Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information.Generally,collected information is transmitted to a centralized resourceintensive cloud platform for service implementation.Edge Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users.Additionally,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time requests.Artificial Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically.Although extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite few.To address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV.Firstly,we establish the general condition and relative concepts about IoV,EC,and AI.Secondly,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading.Finally,we discuss a number of open issues in optimizing edge services with AI.

Video summarization with a graph convolutional attention network

Video summarization has established itself as a fundamental technique for generating compact and concise video, which alleviates managing and browsing large-scale video data. Existing methods fail to fully consider the local and global relations among frames of video, leading to a deteriorated summarization performance. To address the above problem, we propose a graph convolutional attention network(GCAN) for video summarization. GCAN consists of two parts, embedding learning and context fusion, where embedding learning includes the temporal branch and graph branch. In particular, GCAN uses dilated temporal convolution to model local cues and temporal self-attention to exploit global cues for video frames. It learns graph embedding via a multi-layer graph convolutional network to reveal the intrinsic structure of frame samples. The context fusion part combines the output streams from the temporal branch and graph branch to create the context-aware representation of frames, on which the importance scores are evaluated for selecting representative frames to generate video summary. Experiments are carried out on two benchmark databases, Sum Me and TVSum, showing that the proposed GCAN approach enjoys superior performance compared to several state-of-the-art alternatives in three evaluation settings.

cMonitor: VMI-Based Fine-Grained Monitoring Mechanism in Cloud

For the lack of detailed semantic in prior works, a transparent fine-grained monitoring technique （cMonitor） is pro- posed. Deployed outside the virtual machines, the cMonitor util- izes the elevated privileges of the virtual machine monitor to monitor the network connection, the processes and the relationship between them in protected systems by reconstructing fine-grained system semantics. These semantics contain process states and corresponding network connection. Experimental results show that cMonitor not only can be rapidly deployed in realistic cloud, but also can effectively and universally obtain these fine-grained semantics to assist detection of some advanced network attack. Meanwhile, the network performance overhead is about 3%, which is acceptable.

A Brief Review of Network Embedding

Learning the representations of nodes in a network can benefit various analysis tasks such as node classification, link prediction, clustering, and anomaly detection. Such a representation learning problem is referred to as network embedding, and it has attracted significant attention in recent years. In this article, we briefly review the existing network embedding methods by two taxonomies. The technical taxonomy focuses on the specific techniques used and divides the existing network embedding methods into two stages, i.e., context construction and objective design. The non-technical taxonomy focuses on the problem setting aspect and categorizes existing work based on whether to preserve special network properties, to consider special network types, or to incorporate additional inputs. Finally, we summarize the main findings based on the two taxonomies, analyze their usefulness,and discuss future directions in this area.