An increasing number of IoT devices are being introduced to the market in many industries, and the number of devices is expected to exceed billions in the near future. With this trend, many researchers have proposed new architectures to manage IoT devices, but the proposed architecture requires a huge memory footprint and computation overheads to look-up billions of devices. This paper proposes a hybrid hashing architecture called H- TLA to solve the problem from an architectural point of view, instead of modifying a hashing algorithm or designing a new one. We implemented a prototype system that shows about a 30% increase in performance while conserving uniformity. Therefore, we show an efficient architecture-level approach for addressing billions of devices.

Object detection is the first step in the object recognition. According to the detection results, its following works are affected. However, object detection has a heavy resource requirement in terms of, computing power and memory. If an image is enlarged, the computational load required for object detection is also increased. An-integral-image-based method guarantees fast object detection. Once an integral image is generated, the speed of the object detection procedure remains fixed, regardless of the pattern region size. However, this becomes an even greater issue if the image is enlarged. In this paper, we propose the use of directional integral image based object detection. A directional integral image gives direction to an integral image, which can then be calculated from various directions. Furthermore, many unnecessary calculations, which typically occur when a partial integral image is used for object detection, can be avoided. Therefore, the amount of computation is reduced, compared with methods using integral images. In experiments comparing methods, the proposed method required 40% fewer computations.

Multiple Input Multiple Output (MIMO) represents a highly promising technique for 4G communication networks as it uses multiple antennas at the transmitter and receiver to improve the reliability of transmissions and to provide a high data rate. This paper introduces an adjustable scheduling algorithm for multi-user MIMO systems that can provide an advantageous trade-off solution between throughput maximization and fair resource allocation among users. Specifically, our algorithm is proposed as a solution to system requirement issues through the flexible control of fairness factors.

In this paper, we analyze the best relay selection scheme for the soft-decision-and-forward (SDF) cooperative networks with multiple relays. The term `best relay selection' implies that the relay having the largest end-to-end signal-to-noise ratio is selected to transmit in the second phase transmission. The approximate performances in terms of pairwise error probability (PEP) and bit error rate (BER) are analyzed and compared with the conventional multiple-relay transmission scheme where all the relays participate in the second phase transmission. Using the asymptotics of the Fox's H-function, the diversity orders of the best relay selection and conventional relay scheme for the SDF cooperative networks are derived. It is shown that both have the same full diversity order. The numerical results show that the best relay selection scheme outperforms the conventional one in terms of bit error rate.