Reducing Message Collisions in Sensing-based Semi-Persistent Scheduling (SPS) by Using Reselection Lookaheads in Cellular V2X

In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent scheduling (SPS) implements a message collision avoidance algorithm to cope with the undesirable effects of wireless channel congestion. Still, the current standard mechanism produces high number of packet collisions, which may hinder the high-reliability communications required in future C-V2X applications such as autonomous driving. In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. Specifically, we propose the use of the "lookahead," which contains the next starting resource location in the time-frequency plane. By exchanging the lookahead information piggybacked on the periodic safety message, vehicular user equipments (UEs) can eliminate most message collisions arising from the ignorance of other UEs' internal decisions. Although the proposed scheme would require the inclusion of the lookahead in the control part of the packet, the benefit may outweigh the bandwidth cost, considering the stringent reliability requirement in future C-V2X applications.Comment: Submitted to MDPI Sensor

Congestion Control by Bandwidth-Delay Tradeoff in Very High-Speed Networks: The Case of Window-Based Control

Increasing bandwidth-delay product of high-speed wide-area networks is well-known to make conventional dynamic traffic control schemes sluggish . Still, most existing schemes employ dynamic control, among which TCP and ATM Forum\u27s rate-based flow control are prominent examples. So far, little has been investigated as to how the existing schemes will scale as bandwidth further increases up to gigabit speed and beyond. Our investigation in this paper is the first to show that dynamic control has a severe scalability problem with bandwidth increase, and to propose an entirely new approach to traffic control that overcomes the scalability problem. The essence of our approach is in exercising control in bandwidth domain rather than time domain, in order to avoid time delay in control. This requires more bandwidth than the timed counterpart, but achieves a much faster control. Furthermore, the bandwidth requirement is not excessively large because the bandwidth for smaller control delay and we call our approach Bandwidth-Latency Tradeoff (BLT). While the control in existing schemes are bound to delay, BLT is bound to bandwidth. As a fallout, BLT scales tied to bandwidth increase, rather than increasingly deteriorate as conventional schemes. Surprisingly, our approach begins to pay off much earlier than expected, even from a point where bandwidth-delay product is not so large. For instance, in a roughly AURORA-sized network, BLT far outperforms TCP on a shared 150Mbps link, where the bandwidth-delay product is around 60KB. In the other extreme where bandwidth-delay product is large, BLT outperforms TCP by as much as twenty times in terms of network power in a gigabit nationwide network. More importantly, BLT is designed to continue to scale with bandwidth increase and the performance gap is expected to widen further

FDF: Frequency Detection-Based Filtering of Scanning Worms

Abstract — In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms from a monitored network. Its low complexity allows it to be used on any network-based intrusion detection system as a real time detection module for high-speed networks. Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate. I

Vehicle-to-Vehicle (V2V) Message Content Plausibility Check for Platoons through Low-Power Beaconing

Although the IEEE Wireless Access in Vehicular Environment (WAVE) and 3GPP Cellular V2X deployments are imminent, their standards do not yet cover an important security aspect; the message content plausibility check. In safety-critical driving situations, vehicles cannot blindly trust the content of received safety messages, because an attacker may have forged false values in it in order to cause unsafe response from the receiving vehicles. In particular, the attacks mounted from remote, well-hidden positions around roads are considered the most apparent danger. So far, there have been three approaches to validating V2X message content: checking based on sensor fusion, behavior analysis, and communication constraints. This paper discusses the three existing approaches. In addition, it discusses a communication-based checking scheme that supplements the existing approaches. It uses low-power transmission of vehicle identifiers to identify remote attackers. We demonstrate its potential address in the case of an autonomous vehicle platooning application

An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals

The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI

Reducing Energy Consumption and Health Hazards of Electric Liquid Mosquito Repellents through TinyML

Two problems arise when using commercially available electric liquid mosquito repellents. First, prallethrine, the main component of the liquid repellent, can have an adverse effect on the human body with extended exposure. Second, electricity is wasted when no mosquitoes are present. To solve these problems, a TinyML-oriented mosquito sound classification model is developed and integrated with a commercial electric liquid repellent device. Based on a convolutional neural network (CNN), the classification model can control the prallethrine vaporizer to turn on only when there are mosquitoes. As a consequence, the repellent user can avoid inhaling unnecessarily large amounts of the chemical, with the added benefit of dramatically reduced energy consumption by the repellent device