In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts data. These computing tasks sometimes far exceed capability SDs and require lots energy time process. How effectively reduce consumption latency is necessary be solved. To this end, we first propose a software-defined network (SDN)-based edge (MEC) system. MEC system, can offload computation servers decrease processing avoid waste energy. At same time, taking advantage SDN's...

Industrial Internet of Things (IIoT), which has the capability perception, monitoring, communication and decision–making, already exposed more security problems that are easy to be invaded by malware because many simple edge devices help smart factories, cities homes. In this paper, a two–layer spread–patch model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IIPV</i> is proposed based on hybrid patches distribution method according...

Opportunistic computation offloading is an effective way to improve the computing performance of Industrial Internet Things (IIoT) devices. However, as more and tasks are being offloaded mobile-edge (MEC) servers for processing, it can lead IIoT privacy security issues, such personal usage habits. In this paper, we aim design a Lyapunov-based privacy-aware framework that defines amount user designs "reduced privacy" mechanism. We first define cumulative each trigger protection mechanism when...

Hyperspectral data usually consists of hundreds narrow spectral bands and provides more detailed characteristics compared to commonly used multispectral in remote sensing applications. However, highly correlated hyperspectral lead computational complexity, which limits many applications or traditional methods when applied data. The dimensionality reduction becomes one the most important pre-processing steps analysis. Recently, deep reinforcement learning (DRL) has been introduced band...

Industrial Internet of Things (IIoT) has brought a lot convenience for the industrial world to digitization, automation and intelligence, but it inevitably introduces inherent cyber security risks, resulting in an issue that traditional intrusion detection techniques are no longer sufficient IIoT environments. To solve this issue, we propose open-set solution called DC-IDS based on deep reinforcement learning. In solution, recognition problem is modeled as discrete-time Markov decision...

The Industrial Internet of Things (IIoT) has experienced rapid growth in recent years, with an increasing number interconnected devices, thereby expanding the attack surface. Effectively detecting intrusions is crucial for safeguarding IIoT systems from malicious attacks. However, due to dynamic and complex nature environment, designing intrusion detection strategy that balances accuracy efficiency remains a significant challenge. In this paper, we propose novel based on stochastic games...

Mobile edge computing (MEC) provides a new development direction for emerging computing-intensive applications because it can improve performance and lower the threshold users to use such applications. However, designing an effective computation offloading strategy determine which tasks should be uninstalled server is still crucial challenge. To this end, we propose offload scheme based on dynamic resource allocation optimize energy consumption in MEC systems. We further formulate as...

In recent years, the type and quantity of news are growing rapidly, it is not easy for users to find they interested in massive amount news. A recommendation system can score predict candidate news, finally recommend with high scores users. However, existing user models usually only consider users’ long-term interests ignore interests, which affects usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network capture short-term combines characterize While...

The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement computation performance including energy consumption and delay by offloading. To this end, we propose a deep reinforcement learning (DRL)-based offloading scheme to optimize jointly protection performance. exposure risk caused history is investigated, analysis metric defined evaluate level. find optimal strategy, algorithm combining actor-critic, off-policy,...

A great deal of Web table information exists in cooperative learning activities. The paper presents a new method that extracts from tables documents. Using tabled abstract semantic model to describe complicated and understand the point view semantics, reduces dependence for design difference constructions extraction process. At same time, it utilizes characteristics HTML techniques natural language processing some heuristic rules, thus aids identification items. On above basis, we prototype,...

To evaluate the steady-state availability of heterogeneous edge computing-enabled wireless sensor networks (HECWSNs) with malware infections, we first propose a Stackelberg attack-defence game to predict optimal strategies and intrusion detection systems (IDSs) deployed in nodes (HSNs). Next, present new infection model—heterogeneous susceptible-threatened-active-recovered-dead (HSTARD) based on epidemic theory. Then, considering heterogeneity sink common attack correlation, derive state...