A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions

STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN

Dynamic routing in software-defined networking (SDN) can be viewed as a centralized decision-making problem. Most of the existing deep reinforcement learning (DRL) agents can address it, thanks to the deep neural network (DNN)incorporated. However, fully-connected feed-forward neural network (FFNN) is usually adopted, where spatial correlation and temporal variation of traffic flows are ignored. This drawback usually leads to significantly high computational complexity due to large number of training parameters. To overcome this problem, we propose a novel model-free framework for dynamic routing in SDN, which is referred to as spatio-temporal deterministic policy gradient (STDPG) agent. Both the actor and critic networks are based on identical DNN structure, where a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) with temporal attention mechanism, CNN-LSTM-TAM, is devised. By efficiently exploiting spatial and temporal features, CNNLSTM-TAM helps the STDPG agent learn better from the experience transitions. Furthermore, we employ the prioritized experience replay (PER) method to accelerate the convergence of model training. The experimental results show that STDPG can automatically adapt for current network environment and achieve robust convergence. Compared with a number state-ofthe-art DRL agents, STDPG achieves better routing solutions in terms of the average end-to-end delay.Comment: 6 pages,5 figures,accepted by IEEE ICC 202

Densely Knowledge-Aware Network for Multivariate Time Series Classification

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e.g., classification. Hence, a model’s representation learning ability is critical for enhancing its performance. This article proposes a densely knowledge-aware network (DKN) for MTSC. The DKN’s feature extractor consists of a residual multihead convolutional network (ResMulti) and a transformer-based network (Trans), called ResMulti-Trans. ResMulti has five residual multihead blocks for capturing the local patterns of data while Trans has three transformer blocks for extracting the global patterns of data. Besides, to enable dense mutual supervision between lower- and higher-level semantic information, this article adapts densely dual self-distillation (DDSD) for mining rich regularizations and relationships hidden in the data. Experimental results show that compared with 5 state-of-the-art self-distillation variants, the proposed DDSD obtains 13/4/13 in terms of “win”/“tie”/“lose” and gains the lowest-AVG_rank score. In particular, compared with pure ResMulti-Trans, DKN results in 20/1/9 regarding win/tie/lose. Last but not least, DKN overweighs 18 existing MTSC algorithms on 10 UEA2018 datasets and achieves the lowest-AVG_rank score

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called CapMatch. CapMatch gracefully hybridizes supervised learning and unsupervised learning to extract rich representations from input data. In unsupervised learning, CapMatch leverages the pseudolabeling, contrastive learning (CL), and feature-based KD techniques to construct similarity learning on lower and higher level semantic information extracted from two augmentation versions of the data“, weak” and “timecut”, to recognize the relationships among the obtained features of classes in the unlabeled data. CapMatch combines the outputs of the weak-and timecut-augmented models to form pseudolabeling and thus CL. Meanwhile, CapMatch uses the feature-based KD to transfer knowledge from the intermediate layers of the weak-augmented model to those of the timecut-augmented model. To effectively capture both local and global patterns of HAR data, we design a capsule transformer network consisting of four capsule-based transformer blocks and one routing layer. Experimental results show that compared with a number of state-of-the-art semi-supervised and supervised algorithms, the proposed CapMatch achieves decent performance on three commonly used HAR datasets, namely, HAPT, WISDM, and UCI_HAR. With only 10% of data labeled, CapMatch achieves F1 values of higher than 85.00% on these datasets, outperforming 14 semi-supervised algorithms. When the proportion of labeled data reaches 30%, CapMatch obtains F1 values of no lower than 88.00% on the datasets above, which is better than several classical supervised algorithms, e.g., decision tree and k -nearest neighbor (KNN)

MonLink: Piggyback Status Monitoring over LLDP in Software-Defined Energy Internet

While software-defined networking (SDN) has been widely applied in various networking domains including datacenters, WANs (Wide Area Networks), QoS (Quality of Service) provisioning, service function chaining, etc., it also has foreseeable applications in energy internet (EI), which envisions an intelligent energy industry on the basis of (information) internet. Global awareness provided by SDN is especially useful in system monitoring in EI to achieve optimal energy transportation, sharing, etc. Link layer discovery protocol (LLDP) plays a key role in global topology discovery in software-defined energy internet when SDN is applied. Nevertheless, EI-related status information (power loads, etc.) is not collected during the LLDP-based topology discovery process initiated by the SDN controller, which makes the optimal decision making (e.g., efficient energy transportation and sharing) difficult. This paper proposes MonLink, a piggyback status-monitoring scheme over LLDP in software-defined energy internet with SDN-equipped control plane and data plane. MonLink extends the original LLDP by introducing metric type/length/value (TLV) fields so as to collect status information and conduct status monitoring in a piggyback fashion over LLDP during topology discovery simultaneously without the introduction of any newly designed dedicated status monitoring protocol. Several operation modes are derived for MonLink, namely, periodic MonLink, which operates based on periodic timeouts, proactive MonLink, which operates based on explicit API invocations, and adaptive MonLink, which operates sensitively and self-adaptively to status changes. Various northbound APIs are also designed so that upper layer network applications can make full use of the status monitoring facility provided by MonLink. Experiment results indicate that MonLink is a lightweight protocol capable of efficient monitoring of topological and status information with very low traffic overhead, compared with other network monitoring schemes such as sFlow

Influence of O-related defects introduced by reduction on release behavior of hydrogen isotopes in Li2TiO3

In fusion reactors, Li2TiO3 is one of the most promising candidates among solid breeder materials. However, Li2TiO3 will be reduced after heating under a reducing atmosphere resulting in a deficiency of oxygen. In the present study, the O-related defects introduced by reduction and their effect on the release behavior of hydrogen isotopes in Li2TiO3 were investigated by means of Raman spectroscopy, electron spin resonance and thermal desorption spectroscopy. O-related defects were confirmed as E-centers by electron spin resonance. The concentration of defects increased as the exposing temperature increased and then decreased when the temperature increased higher above 750 degrees C. The color of Li2TiO3 samples changed from white to dark blue after heating under deuterium and recovered to white again after annealing in air. This color change suggested a change from TO + to Ti3+ due to a decrease in the oxygen content. Raman spectroscopy results indicated that there are no modifications in Li2TiO3 crystal structure, but on crystallinity. Thermal desorption spectroscopy showed the release behavior of deuterium has been affected considerably by O-related defects