Spectrum Access Systems (SASs) and similar systems coordinate access to shared radio frequency bands to efficiently allocate the use of spectrum between users in a locality. To fill the need for dense spectrum occupancy information, SASs will utilize crowdsourced data from nodes outside the SAS’s control. This crowdsourcing of data, however, makes the SAS vulnerable to many types of attacks. The attacks covered in this paper include copying and manipulating existing data to create a Spectrum Sensing Data Falsification (SSDF) attack. We propose methods to identify two categories of easily implemented SSDF attacks and show the proposed methods to be both effective and efficient. Further, we recommend that the proposed techniques be used in conjunction with other SSDF thwarting methods that use statistics, probability, or machine learning, and can identify a wider range of SSDF attacks, albeit more slowly and less reliably than the proposed methods can identify the specific types of SSDF attacks for which they are effective. Our findings demonstrate the feasibility of discerning diverse forms of manipulated data while maintaining pace with the influx of incoming data. The ability to identify manipulated data rapidly without imposing undue strain on a centrally aggregated system helps reduce the number of ways to create a potentially successful SSDF attack and increases the accuracy of determining the radio transmission activity of a Primary User (PU). Several methods are explored and evaluated for identifying copied or manipulated spectrum data. We recommend utilizing an exact match identification algorithm with Elasticsearch to search for exact copies of spectrum data. Additionally, we recommend utilizing a cosine similarity function with Elasticsearch to search for manipulated spectrum data and exact copies when sufficient computational resources are available.
Telecommunication, Transportation and communications
Emerson Philipe Sinesio, Marcele Elisa Fontana, Júlio César Ferro de Guimarães
et al.
<i>Background:</i> Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. <i>Methods:</i> Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. <i>Results:</i> The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. <i>Conclusions:</i> These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance.
Transportation and communication, Management. Industrial management
Supply chain networks have proven to be rather important and delicate to seemingly small perturbations of their operations. Although scheduling has been extensively studied in logistics systems, there are several remaining open challenges regarding scalability, stability, and other quality indices. In this work, we present a novel framework denoted as BackPressure-style Packet transfer algorithm for Logistics Systems (BPLS) for making jointly optimal routing and scheduling decisions in freight networks. The proposed approach is based on the backpressure algorithm and maximum weight matching, which have been extensively applied for optimal routing and scheduling of data packet transmissions in communications networks. Our goal is to develop a broader optimal transfer process for freight networks consisting of multiple entities, such as last-mile companies, freight subcontractors, etc., addressing all the previously mentioned challenges and in addition, allow for setting different optimization goals regarding the offered quality of service. Special features of freight networks such as the limited capacities of both storage places and transportation means along with the time-varying availability of the latter are considered by means of integrating pressure functions in the original backpressure approach. We provide extensive simulations with evaluation and comparison results on the performance of the approach, demonstrating its potential to improve up to more than <inline-formula> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> on the traditional BP algorithm. In addition, we have incorporated an implementation in an operational information system to assess the potentials of BPLS with multiple interested stakeholders. Through simulations and the actual evaluation, we were able to show how the framework can be used to provide long and short term decisions for optimizing holistically freight networks.
Our understanding of non-linear and time varying effects on shared e-scooter ridership dynamics is limited. Consequently, both operators and city councils supporting shared e-scooter schemes do not have the requisite information to help optimise infrastructure planning and operation management. Focussing on subtropical Brisbane, Australia, the current study examines time varying and non-linear effects of weather and built environment factors on shared e-scooter ridership. Results from XGBoost models reveal threshold relationships with both the availability of cycling infrastructure and the presence of park and commercial land uses. Additionally, we show how hot weather increases ridership especially around large parks and in commercial areas on both weekdays and weekends. Understanding the intricate (non-linear) interplay (interaction) between weather and built environment factors and their variation over time on shared e-scooter ridership have important implications for policymakers, transportation planners, and environmental advocates in providing the requisite evidence for data driven decision making.
Abstract Dockless e-scooter schemes have seen increasing popularity in 28 German cities. Increasing use on insufficiently dimensioned bicycle infrastructure can lead to conflicts between e-scooter riders and cyclists. A new approach was developed in order to detect potential zones of conflict by overlaying aggregated bicycle and e-scooter trajectories in the City of Dresden, Germany. Bicycle data is being obtained by the annual STADTRADELN campaign where cyclists record and transmit daily trips via GPS for a period of three weeks. Simultaneously, e-scooter API data has been collected over a course of 8 weeks from June to September 2021. Origin/Destination data has been generated and routed over a OSM network in order to obtain aggregate d e-scooter flows. We extrapolated the aggregated bicycle data to match them with the timeframe of the e-scooter data acquisition. Afterwards we spatially joined both: bicycle and e-scooter flows and calculated the link wise proportion of e-scooter trips in relation to bicycle trip volumes. Two important findings emerged: (1) Residential roads have a higher proportion of e-scooter trips. (2) E-scooters are exposed to high bicycle trip volumes on primary roads with bicycle infrastructure. We conclude that this approach can detect possible links of conflict, where overtaking cyclists or insufficient space can lead to dangerous situations. That approach is biased towards a missing route choice model for e-scooter riders or better route data of e-scooters, which needs further research.
Transportation engineering, Transportation and communications
The development of the Internet of vehicles technology can improve the communication between vehicles, thereby changing the driving behavior of drivers. Therefore, the traditional safe-following model cannot accurately describe the driving behavior and needs to be improved accordingly. First, two key parameters (i.e., drivers’ reaction sensitivity and road friction coefficient) are obtained through a comprehensive comparative analysis of influencing factors on the Internet of vehicles environment. And the calculation methods of these two parameters are proposed by using the multilevel comprehensive weighted evaluation method and the BP neural network. Then, these two key parameters are used to improve the traditional minimum safety distance model for adapting to driving behavior under the Internet of vehicles environment. Finally, through setting up simulation experiments and comparative analysis, the relationship between different influencing factors and the minimum safe following distance is obtained, and the influence degree of different influencing factors is sorted. The most important factor affecting car-following safety is the drivers’ characteristics. It can provide strong theoretical support for the safe driving assistance system of vehicles.
Transportation engineering, Transportation and communications
Shortening the packet length has been a consensus in wireless network design for supporting the ultra-low latency Internet of Things (IoT) applications. Yet, with short-packet transmission, the rate loss would occur, which further depends on the blocklength, making the network optimization notoriously difficult, especially for random access networks. This paper focuses on the representative random access network, i.e., Aloha, with short packet transmission, namely, short-packet Aloha. Specifically, we aim to optimize the sum rate and access delay of short-packet Aloha. By deriving the probability of successful transmissions of packets, both the network sum rate and the probability generating function of access delay are obtained as explicit functions of key system parameters. The maximum sum rate and the minimum mean access delay are further derived by jointly tuning the packet transmission probability and the blocklength of packets. The effect of system parameters on the optimal sum rate and access delay performance is investigated. It is shown that the maximum sum rate is insensitive to the retry limit <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>, while deteriorates as the information bits per packet <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> decreases. In contrast, the optimal delay performance can be improved with a small <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. The reliability performance is also evaluated and shown to be enhanced with a large retry limit <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. The analysis sheds important light on the access design of practical short-packet Aloha networks. By taking LTE-M as an example, it is found that to improve access delay performance, the information bits per packet <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> should not exceed an upperbound, which polynomially decreases as the network size increases.
Telecommunication, Transportation and communications
With the development of autonomous driving and communication technology, the heterogeneous traffic flow by combining connected and autonomous vehicles (CAVs) and manually driven vehicles (MVs) will appear on the freeway in the near future. It is expected that CAVs can improve the freeway capacity and reduce vehicle exhaust emissions, but sharing the same road by CAVs and MVs will cause certain interference to CAVs. In order to reduce the negative influence of the heterogeneous traffic flow, setting up CAV dedicated lanes to separate CAVs from MVs to a certain extent is regarded as a reasonable solution. Based on the characteristics that MVs should be decelerated by a realistic amplitude and that the connected and autonomous vehicle can accurately predict the speed of its preceding and rear CAVs at the next time step, a heterogeneous traffic flow model was established. Based on this model, we studied the overall influence of different lane strategies on the operating efficiency of freeway traffic flow and vehicle exhaust emissions under different densities with different CAV penetration rates. The results show that setting up CAV dedicated lanes with low CAV penetration rates will have a negative impact on the freeway traffic flow. When the CAV penetration rate is 40%–60% and the density is not less than 30 veh/km/lane, setting up one CAV dedicated lane is the best choice. When the CAV penetration rate exceeds 60% and the density is not less than 40 veh/km/lane, setting up two CAV dedicated lanes is the best choice. The research finding will assist in understanding the overall influence of CAV dedicated lanes on freeway traffic flow and help determine the optimal number of CAV dedicated lanes under different traffic conditions.
Transportation engineering, Transportation and communications
Electric vehicles (EVs) are becoming the potential contender for the conventional gasoline vehicles in view of the environment-friendly and energy-efficient characteristics. The prediction of EV charging-related states (defined in this study as home charge, outside charge, home stop, outside stop, low-battery travel, and high-battery travel) could help to identify the future charging demand (power consumption) of EV individuals. Specifically, it could guide the operation and management of charging facilities and also provide tailored charger availability information based on users’ real-time locations. This study aims to predict charging-related states of individual EVs using a deep learning approach. We first propose a tangible approach to convert EV trajectory data into state sequences and then develop a bidirectional gated recurrent unit model with attention mechanism (Bi-GRU-Attention) to forecast EV states. A sensitivity analysis is conducted to tune and/or calibrate parameters in the model based on plug-in hybrid EV trajectories dataset collected in Shanghai, China. Experiment results show that (i) the proposed method could achieve an average accuracy of 77.15% with a 1-hour prediction length and it outperforms the baseline models for all tested prediction lengths; (ii) it is also revealed that the prediction accuracy varies dramatically with different states and time periods. Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). As for time periods, the EV states around 08:00 am and 04:00 pm are hard to predict, and a comparatively low prediction accuracy (close to 60%) is obtained; and (iii) the stability and robustness analysis implies that the proposed model is stable and insensitive to SOC noise or season.
Transportation engineering, Transportation and communications
The outbreak of COVID-19 and preventive measures to limit the spread of the virus has significantly impacted our daily activities. This study aims to investigate the effect of daily activity engagement including travel activity and sociodemographic characteristics on travel satisfaction during COVID-19. This study develops a latent segmentation-based ordered logit (LSOL) model using data from the 2020 COVID-19 Survey for Assessing Travel Impact (COST), for the Kelowna region of British Columbia, Canada. The LSOL model accommodates the ordinal nature of the satisfaction level and captures heterogeneity by allocating individuals into discrete latent segments. The model results suggest that the two-segment LSOL model fits the data best. Segment one is more likely to be younger and older high-income workers; whereas, segment two includes middle-aged lower-income, unemployed individuals. The model results suggest that daily activity engagement and sociodemographic attributes significantly affect travel satisfaction. For example, participation in travel for routine shopping, recreational activity, and household errands has a positive effect on travel satisfaction. The use of transportation modes like bike/walk depicted a higher probability to yield travel satisfaction. The model confirms the existence of significant heterogeneity. For instance, travel for work showed a negative relationship in segment one; whereas, a positive relationship is found in segment two. Access to higher household vehicle yield lower satisfaction in segment one; in contrast, a positive relationship is found in segment two. The findings of this study provide important insights towards maintaining the health and well-being of the population during this and any future pandemic crisis.