The networking space has evolved dramatically over the last two years as organizations realize the increasing value of AIOps, the benefits of full network visibility, and the role secure access plays with remote workforces. Network analytics and monitoring procedures that once were considered standard have quickly becoming inadequate in today's rapidly changing IT network landscape. But fortunately, advanced networking analytics leveraging Artificial Intelligence (AI) and Machine Learning (ML) are helping to overcome new challenges when it comes to maintaining network performance and future-proofing NetOps teams. As a quick refresher, network analytics applies data analytic techniques to network data to monitor complete network behavior. With the addition of AI/ML technologies (and the rise of AIOps), deeper insights into application and network performance can be drawn on network data.

Today's deal will eliminate that hassle from your life. Amazon is selling the SharkNinja Shark IQ AV970 robot vacuum for $250. It's not clear when the sale price will end. This robovac features Wi-Fi, and the accompanying smartphone app allows you to set schedules or start a cleaning session. It also works with Amazon's Alexa and Google Assistant for activation via voice control.

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, ground- and sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results based on real satellite, flight, and shipping data in the North Atlantic region show that the integrated network enhances the coverage quality by reducing the end-to-end (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multi-objective routing algorithm is capable of achieving near Pareto-optimal performance.

Incentivized exploration in multi-armed bandits (MAB) has witnessed increasing interests and many progresses in recent years, where a principal offers bonuses to agents to do explorations on her behalf. However, almost all existing studies are confined to temporary myopic agents. In this work, we break this barrier and study incentivized exploration with multiple and long-term strategic agents, who have more complicated behaviors that often appear in real-world applications. An important observation of this work is that strategic agents' intrinsic needs of learning benefit (instead of harming) the principal's explorations by providing "free pulls". Moreover, it turns out that increasing the population of agents significantly lowers the principal's burden of incentivizing. The key and somewhat surprising insight revealed from our results is that when there are sufficiently many learning agents involved, the exploration process of the principal can be (almost) free. Our main results are built upon three novel components which may be of independent interest: (1) a simple yet provably effective incentive-provision strategy; (2) a carefully crafted best arm identification algorithm for rewards aggregated under unequal confidences; (3) a high-probability finite-time lower bound of UCB algorithms. Experimental results are provided to complement the theoretical analysis.

Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in the emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues.

Recruiters nowadays are checking your online presence before contacting you about an interview. They will look for your LinkedIn profile, GitHub, and Kaggle to figure out what value you will bring to their company. The hiring manager will also look for the latest blogs or projects you have worked on in the past to prepare interview questions so that they can test your intelligence (catherinescareercorner). Other than that, working on real-world projects will give you the required experience for the job, and with a few projects in your portfolio, you will make a good impression on the recruiter (data-flair). We will be learning new ways to crack your interviews and how creating a strong portfolio has helped me aced multiple interviews.

Zyter will be implementing its Smart Warehouse module running on the Zyter SmartSpaces IoT platform. The SmartSpaces platform collaborated with Qualcomm Technologies for the Qualcomm Smart Cities Accelerator Program ecosystem, and it integrates and consolidates data from IoT devices and applications. This is a seamless interface that helps break down data silos. The Smart Warehouse will also contain the private LTE CBRS (Citizens Broadband Radio Service) network cell sites that are powered by Qualcomm RAN Platforms for Small Cells.

And surprisinly, it has become more famous after the coronavirus spread since some people claimed that 5G caused the pandemic although there is no evidence. Leaving all the nonsense discussions aside, let's focus on the developments coming with 5G. The main advantage of 5G is using milimeter waves. These waves have higher frequencies compared to traditional generations, hence allow us to use wider bandwidths which also increase the data rates. It means that we will have much faster connections, more devices connected to Internet concurrently and less latency.

GlobalData research has found the top influencers in future infrastructure based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people and companies in future infrastructure on Twitter during Q2 2021. Guidaautonoma is a technologist who blogs about driverless, autonomous and self-driving cars on the online publishing platform Medium. He covers news and content on the potential of autonomous vehicles to disrupt the mobility sector. He believes that lack of infrastructure and legislation is impacting the transition of all forms of mobility to electric.