Shannon Wireless’ Post

This paper proposes a security-aware computation offloading framework tailored for #mobile #edge #computing (#MEC)-enabled #Internet #of #Things (#IoT) networks operating in environments with #aerial #eavesdroppers (#AEs) and #ground #eavesdroppers (#GEs). It is envisaged that multiple #ground #nodes (#GNs) should perform computation tasks partly locally and partly remotely by offloading a portion of these tasks to MEC servers. To facilitate this paradigm, an #unmanned #aerial #vehicle (#UAV) is deployed, serving as both an aerial MEC server and a relay for forwarding part of the tasks to a ground #access #point (#AP) for computing. The computation offloading is further reinforced by incorporating a #reconfigurable #intelligent #surface (#RIS) unit in close proximity to the AP. Within this context, this paper provides an analysis of the #secrecy #outage #probability (#SOP) and formulates an optimization problem aimed at maximizing the minimum #secure #computation #efficiency (#SCE) by jointly optimizing transmit power allocation, time slot scheduling, task allocation, and RIS’s phase shifts. Given the non-convex nature of the problem, an iterative algorithm is introduced to address the fractional objective function and coupled optimization variables by employing Dinkelbach- and #block #coordinate #descent (#BCD)-based methods, respectively. ---- Emmanouel Michailidis, PhD, SMIEEE, Maria-Garyfallio Volakaki, Nikolaos I. Miridakis, Demosthenes Vouyioukas More details can be found at this link: https://lnkd.in/eucbkwxR

In this paper, the authors propose a secure #short-#packet #communication (#SPC) system, where a #unmanned #aerial #vehicle (#UAV) serves as a mobile #decode-and-#forward (#DF) relay, periodically receiving and relaying small data packets from a remote IoT device to its receiver in two hops with strict latency requirements, in the presence of an eavesdropper. This system requires careful optimization of important design parameters, such as the coding blocklengths of both hops, transmit powers, and the UAV's trajectory. While the overall optimization problem is nonconvex, the authors tackle it by applying a #block #successive #convex #approximation (#BSCA) approach to divide the original problem into three subproblems and solve them separately. ---- Milad Tatar Mamaghani, PhD, Xiangyun (Sean) Zhou, Nan Yang, A. Lee Swindlehurst More details can be found at this link: https://lnkd.in/ddeZFn5v

This paper presents a novel framework for #joint #field #data #collection and #wireless #charging in an #unmanned #aerial #vehicle (#UAV)-aided wireless sensor network via #monostatic #backscatter #communication at #millimeter #waves. The framework is divided into three tasks, namely, #energy-#optimized #UAV #transceiver #design, UAV constraints aware #backscatter #nodes (#BSNs) clustering, and optimized resource allocation per cluster. To strike a balance between serving efficiency and self-interference, the optimum BSN cluster size is estimated offline, which in turn governs BSN clustering optimization. With UAV communication energy and clustering information, a #joint #sum #energy #transfer and #sum #data #collection #maximization #problem is formulated by considering the minimum required charging and data collection constraints.----Amit Goel, Nancy Varshney, Swades De More details can be found at this link: https://lnkd.in/gWr8J_GG

This paper tackles the energy concern of #Internet #of #Things (#IoT) devices by utilizing #Unmanned #Aerial #Vehicles (#UAVs) as data collectors and energy transmitters and by promptly charging IoT devices whenever necessary. As for the energy concern of UAVs, the authors deploy a set of #Unmanned #Ground #Vehicles (#UGVs) to energy supply UAVs, allowing them to complete their tasks successfully. Their objective is to employ a #multi-#agent #reinforcement #learning method for optimally controlling the trajectories of both UGVs and UAVs so that it jointly decreases their energy consumption, reduces the #Age #of #Information (#AoI) of IoT devices, and timely charges UAVs and avoids their failures.----Kaddour MESSAOUDI, @Abdullah Baz, Omar Sami Oubbati, Abderrezak Rachedi, Mohamed tahar Bendouma, Mohammed Atiquzzaman More details can be found at this link: https://lnkd.in/g4meWGjv

🚀 We are pleased to announce the publication of our research paper titled "DroneSSL: Self-Supervised Multimodal Anomaly Detection in Internet of Drone Things" in IEEE Transactions on Consumer Electronics. 🔍 This paper introduces DroneSSL, a novel framework that merges spatial crowdsourcing with TinyML to enhance anomaly detection capabilities within the Internet of Drone Things (IoDT). The framework utilizes drones and unmanned ground vehicles (UGVs) for extensive data collection in environments that are typically inaccessible or hazardous, such as during Australian bushfire incidents. By leveraging lightweight machine learning models and advanced communication technologies, DroneSSL offers significant improvements over traditional data analysis methods, processing multimodal data from diverse Points-of-Interest (PoIs) more effectively. 🌐 This study not only demonstrates the potential of integrating TinyML with spatial crowdsourcing within the IoDT but also sets a new benchmark for efficient, scalable anomaly detection technologies. These advancements pave the way for future innovations in IoT edge devices and environmental monitoring systems. 🔗 The paper is now available for reading on IEEE Xplore: https://lnkd.in/gM8q9Mrj 👏 We extend our gratitude to my co-authors for their invaluable contributions to this research. #IoDT #MachineLearning #TinyML #IEEE #DroneTechnology #SpatialCrowdsourcing #AnomalyDetection #EnvironmentalSurveillance #BushfireManagement #AcademicPublishing

This paper considers a multi-#unmanned #aerial #vehicle (#UAV) assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide #mobile #edge #computing (#MEC) services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic #geometric #waterfilling (#GWF) technique, and the UAV trajectory by #successive# convex #approximation (#SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. ---- Farhan Pervez, Ajmery Sultana, Cungang Yang, Lian Zhao More details can be found at this link: https://lnkd.in/eepwzgmA

Integrating sensing and communication capabilities (ISAC) in mobile networks An exciting part of the evolution of mobile networks is the opportunity to support both sensing and communication capabilities in the future. A network acting as a sensor is unique as it can perform spatial location of unconnected objects and objects connected to other networks with the extensive reach of a mobile network. 3GPP has studied 32 potential use cases where ISAC can add new types of sensors and give access to data never seen before. Enjoy reading this blog post that outlines what this innovation can do in America. #5G #6G #ISAC #JCAS #NetworkAsASensor Yossi Cohen | Erik Ekudden | Marie Hogan | Patrik Persson | Robert Baldemair | Christina Chaccour

Integrated Sensing and Communication (ISAC) represents a transformative approach within 5G and beyond, aiming to merge wireless communication and sensing functionalities into a unified network infrastructure. By repurposing communication signals for sensing, this integration offers enhanced spectrum efficiency, real-time situational awareness, cost and energy reductions, and improved operational performance. We have developed I-SCOUT, an innovative ISAC solution designed to uncover moving targets in NextG networks. I-SCOUT can accurately estimate both the range and velocity of these targets (e.g., drones), and distinguish between multiple targets such as in swarm environments. Joint work of Utku Demir, PhD, Kemal Davaslioglu, Yalin Sagduyu, Tugba Erpek, Gustave Anderson, and Sastry Kompella, Ph.D. was accepted to appear at the IEEE MILCOM’24 conference. A preprint is available at: https://lnkd.in/ekmSNunh Nexcepta, Inc. looks forward to discussing more on the capabilities of I-SCOUT and future directions at MILCOM. #5G #6G #nextg #isac #sensing #communications

I am pleased to share my recent publication, "Wireless Mobile Distributed-MIMO for 6G," which introduces a novel Distributed MIMO (D-MIMO) architecture for next-generation communication systems. This work proposes an innovative approach where wireless mobile nodes, such as user equipment (UEs), unmanned aerial vehicles (UAVs), and vehicular UEs, actively participate in joint D-MIMO transmission with the base station (BS). These nodes, referred to as D-MIMO nodes, establish high-SNR links with the BS and edge-located UEs, contributing to improved connectivity and system performance. Key Contributions: - Collaborative D-MIMO Architecture: D-MIMO nodes operate as both users and cooperative transmitters within the network. - Two-Phase Operation: The BS first forwards data to D-MIMO nodes, which then collaborate with the BS to jointly serve UEs using coherent D-MIMO techniques. - Capacity Enhancement: Using realistic 3GPP channel models, the proposed architecture demonstrates significant capacity improvements over baseline direct BS-to-UE communication. This work highlights the potential of cooperative communication in advancing the efficiency and adaptability of 6G networks. I extend my gratitude to my advisors and collaborators, Daniel Jakubisin, Karim Said, Mike Buehrer, Lingjia Liu for their guidance and support. For more details, you can access the full paper here: [https://lnkd.in/ehCejsaU]. I welcome discussions and collaboration opportunities to further explore the future of wireless communication. #6G #DistributedMIMO #WirelessResearch #Innovation #FutureConnectivity

Octa's sensor-agnostic algorithms allow it to accept data from a range of sources simultaneously, including both active and passive detectors. This article gives a great overview of these two types of detection systems and the differences between them. Check out the article here: https://lnkd.in/eiarrwMQ Find out more about Octa here: https://lnkd.in/ewGx6K3H #sensors #innovation #objectdetection #CUAS #Radar #Octa