Body Area Networks (BANs) are expected to play a major role in patient health monitoring in the near future. Providing an efficient key agreement with the prosperities of plugn-play and transparency to support secure inter-sensor communications is critical especially during the stages of network initialization and reconfiguration. In this paper, we present a novel key agreement scheme termed Ordered-Physiological-Feature-based Key Agreement (OPFKA), which allows two sensors belonging to the same BAN to agree on a symmetric cryptographic key generated from the overlapping physiological signal features, thus avoiding the pre-distribution of keying materials among the sensors embedded in the same human body. The secret features computed from the same physiological signal at different parts of the body by different sensors exhibit some overlap but they are not completely identical. To overcome this challenge, we detail a computationally efficient protocol to securely transfer the secret features of one sensor to another such that two sensors can easily identify the overlapping ones. This protocol possesses many nice features such as the resistance against brute force attacks. Experimental results indicate that OPFKA is secure, efficient, and feasible. Compared with the state-of-the-art PSKA protocol, OPFKA achieves a higher level of security at a lower computational overhead.Index Terms-Body Area Networks (BANs); secure intersensor communications; Inter-Pulse-Interval (IPI); physiological feature based key agreement.

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BackgroundThe TNM staging system is based on three anatomic prognostic factors: Tumor, Lymph Node and Metastasis. However, cancer is no longer considered an anatomic disease. Therefore, the TNM should be expanded to accommodate new prognostic factors in order to increase the accuracy of estimating cancer patient outcome. The ensemble algorithm for clustering cancer data (EACCD) by Chen et al. reflects an effort to expand the TNM without changing its basic definitions. Though results on using EACCD have been reported, there has been no study on the analysis of the algorithm. In this report, we examine various aspects of EACCD using a large breast cancer patient dataset. We compared the output of EACCD with the corresponding survival curves, investigated the effect of different settings in EACCD, and compared EACCD with alternative clustering approaches.ResultsUsing the basic T and N definitions, EACCD generated a dendrogram that shows a graphic relationship among the survival curves of the breast cancer patients. The dendrograms from EACCD are robust for large values of m (the number of runs in the learning step). When m is large, the dendrograms depend on the linkage functions.The statistical tests, however, employed in the learning step have minimal effect on the dendrogram for large m. In addition, if omitting the step for learning dissimilarity in EACCD, the resulting approaches can have a degraded performance. Furthermore, clustering only based on prognostic factors could generate misleading dendrograms, and direct use of partitioning techniques could lead to misleading assignments to clusters.ConclusionsWhen only the Partitioning Around Medoids (PAM) algorithm is involved in the step of learning dissimilarity, large values of m are required to obtain robust dendrograms, and for a large m EACCD can effectively cluster cancer patient data.

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Accurate estimation of mortality in patients with cancer is important when discussing prognosis and selecting treatment. Survival estimation for many cancers is based on Tumor-Node-Metastasis (TNM) staging systems that involve three factors: tumor extent, lymph node involvement, and metastasis. The most recent clinical staging of melanoma uses TNM staging but does not include a growing number of other prognostic features. The Ensemble Algorithm of Clustering of Cancer Data (EACCD) by Chen et al. is a machine learning algorithm that regroups patients with different prognostic factors according to the survival dissimilarity. This algorithm has the potential to integrate emerging prognostic factors to more accurately stage melanoma. In this study, we use EACCD to examine the current AJCC staging of melanoma by analyzing a melanoma dataset from the National Cancer Centers Surveillance, Epidemiology, and End Rresults (SEER) database. Our results demonstrates that the EACCD algorithm generates results in-line with AJCC staging and may provide a mechanism to incorporate other prognostic factors to produce a more nuanced estimation of prognosis and survival.

Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people.Trial registrationClinicaltrials.gov (NCT01038583)

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Software-defined radios (SDRs) are a promising technology to enable dynamic channel access and sharing. Multiple Input Multiple Output (MIMO) is another radio technology breakthrough for increasing wireless network capacity. To obtain the full benefits brought by the SDR and MIMO technologies in wireless mesh networks (WMNs), the higher layer mechanisms should exploit their capabilities in a systematic way. In this paper, we investigate routing, channel assignment, link scheduling, and MIMO transmission mode control in multi-channel multi-antenna wireless mesh networks. We first present a cross-layer framework for network performance optimization. Network traffic routing is established on a relatively long time scale to maintain system stability. We then proposed a stream controlled multiple access (SCMA) scheme, which is responsible for assigning frequency channels and scheduling links for data transmission and controlling MIMO transmission modes. It enables efficient spectrum access and sharing in time, frequency, and space, and adapts to varying network conditions. The evaluation results show that the proposed scheme greatly improves network performance compared to the traditional schemes.