  The emergence of the Fog computing paradigm that leverages in-network
virtualized resources raises important challenges in terms of resource and IoT
application management in a heterogeneous environment offering only limited
computing resources. In this work, we propose a novel Pareto-based approach for
application placement close to the data sources called Multiobjective IoT
application Placement in fOg (MAPO). MAPO models applications based on a finite
state machine and uses three conflicting optimization objectives, namely
completion time, energy consumption, and economic cost, considering both the
computation and communication aspects. In contrast to existing solutions that
optimize a single objective value, MAPO enables multi-objective energy and
cost-aware application placement. To evaluate the quality of the MAPO
placements, we created both simulated and real-world testbeds tailored for a
set of medical IoT application case studies. Compared to the state-of-the-art
approaches, MAPO reduces the economic cost by up to 27%, while decreasing the
energy requirements by 23-68%, and optimizes the completion time by up to 7.3
times.
