Search

Multi-vehicle tracking is one of the most crucial components of an intelligent transportation system (ITS). However, when it comes to busy traffic flow, tracking targets robustly becomes more problematic due to occlusion, motion blur, high appearance similarity, etc. To achieve accurate and efficient tracking performance, we present a novel multi-vehicle tracking method based on the enhanced detection model and joint scoring strategy. Specifically, the former aims to (1) adopt lightweight yet efficient YOLOv5s to improve detection accuracy and running speed, and (2) incorporate the CBAM and transformer encoder modules into the detection model to generate the refined features for the target localization. The Latter preferentially provides high-confidence detections and tracklets for subsequent data association, significantly reducing the number of identity switches and redundant vehicle trajectories caused by mutual occlusion, similar object interference, etc. We evaluated the proposed multivehicle tracking approach on the UA-DETRAC vehicle tracking dataset and demonstrated its superior capabilities through intensive comparison and analysis. Moreover, our proposed method runs at 24.4 FPS on a single GPU and meets the real-time requirement.

Interference alignment (IA) is one of the most promising techniques to eliminate interference in multiuser multiantenna systems. Simultaneous wireless information and power transfer (SWIPT) technology is applied to harvest energy from radio frequency signals in green communications, while interference signal is assumed to be utilized as energy supply source in SWIPT. In this paper, an enhanced maximum signal to interference plus noise ratio (MAX-SINR) strategy with interference leakage power constraint is proposed, which realizes a tradeoff between information transmission and interference leakage in multiuser multiantenna SWIPT systems. Optimization problem is formulated and a three-step optimization solution is proposed. Finally, the problem can be transformed to a standard semi-definite programming (SDP) to obtain optimal power splitting factor and system sum-rate (spectral efficiency). Simulation results are presented to show that system sum-rate performance is enhanced with the increasing of receiving antenna numbers. The tradeoff between sum-rate and the harvested energy can be realized via the proposed IA strategy.

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.

Non-orthogonal multiple access (NOMA) is a promising spectrally-efficient technology to satisfy the massive data requirement of the next-generation wireless communication networks. In this paper, we consider cooperative non-orthogonal multiple access (CNOMA) networks with relaying user selection, where a two-stage user selection scheme is adopted to optimize the received signal-to-noise ratio (SNR) of the weak user. We derive analytical expressions of the outage probability of the CNOMA users under two different types of channel estimation errors. Afterwards, we investigate the asymptotic characteristics the outage probability of the CNOMA systems in the high SNR regimes. Our results show that for the case of constant variance of the channel estimation error, the outage probability of each user is saturated and cannot be improved by increasing the transmit power of the access point. In contrast, there is no performance bottleneck of the outage probability for all the users when the variance of the channel estimation errors decease with SNR, whilst fully diversity gain is achieved by the weak user.

The classic demand-based flash translation layer (DFTL) algorithm is well-known since it can solve the contradiction between mapping flexibility and the size of mapping cache by dynamically loading mapping entries. However, DFTL failed to utilize the spatial locality and hot-cold characteristics of the request and had an inefficient mapping entry eviction scheme. This paper proposes an adaptive read-write partitioning flash translation layer algorithm (ARWFTL). First, the cache mapping table (CMT) is divided into the read CMT and the write CMT. The size of the two can be adaptively adjusted by sensing the characteristics of the upper workload and the read-write latency of the underlying flash page. Second, a priority eviction window is set at the tail of the write CMT to evict the clean mapping entry firstly. When there is no clean mapping entry in the priority eviction window, the tail mapping entry and other mapping entries that belong to the same translation page are clustered to write back into the translation page. Then, other written back mapping entries are set to be clean and the tail mapping entry is evicted. Third, a hot data window is set at the head of the write CMT to recognize the hot and cold data of write requests. Then, the hot and cold data are stored in different data blocks of flash to avoid hot and cold data entanglement and reduce valid page migrations in garbage collection. Experimental results show that, compared with DFTL, ARWFTL can reduce the translation page write counts, the valid page migration counts, the block erase counts, and the average response time by 92.8%, 47.7%, 31.7%, and 31.4%, respectively. In addition, ARWFTL is also superior to the other recent DFTL-based improved algorithms, and even exceeds the pure page-level FTL in some indicators.

Since the initial publication of Kelly et al.'s seminal paper on resource allocation in wired networks, many studies based on the cross-layer design philosophy have been conducted in both wired and wireless networks. The Lagrangian duality technique has been widely adopted to solve the cross-layer optimization problem, but it is slowly convergent and sensitive to the iterative step size, especially for large networks. In this paper, a joint congestion control and power allocation second-order algorithm (JCCPA) is proposed, in which the joint optimization problem is modeled as a network utility maximization (NUM) framework, and the primal-dual interior-point method is used to solve the NUM problem. The JCCPA updates the primal variables and dual variables simultaneously according to their Newton directions; as a result,resulting in a faster convergence speed is reached. Moreover, the matrix-splitting technology is utilized to decompose the computation of the Hessian matrix and its inverse into different nodes and links so that the distributed update of source rate and node power can be implemented. The simulation results demonstrate that the JCCPA not only significantly improves energy efficiency but also has a faster convergence speed and is insensitive to step size.

The traditional solid-state drive buffer management algorithm generally adopts fixed structures and parameters, leading to their poor adaptability. For example, after the underlying flash translation layer (FTL) or the upper workload is changed, the traditional algorithm's performance fluctuates significantly. Focusing on this problem, based on the cross-layer aware method, we propose an Advanced Adaptive Least Recently Used buffer management algorithm (AALRU). The core idea of the AALRU is that by sensing the characteristics of the upper workload and the status of the underlying FTL, the AALRU adaptively adjusts its structure, parameters, and write-back strategy to optimize the buffer's performance. First, the AALRU divides the buffer into two parts: read buffer and write buffer, and their proportion is adjusted by sensing the read-write characteristics of the workload and the underlying read-write latency. Second, the AALRU employs different granularities to manage the buffer. On one hand, for data loading and migrating, the AALRU adopts page-level granularity, which can avoid the problem of hot and cold data page entanglement in block management, and thus improve the buffer hit ratio. On the other hand, for data writing back to the FTL, the AALRU adopts block-level granularity, which can enhance the continuity of write requests and thus reduce the underlying FTL's garbage collection overhead. Finally, when the clustered data are written back, by sensing the underlying FTL's garbage collection status, the AALRU adaptively adjusts the page-padding trigger threshold to reconstruct the continuity of the write-back data, which can mitigate the underlying FTL's garbage collection overhead. The experimental results show that the AALRU has the best adaptability to different FTLs and test workloads, and it can achieve optimal or near-optimal results.

In order to solve the node localization problem in wireless sensor networks, we propose a novel distributed weighted search localization algorithm (WSLA) in this paper. The WSLA adopts a modified received signal strength indicator-based range model to estimate the distances between nodes, utilizes the results of a centroid localization algorithm as the search initial point, and employs a new weighted search method to compute the positions of nodes in a distributed and recursive manner. The key ideas of the WSLA include a node localization precision classification scheme, a processing scheme for special nodes, and weight-based searches. Compared with three state-of-art localization algorithms—namely, maximum likelihood estimation (MLE), edge-based second-order cone programming + nonconvex sequential greedy (ESOCP + NCSG), and particle swarm optimization (PSO)—the simulation results show that localization performance of the WSLA is superior to that of MLE, ESOCP + NCSG, and PSO.