Despite the numerous advantages of microchip implants, their adoption remains low in the public sector. We conducted a cross-sectional survey to identify concerns and expectations about microchip implants among potential users. A total of 179 United States adults aged 18–83 years responded to two qualitative questions that were then analyzed using the thematic analysis technique. The identified codes were first categorized and then clustered to generate themes for both concerns and expectations. The prevalence of each theme was calculated across various demographic factors. Concerns were related to data protection, health risks, knowledge, negative affect, ease of use, metaphysical dilemmas, monetary issues, and negative social impact. Expectations included medical and non-medical uses, dismissal of microchips, technical advances, human enhancement, regulations, and affordability. The prevalence of concerns and benefits differed by immigration status and medical conditions. Informed by our findings, we present a modification to the Technology Acceptance Model for predicting public’s behavioral intention to use subcutaneous microchips. We discuss the five newly proposed determinants and seven predictor variables of this model by surveying the literature.

Abstract-Among some of sensor network properties which make it different from other networks, can refer to very high number of nodes, dynamic, and probably periodic topological changes and also some constraints in physical size of nodes, energy resource and power of processing. According to these restrictions, giving solutions and self-configurable protocols that do global tasks without requiring a central controller or manager are necessary. Topology control and node scheduling that constitute a part of the maintenance phase of self-organization protocols, are providing the main goal of this phase which is increasing network lifetime and also maintaining the infrastructure support for the network. In consideration of learning Automata's abilities such as low computational load, the ability of being used in distributed environments, with no precise information, the adaptability to changes via low environmental feedbacks and etc. and also its functionality that has some correspondence with essential methods which are used in self-organization systems, such as positive and negative feedbacks, interacting of special nodes with each other and with the environment, and probabilistic methods, results in the fact that using them is proper for improving the performance of sensor networks. So, in this paper a neighbour based topology control protocol has been proposed, in which an irregular cellular learning automaton is mapped to network, and with it nodes which are equipped with Automata, try to adapt their selected actions with required conditions for creating a connected, energy efficient network through selecting the best radio transmission range for themselves. This approach finally forms a proper topology which causes to lower network's energy consumption in its lifetime. The exclusive characteristic of this method is, the high number of transmission ranges that each node can select as transmission radius. Simulation's results show favorite functionality of the proposed protocol in comparison with some others from the above point of view.

The high number of nodes and dynamic and periodic topological changes, as well as constraints in the physical size of nodes, energy resources, and power of processing are some characteristics of sensor networks that make them different from other networks. One method to overcome these constraints is topology control with the aim of reducing energy consumption and increasing the network’s capacity, which has the most influence on the network’s efficiency, especially in terms of energy consumption and lifetime. In  consideration of learning  Automata’s abilities, such as low computational load and adaptability to changes via low environmental feedbacks, in this paper, neighbor-based topology control protocols based on learning Automata have been proposed somehow that all nodes are equipped with Automata. The nodes try to adapt their selected actions with required conditions for creating a connected and energy efficient network by selecting the best radio range for themselves. This approach finally forms a proper topology, and in this way it lowers the network’s energy consumption in its lifetime. The exclusive characteristic of these methods is the high number of transmission ranges that each node can select as transmission radius. In the first proposed protocol, a P-model environment is used for learning phase, but in the second proposed protocol, a Q-model environment is applied. Simulation results show favorite functionality of proposed protocols in comparison with some other similar protocols from topology control point of view, as well as high improvement of achieved results for the Q-model environment.

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