U.S. military commanders covered up an air strike over Syria that killed several dozen civilians, dishonestly portraying it as a successful attack against ISIS fighters and ignoring firm recommendations--filed by military lawyers--to investigate the strike as a war crime. The attack and subsequent cover-up--revealed in a long, extensively documented story in this weekend's New York Times--took place in 2019, during the final phase of the U.S. and allied campaign to oust the Islamic State from its self-declared caliphate in Syria. The Times report comes a few months after the final U.S. drone strike in Afghanistan in August, which Pentagon officials touted as halting a terrorist attack--but which in fact, as another Times investigation soon revealed, killed 10 civilians, none of whom had any connection to terrorists. Together, the two reports raise questions about the moral and strategic wisdom of launching airstrikes in areas where civilians and fighters routinely mix. These questions have been raised many times in the course of America's 20-year "global war on terror."

Zephyr, a solar-powered unmanned aerial vehicle (UAV) built by Airbus, was used to deliver next generation wireless internet, as part of a test flight over Arizona. Airbus was testing the'High Altitude Platform Station' (HAPS), onboard the British-built UAV, as part of an 18-day flight in the stratosphere, 76,100ft above the surface. The test was in partnership with Japanese mobile operator, NTT DOCOMO, and could one day lead to super-fast broadband in remote areas, without the need to send a fleet of satellites into low Earth orbit, according to Airbus. It carried an onboard radio transmitter that let it provide a datalink to simulate future systems that would send internet signals between the UAV and a computer. The successful test could pave the way for a fleet of Zephyr aircraft delivering 5G and 6G mobile internet to the most remote parts of the planet, or providing a short-term signal boost during a major event in a densely populated area, Airbus says.

Italian and European authorities weren’t aware that a Chinese state-controlled company bought an Italian military-drone manufacturer and transferred its know-how and technology, revealing how Beijing is skirting weak investment-screening in Europe to acquire sensitive technology.

When it comes to national and international defense, drone warfare has placed itself firmly as one of the prime options these days. To understand the challenge at hand, let's first take a step back, and look at defense as a whole in general. The development of technologies such as artificial intelligence and advanced computing have made defense only more complicated. These complications have made military divisions more potent. But all this progress comes with a catch: this has largely evened-up the playing field as far as lower-mid-tier weaponry is concerned.

Drones continue to grow in popularity. UAV's, in fact, are being put to work in professional settings all over the country. If you aren't already using a drone, maybe it's time to consider getting one -- such as the Ninja Dragon Alpha Z PRO, which is on sale for an extra 15% off during our Pre-Black Friday Sale. The Alpha Z PRO is easy to fly, offers a 260'-300' range, and captures crystal clear 4K video and stills. That makes it an ideal first drone for most business applications.

Autonomous surveillance unmanned aerial vehicles (UAVs) are deployed to observe the streets of the city for any suspicious activities. This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets. An extensive database is collected from UAV surveillance drones. With the aid of artificial intelligence (AI), fire stations can swiftly identify the presence of a fire emerging in the neighborhood. Spatio-temporal split learning is applied to this scenario to preserve privacy and globally train a fire classification model. Fires are hazardous natural disasters that can spread very quickly. Swift identification of fire is required to deploy firefighters to the scene. In order to do this, strong communication between the UAV and the central server where the deep learning process occurs is required. Improving communication resilience is integral to enhancing a safe experience on the roads. Therefore, this paper explores the adequate number of clients and data ratios for split learning in this UAV setting, as well as the required network infrastructure.

The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs. To achieve the promising benefits, the crucial I-UAV networking issue should be tackled. This article argues that I-UAV networking can be classified into three categories, quality-of-service (QoS) driven networking, quality-of-experience (QoE) driven networking, and situation aware networking. Each category of networking poses emerging challenges which have severe effects on the safe and efficient accomplishment of I-UAV missions. This article elaborately analyzes these challenges and expounds on the corresponding intelligent approaches to tackle the I-UAV networking issue. Besides, considering the uplifting effect of extending the scalability of I-UAV networks through cooperating with high altitude platforms (HAPs), this article gives an overview of the integrated HAP and I-UAV networks and presents the corresponding networking challenges and intelligent approaches.

Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM). There are many techniques for real-time robust drone guidance, but many of them solve in discretized airspace and control, which would require an additional path smoothing step to provide flexible commands for UAS. To provide a safe and efficient computational guidance of operations for unmanned aircraft, we explore the use of a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to guide autonomous UAS to their destinations while avoiding obstacles through continuous control. The proposed scenario state representation and reward function can map the continuous state space to continuous control for both heading angle and speed. To verify the performance of the proposed learning framework, we conducted numerical experiments with static and moving obstacles. Uncertainties associated with the environments and safety operation bounds are investigated in detail. Results show that the proposed model can provide accurate and robust guidance and resolve conflict with a success rate of over 99%.

Apple is rumored to be developing several technologies outside of smartphones and tablets, such as a VR headset and a car, but new patents awarded to the tech giant on Thursday suggest it may be working on a drone. Approximately two patents describe small unmanned aerial vehicles (UAVs) that pair with wireless controllers or drones operated via an iPhone or a Nintendo DS. Apple, however, initially filed the patents in Singapore'to keep the projects a secret,' but have since filed the pair with the US Patent & Trademark Office. The images in the patents depict a small drone with four rotors, a common designed for small UAVs. Approximately two patents describe small unmanned aerial vehicles (UAVs) that pair with wireless controllers or drones operated via an iPhone or a Nintendo DS.