Reinforcement learning has been applied to various decision-making tasks and has achieved high profile successes. More and more studies have proposed to use reinforcement learning (RL) for traffic signal control to improve transportation efficiency. However, these methods suffer from a major exploration problem, and their performance is particularly poor. And even fail to quickly converge during the initial stage when interacting with the environment. To overcome this problem, we propose an RL model for traffic signal control based on demonstration data, which provides prior expert knowledge before RL model training. The demonstrations are collected from the classic method self-organizing traffic light (SOTL). It not only serves as expert knowledge but also explores and improves the entire decision-making system. Specifically, we use small demonstration data sets to pre-train the Ape-X Deep Q-learning Network (DQ N) for traffic signal control. When training a RL model from scratch, we often need a lot of data and time to learn a better initialization. Our approach is dedicated to making the RL algorithm converge quickly and accelerating the pace of learning. Extensive experiments on three urban datasets confirm that our method performs better with faster convergence and least travel time than the current RL-based methods by an average of 23.9%, 23.8%, 11.6% © 2022 IEEE.

Emerging non-volatile memory (NVM) has been considered as a promising candidate for the next generation memory architecture because of its excellent characteristics. However, the endurance of NVM is much lower than DRAM. Without additional wear management technology, its lifetime can be very short, which extremely limits the use of NVM. This paper observes that the tail wear with a very small percentage of extreme deviation significantly hurts the lifetime of NVM, which the existing methods do not effectively solve. We present Lamina to address the tail wear issue, in order to improve the lifetime of NVM. Lamina consists of two parts: bounded tail wear leveling (BTWL) and lightweight wear enhancement (LWE). BTWL is used to make the wear degree of all pages close to the average value and control the upper limit of tail wear. LWE improves the accuracy of BTWL by exploiting the locality to interpolate low-frequency sampling schemes in virtual memory space. Our experiments show that compared with the state-of-the-art methods, Lamina can significantly improve the lifetime of NVM with low overhead. © 2022 IEEE.

Probing techniques are widely used to identify faulty nodes in networks. Existing probe-based solutions for SDN fault localizationcan focus on two ways: per-rule and per-path. Both promote some certain switches to reporters by installing on them report rules. To avoid hindering other test packets, such report rules must vary between tests or be deleted before a next test, thus incurring excessive consumption on either TCAM resources of switches or bandwidth reserved for control messages. In this paper we present Voyager, a hybrid fault localization solution for SDN that fully combines the advantages of per-rule and per-path tests. Voyager significantly reduces the number of report rules and allows them to reside and function in switches persistently. With only one well-designed report rule for each switch installed, Voyager pinpoints faulty switches easily and tightly by sending test packets straight. Tests in Voyager are parallelizable and report rules are non-invasive. The performance evaluation on realistic datasets shows that Voyager is 24.0% to 92.3% faster than existing solutions. © 2021 IEEE.

The introduction of a lightweight encryption scheme in the vehicular network has improved the reliability and security of data transmission substantially among vehicles. However, it is still not guaranteed that all vehicles comply with the encryption scheme throughout the whole operation period, considering the inherent selfishness of each participant. Apart from that, the data scheduling issue is another major concern in the vehicular network due to the high-speed mobility of vehicles. To tackle these issues, a multi-objective and multi-dimensional incentive mechanism is developed in this paper to achieve privacy-Aware data scheduling for vehicles. This mechanism is designed to encourage vehicles to carry out different tasks by providing relevant incentives while maximizing the overall utility of the network. Additionally, security and privacy of data transmission between vehicles and cloud servers are realized through a data perturbation approach. Experimental evaluation shows that the proposed incentive mechanism is better than the traditional methods when it comes to maximizing the number of participants and completed key tasks. © 2020 IEEE.

Non-Volatile Memory (NVM) technologies, such as Phase Change Memory (PCM), herald the next generation of main memory as they offer superior features compared with DRAM. Unfortunately, NVM's limited write endurance hinders its adoption as its lifetime can be extremely short under skew writes. This paper observes that the loops in programs are one of the primary causes of uneven writes as they introduce the hot data and cause a large number of stack frames to be allocated to the same locations. To alleviate this problem, we present Loop2Recursion, a compile-time wear leveling technique for transforming loops into recursions automatically. Our approach is flexible as it can avoid a substantial memory overhead by limiting the depth of recursion. Experimental results demonstrate that Loop2Recursion can significantly improve the wear leveling over stack area compared to the state-of-the-art methods, while incurring only negligible performance overhead.
© 2020 IEEE.

A group of connected and autonomous vehicles (CAVs) with common interests can drive in a cooperative manner, namely cooperative driving, which has been verified to significantly improve road safety, traffic efficiency and environmental sustainability. A more general scenario that various types of cooperative driving applications such as truck platooning and vehicle clustering, will coexist on roads in the foreseeable future. To support such multiple cooperative drivings, it is critical to design an efficient message dissemination scheduling in a shared communication channel. Most ongoing research suggests using the time-division multiple access (TDMA) method on top of IEEE 802.11p as a potential remedy. However, TDMA requires time synchronization and is not flexible, especially in the multiple cooperative drivings scenario where the beacon frequency needs to be updated and the number of cooperative drivings changes to meet the time-varying traffic conditions. In this paper, we focus on the study of the message dissemination protocol for platooning, a typical and well-known cooperative driving pattern. Specifically, we proposed a hybrid message dissemination protocol which aims at guaranteeing the reliable delivery of beacon messages for a multi-platooning system. We first adopt a TDMA-based medium access method for intra-platoon communication to improve the reliability and efficiency of beacon dissemination. We then present a token-passing medium access method for inter-platoon communication, which maps platoons into a token ring to schedule their beacon transmission time. We conduct extensive numerical experiments to validate the effectiveness of our protocol. © 2020 IEEE.