consumption as compared to RR.