Psychic: An autonomic inference engine for M2M management in Future Internet

IoT (Internet-of-Things) is meant to provide networked-life to the embedded monitoring devices. M2M (Machine-to-Machine) looks one step ahead, by facilitating intelligent communication among those devices, without or with the least human intervention. Therefore, M2M, by featuring smart monitoring devices, have started to play key role in different sectors, namely, Smart-Grid, Smart-Health, Smart-City, Smart-Electronic-Vehicle, etc. Consequently, numerous device-manufacturers and service-providers have arrived to resolve the increasing demand of personalized smart-devices and services. Traditional management technique is unable to scale up to such growth on M2M networks and services. In this context, we have developed an Autonomic M2M Management System that can learn to scale up to the personalized service-requirements. We have proposed and developed Psychic, an autonomic inference engine, that is capable to learn personalized service-recommendation by inferring service-usage from environmental (such as different locations, weather, time, etc.) and emotional Information(such as happiness, sadness, etc.) of users. A case-study by E-mail survey with 77 people and by traffic-analysis of 16 people's smart-device usage, is performed to evaluate the functionality of the developed system.