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Wireless sensor networks (WSNs) provide network services through the cooperation of sensor nodes, while the basis of cooperation depends on the trust relationships among the nodes. In this paper, we construct an evolutionary game-based trust strategy model among the nodes in WSNs, and we subsequently introduce a strategy adjustment mechanism into the process of game evolution to make up for the deficiency that the replicator dynamic model cannot reflect the requirement of individual strategy adjustments. Afterward, we derive theorems and inferences in terms of the evolutionary stable state through dynamic analyses, providing a theoretical basis for WSN trust management. Furthermore, we verify the theorems and inferences with different parameter values, especially the trust incentive and the upper limit of data retransmission after packets are lost, and both of them are closely related to the evolutionary stable state. The experiments demonstrated that, under certain conditions, the involved nodes can finally reach a stable state of the system by constantly adjusting their trust strategy. At the same time, the speed of evolution of our strategy adjustment mechanism in achieving the stable state is much faster than that of the usual replicator dynamic evolution method.

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Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k -anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

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BACKGROUND: In recent years, there have been numerous studies exploring different teaching methods for improving diagnostic reasoning in undergraduate medical students. This systematic review examines and summarizes the evidence for the effectiveness of these teaching methods during clinical training. METHODS: PubMed, Embase, Scopus, and ERIC were searched. The inclusion criteria for the review consist of articles describing (1) methods to enhance diagnostic reasoning, (2) in a clinical setting (3) on medical students. Articles describing original research using qualitative, quantitative, or mixed study designs and published within the last 10 years (1 April 2009-2019) were included. Results were screened and evaluated for eligibility. Relevant data were then extracted from the studies that met the inclusion criteria. RESULTS: Sixty-seven full-text articles were first identified. Seventeen articles were included in this review. There were 13 randomized controlled studies and four quasiexperimental studies. Of the randomized controlled studies, six discussed structured reflection, four self-explanation, and three prompts for generating differential diagnoses. Of the remaining four studies, two employed the SNAPPS 1 technique for case presentation. Two other studies explored schema-based instruction and using illness scripts. Twelve out of 17 studies reported improvement in clinical reasoning after the intervention. All studies ranked level two on the New World Kirkpatrick model. DISCUSSION: The authors posit a framework to teach diagnostic reasoning in the clinical setting. The framework targets specific deficiencies in the students' reasoning process. There remains a lack of studies comparing the effectiveness of different methods. More comparative studies with standardized assessment and evaluation of longterm effectiveness of these methods are recommended.

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A Wireless Sensor Network (WSN), characterized as being self-organizing and multihop, consists of a large number of lowpower and low-cost nodes. The cooperation among nodes is the foundation for WSNs to achieve the desired functionalities, such as the delivery or forwarding of packets. However, due to the limited resources such as energy, computational availability, and communication capabilities, there may exist some selfish nodes that refuse to cooperate with others. If the critical masses of nodes do not cooperate in the network, the network would not be able to operate to achieve its functional requirements. To resolve the problem above, we introduce a Win-Stay, Lose-Likely-Shift (WSLLS) approach into a Prisoner's Dilemma (PD) game framework, and it applies a utility-based function, which is a linear combination of one player's payoff and its neighbors' in a game, to evaluate a player's (i.e., node) performance for a game. Experimental results demonstrate that our approach performs well in stimulating cooperation in different settings under a certain condition with limited information, regardless of the static topologies types of WSNs, initial proportion of cooperation, and the average number of neighbors.