Energy efficiency and balancing is one of the primary challenges for wireless sensor networks (WSNs) since the tiny sensor nodes cannot be easily recharged once they are deployed. Up to now, many energy efficient routing algorithms or protocols have been proposed with techniques like clustering, data aggregation and location tracking etc. However, many of them aim to minimize parameters like total energy consumption, latency etc., which cause hotspot nodes and partitioned network due to the overuse of certain nodes. In this paper, a Distance-based Energy Aware Routing (DEAR) algorithm is proposed to ensure energy efficiency and energy balancing based on theoretical analysis of different energy and traffic models. During the routing process, we consider individual distance as the primary parameter in order to adjust and equalize the energy consumption among involved sensors. The residual energy is also considered as a secondary factor. In this way, all the intermediate nodes will consume their energy at similar rate, which maximizes network lifetime. Simulation results show that the DEAR algorithm can reduce and balance the energy consumption for all sensor nodes so network lifetime is greatly prolonged compared to other routing algorithms.
AimsMycobacterium tuberculosis and non-tuberculous mycobacteria (NTM) are clinically different, and the rapid detection and differentiation of M. tuberculosis complex (MTBC) and NTM is crucial for patient management and infection control. Given the slow growth of most pathogenic mycobacteria, nucleic acid amplification assays are excellent tools for direct identification of mycobacteria in clinical specimens. Recently, a multiplex real-time PCR assay was developed that can directly detect 20 mycobacterial species in clinical specimens. Here, we evaluated the diagnostic performance of the assay for diagnosing mycobacterial disease under routine laboratory conditions.MethodsA total of 3334 specimens collected from 1437 patients suspected of tuberculosis infection were subjected to acid-fast bacilli staining, conventional culture and the multiplex real-time PCR assay. To evaluate the sensitivity and specificity of the assay, the overall diagnosis of tuberculosis was defined by positive culture plus medical history, and the 2007 American Thoracic Society and Infectious Disease Society of America diagnostic criteria for NTM disease were applied.ResultsThe sensitivity, specificity, positive predictive value and negative predictive value were 87.5%, 99.6%, 96.1% and 98.5%, respectively, for the detection of MTBC isolates and 53.3%, 99.9%, 95.2%, and 98.9%, respectively, for detecting NTM isolates.ConclusionsThus, the assay can correctly differentiate between MTBC and NTM isolates in clinical specimens and would be a useful tool for the rapid differentiation of tuberculosis and NTM disease, despite its limited sensitivity for the diagnosis of NTM disease.
Clustering is an efficient method adopted in various routing algorithms for wireless sensor networks. However, most clustering algorithms are not suitable for heterogeneous networks. In this paper, we propose a Density-based Energy-efficient Clustering Heterogeneous Algorithm (DECHA). In DECHA, we define the density of a node and together with its energy condition to adjust the probability for the candidate cluster head selection dynamically. Candidate cluster heads further evaluate the energy level of its neighbors and adjust to find more proper cluster heads. Moreover, we design an intra-cluster algorithm as well as a multi-hop inter-cluster routing algorithm. Simulation results show that cluster heads are properly deployed in a heterogeneous wireless sensor network. Compared with some popular algorithms, in our DECHA, the stability period and network lifetime and prolonged and total energy consumption is prominently reduced.
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