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In future Internet of Thing (IoT) networks, devices can be leveraged to compute tasks or services. To this end, this paper 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: (i) a sample average approximation (SAA) method, and (ii) a heuristic solution called MinC. We have studied the performance of SAA and MinC with Round Robin, which assigns tasks to devices in a round-robin manner. Specifically, we find that the maximum energy consumption of devices when using MinC and Round Robin is respectively around 14.2% and 23.1% higher tha

Round robinApproximate computingHance constraintsEpendent tasksEnergy consumptionNternet of thingsMonte carloResource managementTochastic computingTask analysisWireless sensor networks