No / The main attributes that have been used to conserve the energy in wireless sensor networks (WSNs) are clustering, synchronization and low-duty-cycle operation. Clustering is an energy efficient mechanism that divides sensor nodes into many clusters. Clustering is a standard approach for achieving energy efficient and hence extending the network lifetime. Synchronize the schedules of these clusters is one of the primary challenges in WSNs. Several factors cause the synchronization errors. Among them, clock drift that is accommodated at each hop over the time. Synchronization by means of scheduling allows the nodes to cooperate and transmit data in a scheduled manner under the duty cycle mechanism. Duty cycle is the approach to efficiently utilize the limited energy supplies for the sensors. This concept is used to reduce idle listening. Duty cycle, nodes clustering and schedules synchronization are the main attributes we have considered for designing a new medium access control (MAC) protocol. The proposed OLS-MAC protocol designed with the target of making the schedules of the clusters to be overlapped with introducing a small shift time between the adjacent clusters schedules to compensate the clock drift. The OLS-MAC algorithm is simulated in NS-2 and compared to some S-MAC derived protocols. We verified that our proposed algorithm outperform these protocols in number of performance matrix.

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Chronic pain (CP) is a personal and economic burden that affects more than 30% of the world's population. While being the leading cause of disability, it is complicated to diagnose and manage. The optimal way to treat CP is to identify the pain mechanism or the underlying cause. The substantial overlap of the pain mechanisms (i.e., Nociceptive, Neuropathic, and Nociplastic) usually makes identification unreachable in a clinical setting where finding the dominant mechanism is complicated. Additionally, many specialists regard CP classification as a spectrum or continuum. Despite the importance, a data-driven way to identify co-existing CP mechanisms and quantification is still absent. This work successfully identified the co-existing CP mechanisms within a patient using Unsupervised Learning while quantifying them without the help of diagnosis established by the clinicians. Two different datasets from different cohorts comprised of patient-reported history and questionnaires were used in this work. Unsupervised Learning (k-prototypes) revealed notable overlaps in the data. It was further emphasized by the outcomes of the Semi-supervised Learning algorithms when the same trend was observed with some diagnosis or class information. It became evident that the CP mechanisms overlap and cannot be classified as distinct conditions. Additionally, mixed pain mechanisms do not make an individual cluster or class, and CP should be considered as a continuum. To reduce data dimension and extract hidden features, Autoencoder was used. Using an overlapping clustering technique, the pain mechanisms were identified. The pain mechanisms were also quantified while elucidating overlaps, and the dominant CP mechanism was successfully pointed out with explainable element. The hamming loss of 0.43 and average precision of 0.5 were achieved when considered as a multi-label classification problem. This work is a data-driven validation that there are significant overlaps in CP conditions, and CP should be considered a continuum where all CP mechanisms may co-exist. / Thesis / Master of Applied Science (MASc) / Chronic pain (CP) is a global burden and the primary cause for patients to seek medical attention. Despite continuous efforts in this area, CP remains clinically challenging to manage. The most effective method of treating CP is identifying the underlying cause or mechanism, which is often unattainable. This thesis attempted to identify the CP mechanisms existing in a patient while quantifying them from patient-reported history and questionnaire data. Unsupervised Learning was used to identify clinically meaningful clusters that revealed the three main CP mechanisms, i.e., Nociceptive, Neuropathic, and Nociplastic, achieving acceptable hamming loss (0.43) and average precision (0.5). The results exhibited that the CP mechanisms co-exist and CP should be regarded as a continuum rather than distinct entities. The algorithm successfully indicated the dominant CP mechanism, a goal for optimal CP management and treatment. The results were also validated by a comparative analysis with data from another cohort that demonstrated a similar trend.