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 a 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 equipment (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.

Smartphone magnetometer readings exhibit high linear correlation when two phones coexist within a short distance. Thus, the detected coexistence can serve as a proxy for close human contact events, and one can conceive using it as a possible automatic tool to modernize the contact tracing in infectious disease epidemics. This paper investigates how good a diagnostic test it would be, by evaluating the discriminative and predictive power of the smartphone magnetometer-based contact detection in multiple measures. Based on the sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, we find that the decision made by the smartphone magnetometer-based test can be accurate in telling contacts from no contacts. Furthermore, through the evaluation process, we determine the appropriate range of compared trace segment sizes and the correlation cutoff values that we should use in such diagnostic tests.

There exists a way that attackers can identify software defined networks (SDNs). Knowing the vulnerabilities of a SDN, the attackers can mount a saturation attack on the SDN controller with the aim of incapacitating the entire SDN. Therefore, the controller should have an architecture to weather out such an attack while continuing operation. A scheduling‐based architecture is proposed for the SDN controller that leads to effective attack confinement and network protection during denial of service (DoS) attacks.

Existing works in photovoltaic (PV) power generation focus on accurately predicting the PV power output on a forecast horizon. As the solar power generation is heavily influenced by meteorological conditions such as solar radiation, the weather forecast is a critical input in the prediction performance. However, the weather forecast is traditionally considered to have coarse granularity, so many are compelled to use on-site meteorological sensors to complement it. However, the approach involving on-site sensors has several issues. First, it incurs the cost in the installation, operation, and management of the sensors. Second, the physical model of the sensor dynamics itself can be a source of forecast errors. Third, it requires an accumulation of sensory data that represent all seasonal variations, which takes time to collect. In this paper, we take an alternative approach to use a relatively large deep neural network (DNN) instead of the on-site sensors to cope with the coarse-grained weather forecast. With historical PV output power data from our grid-connected building with a rooftop PV power generation facility and the publicly available weather forecast history data, we demonstrate that we can train a six-layer feedforward DNN for the day-ahead forecast. It achieves the average mean absolute error (MAE) of 2.9%, comparable to that of the conventional model, but without involing the on-site sensors.

In the autonomous sidelink resource scheduling for Cellular V2X (C-V2X), the standard sensing-based semi-persistent scheduling (SPS) algorithm relies on several mechanisms such as random resource selection, resource utilization pattern monitoring, and probabilistic reselection to minimize packet collisions and to cope with vehicular topology changes. Even so, the collision probability is still not negligible, and its performance is far from the projected requirements for low latency and high reliability applications for future C-V2X communication. In this paper, we propose a reservation mechanism that supplements the standard SPS for C-V2X Sidelink Mode 4, which visibly improves the performance in face of congestion and in the fringe of the communication range. In particular, we demonstrate that the reservation achieves the best performance if made much earlier than actual use of the reserved resource at least by an average of one second.

The IEEE 802.15.4 MAC (medium access control) is a protocol used in many applications including the wireless sensor network. Yet the IEEE 802.15.4 MAC layer cannot support different throughput performance for individual nodes with the current specifications. However, if certain nodes are sending data more frequently compared to others, with the standard MAC, it is hard to achieve network efficiency. Therefore, we modified the IEEE 802.15.4 MAC and additionally proposed a new state transition scheme. By adjusting the minBE value of some nodes to a smaller value and by dynamically changing the value depending on the transmission conditions, we shortened the backoff delay of nodes with frequent transmission. It was observed through our simulations that the throughput of the node with a lower minBE value increased significantly, compared to nodes with the original BE range of 3 to 5. Also by the use of the state transition scheme the total network efficiency increased leading to increase in throughput performance