Autonomic Resilient Internet-of-Things(IoT)Management

In Resilient IoT, the revenue of service provider is resilient to uncertain usage-contexts(e.g. emotion, environmental contexts) of Smart-device users. Hence, Autonomic Resilient IoT Management problem is decomposed into two subproblems, namely m-connectivity and k-dominance, such that m-alternations on revenue making process is resilient to users common interests, which might be depicted through k-1 alternations of usage-contexts. In this context, a greedy approximation scheme Bee is proposed, which resolves aforementioned sub-problems with five consecutive models, namely Maverick, Siren, Pigmy, Arkeo and Augeas, respectively. Theoretical analysis justifies the problem as NP-hard, combinatorial optimization problem, which is amenable to greedy approximation. Moreover, Bee lays out the theoretical foundation of Resilient Fact-finding, followed by theoretical and experimental(i.e synthetic) proof, which show how Beeresilience resolves acute CDS measurement problem. Accordingly, experiments on real Social rumor dataset extract dominator and dominate to justify how Bee resilience improves CDS measurement. Finally, case-study and prototype development are performed on Android and Web platforms in a Resilient IoT scenario, where service provider recommends personalized services for Smart-device users. Index Terms Internet-of-Things Management, Big Data Measurement, Autonomic Management, Connected Dominating Set,Resilience Measurement