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.

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.

An enhanced interference alignment strategy with minimum interference leakage (MIL) criterion and Riemannian conjugate gradient (RCG) algorithm is proposed for intelligent reflecting surface (IRS)-assisted multiuser multiple-input multiple-output (MIMO). In this letter, the maximum sum rate is formulated as the optimization objective, with alternate optimization of phase shift vector at IRS as well as precoding and interference suppression vectors at transceivers respectively. MIL interference alignment criterion is used to iteratively solve the precoding vector and the interference suppression vector via channel reciprocity property. RCG algorithm is further applied to derive the IRS phase shift vector and maximize sum rate with the condition of ensuring a given minimum interference leakage threshold to eliminate system interference. Simulation results reveal that the proposed strategy effectively enhances sum rate performance compared with the scheme using random IRS phase shift vector and random precoding/interference suppression vectors as well as ”AP+RCG” scheme in IRS-assisted multiuser single-input single-output (SISO) scenario. In addition, compared with ”MMSE+RCG” strategy in the case of same multi-antenna transceiver pairs and antenna numbers, the proposed strategy could make a tradeoff between sum rate performance and computational complexity.

The spectrum scarcity and low utilization rates constrain further development possibilities in wireless sensor networks, the cognitive radio (CR) enables multiple sensor nodes (SNs) to detect whether the primary node (PU) is occupying spectrum bands by cooperative spectrum sensing (CSS) and provides an opportunity to access unused channels. To address the challenges posed by the inherent characteristics of wireless channels, CSS have emerged in a cognitive wireless sensor network (CWSN). However, this also provides opportunities for malicious sensor nodes (MSNs) to manipulate spectrum sensing data by Byzantine attack. Therefore, in this paper, we employ support vector machine (SVM) to define the optimal hyperplane to distinguish MSNs from normal sensor nodes (NSNs), and combine it with a novel algorithm associated with reputation values to exclude sensing results of MSNs, thereby improving the CSS performance of CWSNs. Furthermore, we also compare this enhanced SVM (ESVM) algorithm with other SVM approaches, evaluating the system’s detection probability, variations with changing parameters and the stability of sensing performance. A series of simulation results demonstrate that ESVM exhibits better robustness against Byzantine attack, and the sensing accuracy is increased by 10.77%-14.21% compared with the existing method in the same scenario.

Mobile edge computing (MEC) distributes computing and storage resources to the edge of the network closer to the user and significantly reduces user task completion latency and system energy consumption. This paper investigates the problem of computation offloading in a three-tier mobile edge computing network composed of multiple users, multiple edge servers, and a cloud server. In this network, each user’s task can be divided into multiple subtasks with serial and parallel priority relationships existing among these subtasks. An optimization model is established with the objective of minimizing the total user delay and processor cost under constraints such as the available resources of users and servers and the interrelationships among the subtasks. An improved gravitational search algorithm (IGSA) is proposed to solve this optimization model. In contrast with the other gravitational search algorithm, the convergence factor is introduced in the calculation of the resultant force and the crossover operation in a genetic algorithm is performed when generating the new particles during each iteration. The simulation results show that the proposed IGSA greatly improves the system performance compared with the existing algorithms.