In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl−) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl− sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl− wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures.