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Fog computing in 5G networks has played a significant role in increasing thenumber of users in a given network. However, Internet-of-Things (IoT) hasdriven system designers towards designing heterogeneous networks to supportdiverse demands (tasks with different priority values) with different latencyand data rate constraints. In this paper, our goal is to maximize the totalnumber of tasks served by a heterogeneous network, labeled task throughput, inthe presence of data rate and latency constraints and device preferencesregarding computational needs. Since our original problem is intractable, wepropose an efficient solution based on graph-coloring techniques. Wedemonstrate the effectiveness of our proposed algorithm using numericalresults, real-world experiments on a laboratory testbed and comparing with thestate-of-the-art algorithm.

On Maximizing Task Throughput in IoT-enabled 5G Networks under Latency and Bandwidth Constraints