  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 Bee-resilience 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.
