As an infrastructure-free positioning and navigation method, pedestrian dead reckoning (PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and stride length estimation (SLE) are two key components of PDR, and it is a challenging problem to apply SD and SLE to different walking patterns. Focusing on this problem, this paper proposes a robust SD and SLE method based on recognizing three walking patterns (i.e., Normal Walk, March in Place, and Quick Walk) using a smartphone. First, we propose a dynamic time warping–based peak prediction with zero-crossing detection to improve the SD accuracy. In particular, the proposed SD can accurately identify the starting and ending points of each step in the three walking patterns. Second, according to the extracted features of each step, a random forest algorithm with classification proofreading is used to recognize the three walking patterns. Finally, an improved SLE model is proposed for the different walking patterns to achieve a higher SLE accuracy. The experimental results show that, on average, the SD accuracy is about 97.9%, the recognition accuracy is about 98.4%, and the relative error of the estimated walking distance is about 3.0%, which outperforms those of the existing commonly used SD and SLE methods.

To minimize the average latency of users to complete tasks, this letter jointly optimizes offloading decision, computation and bandwidth resource allocation for Mobile Edge Computing (MEC) networks via partial offloading. Since the considered problem is strongly non-convex with coupled variables, we decompose the original problem into two subproblems: (1) offloading decision; (2) communication bandwidth and computation resources allocation, and further employ Block Coordinate Descent (BCD) method to tackle them sequentially with linear computation complexity. Simulation results demonstrate that the proposed algorithm can achieve better performance on the average latency, and converge speedily.

With the growing concerns about energy saving and security problems in wireless sensor networks (WSNs), we investigated a joint energy saving scheduling and secure routing algorithm for critical event reporting in WSN. When a critical event has been detected, the notification must be safely sent to the sink node through the uplink transmission, and the sink node needs to broadcast the alarm messages to the whole network through the downlink transmission. In the uplink, a joint power allocation and secure routing strategy (JPASR) has been proposed to maximize the routing secure connection probability (RSCP) under the constraint of power. Meanwhile, combined with the level-by-level sleeping scheduling method, the energy-saving and secure uplink transmission can be guaranteed. In the downlink, an energy-first multi-point relays set selection mechanism (EFMSS) is designed to choose the backbone nodes to broadcast messages, and the backbone nodes are waken up by the same level-by-level sleeping scheduling method as the uplink transmission. With the two-step procedure, the critical events are appropriately dealt with and the responses are broadcast to the whole network. Extensive simulations results demonstrate that the energy saving and security performances of the proposed method are superior to the existing ones.

NAND flash storage devices are gaining popularity in consumer electronic devices, such as digital cameras, tablet PCs, and smartphones, due to their attractive features. However, write operations significantly limit the performance of the flash storage in consumer electronics devices due to their long latency and heavy overhead of garbage collection. Hence, NAND flash storage devices generally use RAM memory as a buffer to improve their write performance and to extend their lifetime. This paper proposes a novel buffer management algorithm (BMA), called as virtual-block-based buffer management scheme (VBBMS), which can make full use of both temporal and spatial localities at virtual-block level. The main difference with the existing BMAs is that the management granularity of VBBMS is a virtual block, while the management granularity of existing BMAs is a page or a block. In addition, VBBMS divides RAM into random request service region (RRSR), which is responsible for handling random requests, and sequential request service region (SRSR), which is responsible for handling sequential requests. When replacement happens, VBBMS selects victim virtual-block according to LRU policy in RRSR and first-in first-out (FIFO) policy in SRSR. VBBMS has been extensively evaluated under various realistic workloads, and our benchmark results show that VBBMS can improve buffer hit ratio, reduce response time, and erase times, compared to five existing BMAs.

In indoor positioning, smartphone-based pedestrian dead reckoning (PDR) is a popular method. It does not require any pre-deployed infrastructure and can provide real-time, continuous position estimation with a known initial position. However, magnetometer sensor data is susceptible to magnetic interference from metal facilities, electrical equipment, etc., thus reducing the accuracy of heading estimation. To solve this problem, we propose a fusion estimation method for pedestrian heading based on magnetic interference detection classification. Firstly, a support vector machine (SVM) method is used to detect magnetic interference, and an improved Relief-F feature selection method is proposed based on the information coefficient and Markov blanket condition. Secondly, according to SVM-based magnetic interference detection results, different methods for scenarios without and with magnetic interference are proposed to improve the accuracy of pedestrian heading estimation. The experimental results show that the proposed SVM-based magnetic interference detection accuracy reaches 98%, the average error of heading estimation is less than 4 degrees, and the average positioning error of PDR integrating the proposed heading estimation method is 0.67m and 2.75m when the walking distance is 84m and 252m, respectively. The above results prove the effectiveness of the proposed method.