A DRL-based service offloading approach using DAG for edge computational orchestration.

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms

Biological Control of Fall Armyworm, Spodoptera frugiperda

The fall armyworm (FAW), Spodoptera frugiperda, is one of the most important invasive pests worldwide, resulting in considerable losses in host crops. FAW comprises two genetic strains, such as the “rice strain”, which prefers rice and other grass species, and the “maize strain”, which feeds upon maize and sorghum. Potential control measures are generally more applicable to the farmers who lack financial assets to buy chemical insecticides or costly pure seeds. The adverse effects of pesticides on the ecosystem and human’s health and the development of resistance to insect pests have exaggerated efforts to find an alternative strategy that is cost-effective, low-risk and target-specific. Therefore, biological control is widely considered as one of the most important options for insect pest management. This comprehensive review amasses the information on biological control in all phases of their development, including predators, parasitoids, entomopathogenic fungi, viruses, nematodes, bacteria, and biopesticides, with a special focus on their effectiveness against FAW. The findings regarding biological control are briefly discussed in light of improving management programs of the invasive pest S. frugiperda