In this paper, we propose a smart insole for inexpensive plantar pressure sensing and a simple visualizing scheme. The insole is composed of two elastomeric layers and two electrode layers where the common top electrode is submerged in the insole. The upper elastomeric layer is non-conductive poly-dimethyl-siloxane (PDMS) and supports plantar pressure buffering and the lower layer is carbon nano-tube (CNT)-dispersed PDMS for pressure sensing through piezo-resistivity. Under the lower sensing layer are 16 bottom electrodes for pressure distribution sensing without cell-to-cell interference. Since no soldering or sewing is needed the smart insole manufacturing processes is simple and cost-effective. The pressure sensitivity and time response of the material was measured and based on the 16 sensing data of the smart insole, we virtually extended the frame size for continuous and smoothed pressure distribution image with the help of a simple pseudo interpolation scheme.

In this paper, we propose a novel MAC protocol, called DG-LoRa, for improving scalability in low power wide area networks. DG-LoRa is backward compatible with legacy LoRaWAN and adds new features, such as group acknowledgment transmissions in the time-synchronized frame structure that supports determinism on channel access. In DG-LoRa, the number of responses to data frames that are transmitted from end devices is maximized by allocating the spreading factor and timeslot in the frame structure. We evaluate the performance of DG-LoRa using the Monte-Carlo simulation and then compare it with the performance of legacy LoRaWAN in terms of data drop rate and the number of retransmissions. Our numerical results show that DG-LoRa supports approximately five times more connections to the LoRa network satisfying a 5% data drop rate. Also, it is observed that DG-LoRa enables low overhead by reducing the number of data frame retransmissions.

A sensor network consists of a large number of sensor nodes that are densely deployed. The large scale sensor network is generally used for applications such as target tracking and emergency response rather than common data. The applications have real-time requirements that sensed data must be exactly delivered to a destination node within a given time. In this paper, we propose a multi-path scheme, which utilizes the virtual grid, to meet the real-time requirements. In order to select one of multi-paths depending on the service differentiation, the proposed scheme uses several information such as the size and transfer period of sensed data. In addition to an existing path, the proposed algorithm dynamically selects an alternative path according to multi-path environments. Moreover, it assigns the shortest path to the sensed data with most strict time restriction. Therefore, our scheme can guarantee the transmission reliability since sensed data is transmitted two or more routes by using the dualized multi-path on the virtual grid. In this paper, we introduce some realistic scenarios and propose an analytical model. Then performance of the proposed scheme is evaluated by numerical analysis. Our results show that the proposed scheme significantly outperforms the SPEED and MMSPEED.

A wireless sensor network consists of a large number of sensor nodes that are randomly deployed. This network is formed to perform the critical mission. The essential elements for the wireless sensor network are a source and sink. Source senses stimulus initiating a critical mission and transmits collected data to a sink. In the wireless network, the environment where a single sink and a large number of sources are deployed is considered in general. Sources may communicate with a sink connected to the Internet. In this paper, we consider the aforementioned environment with a sink connected to the Internet and propose an efficient algorithm for controlling the congestion between a sink and a gateway. Moreover, we propose a routing protocol based on a situation aware profile which is obtained from sensed data.

The dyeing process is a process that reproduces the color of the fabric desired by the buyer and realizes the feeling of touch. In particular, the dyeing process is a parallel process in which there are many dyeing machines with different capacities. Further, the dyeing process is affected by the dyeing color and concentration in the previous process. We propose a continuous dyeing process that can improve productivity and dyeing quality. The proposed algorithm is based on genetic algorithms and deals with both the whole process and the staining process. The dyeing process is an optimization problem that divides and allocates the order quantity according to the capacity of multiple dyeing machines. The genetic algorithm is suitable as an algorithm for finding an optimized solution for this problem. The proposed algorithm simplifies and divides the order quantity by integer programming, and constructs chromosomes. It then runs a genetic algorithm and allocates the dyeing machines so that the continuous dyeing process can be performed while minimizing delivery dates and machine cleaning, which are the objective functions. The proposed algorithm was able to confirm a five hours saving effect based on the existing three days production situation data.