Study and overview on WBAN under IEEE 802.15.6

WBAN (wireless body area networks) is an upcoming technology which stands to be a base for wearable and implantable sensors. The IEEE 802.15.6 formulates the physical and medium access for body area networks. The Body area networks can be implemented in several applications like health monitoring, ambient living environments and consumer electronics. This paper gives a clear overview about the functions of WBAN. The medium access layers and the physical layers of IEEE 802.15.6 are deeply examined and studied in this work. The access mechanisms of the protocol are explained in this paper. A clear literature review has also been stated to know the current state of art of this technology. The future possibilities and area to be explored also has been defined in this work

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In wireless powered sensor networks (WPSN), data of ground sensors can be collected or relayed by an unmanned aerial vehicle (UAV) while the battery of the ground sensor can be charged via wireless power transfer. A key challenge of resource allocation in UAV-aided WPSN is to prevent battery drainage and buffer overflow of the ground sensors in the presence of highly dynamic lossy airborne channels which can result in packet reception errors. Moreover, state and action spaces of the resource allocation problem are large, which is hardly explored online. To address the challenges, a new data-driven deep reinforcement learning framework, DDRL-RA, is proposed to train flight resource allocation online so that the data packet loss is minimized. Due to time-varying airborne channels, DDRL-RA firstly leverages long short-term memory (LSTM) with pre-collected offline datasets for channel randomness predictions. Then, Deep Deterministic Policy Gradient (DDPG) is studied to control the flight trajectory of the UAV, and schedule the ground sensor to transmit data and harvest energy. To evaluate the performance of DDRL-RA, a UAV-ground sensor testbed is built, where real-world datasets of channel gains are collected. DDRL-RA is implemented on Tensorflow, and numerical results show that DDRL-RA achieves 19\% lower packet loss than other learning-based frameworks.This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by national funds through the FCT, under CMU Portugal partnership, within project CMU/TIC/0022/2019 (CRUAV). This work was in part supported by the Federal Ministry of Education and Research (BMBF, Germany) as part of the 6G Research and Innovation Cluster 6G-RIC under Grant 16KISK020K.info:eu-repo/semantics/publishedVersio

DynaMO—Dynamic Multisuperframe Tuning for Adaptive IEEE 802.15.4e DSME Networks

Recent advancements in the IoT domain have been pushing for stronger demands of Qualityof-Service (QoS) and in particular for improved determinism for time-critical wireless communications under power constraints. The IEEE 802.15.4e standard protocol introduced several new MAC behaviors that provide enhanced time-critical and reliable communications. The Deterministic Synchronous Multichannel Extension (DSME) is one of its prominent MAC behaviors that combines contention-based and contentionfree communication, guaranteeing bounded delays and improved reliability and scalability by leveraging multi-channel access and CAP reduction. However, DSME has a multi-superframe structure, which is statically defined at the beginning of the network. As the network evolves dynamically by changing its traffic characteristics, these static settings can affect the overall throughput and increase the network delay because of improper allocation of bandwidth. In this paper, we address this problem, and we present a dynamic multi-superframe tuning technique that dynamically adapts the multi-superframe structure based on the size of the network. This technique improves the QoS by providing 15-30% increase in throughput and 15-35% decrease in delay when compared to static DSME networksinfo:eu-repo/semantics/publishedVersio

Routing Aware DSME Networks

Best oral communication Award (in ex aequo)3rd Doctoral Congress in Engineering will be held at FEUP on the 27th to 28th of June, 2019Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior of IEEE 802.15.4e can avail deterministic service using its multisuperframe structure. RPL is a routing protocol for wireless networks with low power consumption and generally susceptible to packet loss. A combination of these two protocols can integrate real-time QoS demanding and large-scale IoT networks. In this paper, we propose an architecture to integrate routing with DSME. We also show a simulation result by which we improve reliability by 40 % using routing.info:eu-repo/semantics/publishedVersio

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Worst-Case Bound Analysis for the Time-Critical MAC behaviors of IEEE 802.15.4e

13th IEEE International Workshop on Factory Communication Systems Communication in Automation (WFCS 2017). 31, May to 2, Jun, 2017, Main track. Trondheim, Norway.With an advancement towards the paradigm of Internet of Things (IoT), in which every device will be interconnected and communicating with each other, the field of wireless sensor networks has helped to resolve an ever-growing demand in meeting deadlines and reducing power consumption. Among several standards that provide support for IoT, the recently published IEEE 802.15.4e protocol is specifically designed to meet the QoS requirements of industrial applications. IEEE 802.15.4e provides five Medium-Access Control (MAC) behaviors, including three that target time-critical applications: Deterministic and Synchronous Multichannel Extension (DSME); Time Slotted Channel Hopping (TSCH) and Low Latency Deterministic Network (LLDN). However, the standard and the literature do not provide any worst-case bound analysis of these behaviors, thus it is not possible to effectively predict their timing performance in an application and accurately devise a network in accordance to such constraints. This paper fills this gap by contributing network models for the three time-critical MAC behaviors using Network Calculus. These models allow deriving the worst-case performance of the MAC behaviors in terms of delay and buffering requirements. We then complement these results by carrying out a thorough performance analysis of these MAC behaviors by observing the impact of different parameters.info:eu-repo/semantics/publishedVersio

Towards Worst-Case Bounds Analysis of the IEEE 802.15.4e

Work in Progress Session, 22nd IEEE Real-Time Embedded Technology & Applications Symposium (RTAS 2016). 11 to 14, Apr, 2016. Viena, Austria.The IEEE 802.15.4e amendment provides important functionalities to address timeliness and reliability in timesensitive WSN applications, by extending the IEEE 802.15.4-2011 protocol. Nevertheless, in other to make the appropriate network design choices, it is mandatory to understand the behavior of such networks under worst-case conditions. This paper contributes in that direction by proposing a methodology based on Network Calculus that will, by modeling the fundamental performance limits of such networks, enable in the future a quick and efficient worst-case dimensioning of the networks’ schedule and resources.info:eu-repo/semantics/publishedVersio

Work-In-Progress: Worst-Case Response Time of Intersection Management Protocols

Intersections are critical elements of urban traffic management and are identified as bottlenecks prone to traffic congestion and accidents. Intelligent intersection management plays a significant role in improving traffic efficiency and safety determining, among other metrics, the waiting time that vehicles incur when crossing an intersection. This work presents a preliminary analysis of the worst-case response time of intersection management protocols that handle mixed traffic with autonomous and human-driven vehicles. We deduce theoretical bounds for such time considered as the interval between the injection of a vehicle in the road system and its departure from the intersection, considering different intersection management protocols for mixed traffic, namely the Synchronous Intersection Management Protocol (SIMP) and several configurations of the conventional Round-Robin (RR) policy. Simulation results validate the analytical bounds partially. Ongoing work addresses thequeue dynamics and its reliable detection by traffic simulators.This work was supported by Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) through the Carnegie Mellon Portugal Program under Grant CMU/TIC/0022/2019 (CRUAV) and through the Research Unit UIDP/UIDB/04234/2020 (CISTER).info:eu-repo/semantics/publishedVersio

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Over the past decade, Unmanned Aerial Vehicles (UAVs) have provided pervasive, efficient, and cost-effective solutions for data collection and communications. Their excellent mobility, flexibility, and fast deployment enable UAVs to be extensively utilized in agriculture, medical, rescue missions, smart cities, and intelligent transportation systems. Machine learning (ML) has been increasingly demonstrating its capability of improving the automation and operation precision of UAVs and many UAV-assisted applications, such as communications, sensing, and data collection. The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasize the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before the full automation of UAVs and potential cooperation between UAVs and humans come to fruition.This work is supported by the CISTER Research Unit (UIDP/UIDB/04234/2020) and project ADANET (PTDC/EEICOM/3362/2021), financed by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology).info:eu-repo/semantics/publishedVersio