13,654 research outputs found
Smart handoff technique for internet of vehicles communication using dynamic edge-backup node
© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/electronics9030524A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.Published onlin
TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization
Inter-process communication (IPC) is one of the core functions of modern
robotics middleware. We propose an efficient IPC technique called TZC (Towards
Zero-Copy). As a core component of TZC, we design a novel algorithm called
partial serialization. Our formulation can generate messages that can be
divided into two parts. During message transmission, one part is transmitted
through a socket and the other part uses shared memory. The part within shared
memory is never copied or serialized during its lifetime. We have integrated
TZC with ROS and ROS2 and find that TZC can be easily combined with current
open-source platforms. By using TZC, the overhead of IPC remains constant when
the message size grows. In particular, when the message size is 4MB (less than
the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens
of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2
IPC from hundreds of milliseconds to less than 1 millisecond. We also
demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in
autonomous driving scenarios. We show that by using TZC, the braking distance
can be shortened by 16% than ROS
An Intelligent model for supporting Edge Migration for Virtual Function Chains in Next Generation Internet of Things
The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations
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