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

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

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

230901

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

WiCAR - Simulating towards the Wireless Car

DECSoS 2020 was held as part of the 39th International Conference on Computer Safety, Reliability and Security (SafeComp 2020).Part of the Lecture Notes in Computer Science book series (LNCS, volume 12235)Advanced driving assistance systems (ADAS) pose stringent requirements to a system’s control and communications, in terms of timeliness and reliability, hence, wireless communications have not been seriously considered a potential candidate for such deployments. However, recent developments in these technologies are supporting unprecedented levels of reliability and predictability. This can enable a new generation of ADAS systems with increased flexibility and the possibility of retrofitting older vehicles. However, to effectively test and validate these systems, there is a need for tools that can support the simulation of these complex communication infrastructures from the control and the networking perspective. This paper introduces a co-simulation framework that enables the simulation of an ADAS application scenario in these two fronts, analyzing the relationship between different vehicle dynamics and the delay required for the system to operate safely, exploring the performance limits of different wireless network configurations.info:eu-repo/semantics/publishedVersio

Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection

Autonomous UAV cruising is gaining attention dueto its flexible deployment in remote sensing, surveillance, andreconnaissance. A critical challenge in data collection with theautonomous UAV is the buffer overflows at the ground sensorsand packet loss due to lossy airborne channels. Trajectoryplanning of the UAV is vital to alleviate buffer overflows as wellas channel fading. In this work, we propose a Deep DeterministicPolicy Gradient based Cruise Control (DDPG-CC) to reducethe overall packet loss through online training of headings andcruise velocity of the UAV, as well as the selection of the groundsensors for data collection. Preliminary performance evaluationdemonstrates that DDPG-CC reduces the packet loss rate byunder 5% when sufficient training is provided to the UAV.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 the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project ARNET (POCI01-0145-FEDER-029074).info:eu-repo/semantics/publishedVersio

230402

The domain of Intelligent Transportation Systems (ITS) is becoming a key candidate to enable safer and efficient mobility in IoT enabled smart cities. Several recent research in cooperative autonomous systems are conducted over simulation frameworks as real experiments are still too costly. In this paper, we present a platooning robotic test-bed platform with a 1/10 scale robotic vehicles that functions based on the input front commercially off the shelf technologies (COTS) such as Lidars and cameras. We also present an in-depth analysis of the functionalities and architecture of the proposed system. We also compare the performance of the aforementioned sensors in some real-life emulated scenarios. From our results, we were able to concur that the camera based platooning is able to perform well at partially observable scenarios than its counterpart.This work was supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); by the European Regional Development Fund (ERDF) through the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement and the Portuguese Foundation for Science and Technology (FCT) and the Portuguese National Innovation Agency (ANI), under the CMU Portugal partnership, within project POCI-01- 0247-FEDER-045912 (FLOYD); by ERDF through the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, within project NORTE-01-0145-FEDER-000062 (RETINA); and by FCT and the EU ECSEL JU under the H2020 Framework Programme, within project ECSEL/0010/2019, JU grant nr. 876019 (ADACORSA). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Germany, Netherlands, Austria, France, Sweden, Cyprus, Greece, Lithuania, Portugal, Italy, Finland, Turkey. The ECSEL JU and the European Commission are not responsible for the content on this paper or any use that may be made of the information it contains.info:eu-repo/semantics/publishedVersio

Tightening Up Security In Low Power Deterministic Networks

The unprecedented pervasiveness of IoT systems is pushing this technology into increasingly stringent domains. Such application scenarios become even more challenging due to the demand for encompassing the interplay between safety and security. The IEEE 802.15.4 DSME MAC behavior aims at addressing such systems by providing additional deterministic, synchronous multi-channel access support. However, despite the several improvements over the previous versions of the protocol, the standard lacks a complete solution to secure communications. In this front, we propose the integration of TAKS, an hybrid cryptography scheme, over a standard DSME network. In this paper, we describe the system architecture for integrating TAKS into DSME with minimum impact to the standard, and we venture into analysing the overhead of having such security solution over application delay and throughput. After a performance analysis, we learn that it is possible to achieve a minor impact of 1% to 14% on top of the expected network delay, depending on the platform used, while still guaranteeing strong security support over the DSME network.info:eu-repo/semantics/publishedVersio