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Wellness is a term often used to talk about optimal health as "dynamic balance of physical, emotional, social, spiritual, and intellectual health." While healthcare is a term about care offered to patients for improving their health. We use both terms, as well as the Business Model Canvas (BMC) methodology, to design a digital ecosystem model for healthcare and wellness called DE4HW; the model considers economic, technological, and legal asymmetries, which are present on e-services beyond geographical regions. BMC methodology was embedded into the global project strategy called: IBOT (Initiate, Build, Operate and Transfer); it is a methodology to establish a functional, integrated national telemedicine network and virtual education network; of which we took its phases rationale. The results in this work illustrate the design of DE4HW model, into the first phase of IBOT, enriched with the BMC, which enables us to define actors, their interactions, rules and protocols, in order to build DE4HW, while IBOT strategy manages the project goal, up to the transfer phase, where an integral service platform of healthcare and wellness is turned over to stakeholders.

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After an introduction of cognitive radio (CR) technology in communication, the hot research topics are sensing, Primary User Interference (PUI), spectrum management, security, spectrum sharing, and environmental sensing. Among the listed, sensing and Primary User Interference are the bold ones. The base query for these two problems lays finding a means for which and what channel at a particular time is available and avoiding interference with Primary Users (PU). This article presents a novel cognitive radio algorithm called SenPUI for both mentioned main challenges, sensing and PUI. First, energy scan during the inactive portion of communication which is dynamic is done. Second, application packet based primary user identification for PUI avoidance is proposed as base solution. Both techniques described in this work are implemented and resulted in a significant reduction of the target problems estimated around 10-30% reduction in average. Main limitations of Wireless Sensor Network (WSN) such as memory, battery lifetime, and size are considered during the design and implementation of our solutions.