Wireless body area sensor networks based human activity recognition using deep learning.

In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).

Adapting blockchain's proof-of-work mechanism for multiple traveling salesmen problem optimization.

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners' efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.