Cross-Media Wireless Made Easier: Tuning Media Interfaces with Flexible Metasurfaces

Emerging wireless IoT applications increasingly venture beyond over-the-air communication, such as deep-tissue networking for implantable sensors, air-water communication for ocean monitoring, and soil sensing. These applications face the fundamental challenge of significant power loss due to reflection at media interfaces. We present RF-Mediator, a programmable metasurface system placed at media interfaces to virtually mask the presence of the physical boundary. It is designed as a single-layer metasurface comprising arrays of varactor-based elements. By tuning the bias voltage element-wise, the surface mediates between media on both sides dynamically and beamforms towards the endpoint to boost transmission through the interface, as if no media interface existed. The control algorithm determines the surface configuration by probing the search space efficiently. We fabricate the surface on a thin, flexible substrate, and experiment with several cross-media setups. Extensive evaluation shows that RF-Mediator provides a median power gain of 8 dB for air-to-tissue links and up to 30 dB for cross-media backscatter links

Softly, Deftly, Scrolls Unfurl Their Splendor: Rolling Flexible Surfaces for Wideband Wireless

With new frequency bands opening up, emerging wireless IoT devices are capitalizing on an increasingly divergent range of frequencies. However, existing coverage provisioning practice is often tied to specific standards and frequencies. There is little shareable wireless infrastructure for concurrent links on different frequencies, across networks and standards. This paper presents Scrolls, a frequency-tunable soft smart surface system to enhance wideband, multi-network coverage. Scrolls' hardware comprises many rows of rollable thin plastic film, each attached with flexible copper strips. When rolled to different lengths, the copper strips act as wire antennas reflecting signals on the corresponding frequencies. The surface control algorithm determines the unrolled strip lengths for link enhancement by probing the search space efficiently. We build a set of distributed, composable Scrolls prototypes and deploy them in an office. Extensive evaluation shows that Scrolls can adapt the antenna lengths effectively to provide link enhancement across diverse standards on sub-6 GHz bands. For concurrent links on 900 MHz (LoRa), 2.4 GHz (Wi-Fi), 3.7 GHz, and 5 GHz, Scrolls can provide received signal strength gains to all links simultaneously, by a median of 4 dB and up to 10 d

Research on Strategy Control of Taxi Carpooling Detour Route under Uncertain Environment

For the problem of route choice in taxi carpooling detour, considering the uncertainty of traffic and the characteristic of passengers’ noncomplete rationality, an evolutionary game model of taxi carpooling detour route is built, in which prospect theory is introduced and revenue of strategy is replaced by prospect value. The model reflects more really decision-making psychology of passengers. Then the stable strategies of the model are studied, and the influences of detour distance and traffic congestion on detour carpooling success are analyzed, respectively. The results show that when at least one route of which prospect values for two passenger sides are both positive exists, carpooling route can reach an agreement. The route is stable strategy of evolutionary game, and the passengers requiring short travel time tend to select the nondetour route. With the increase of detour distance and traffic congestion rate, the possibility of reaching an agreement decreases gradually; that is, possibility of carpooling failure increases. So taxi carpooling detour is possible under the certain condition, but some measures must be carried out such as constraints of detour distance and mitigation of traffic congestion to improve carpooling success probability. These conclusions have a certain guiding significance to the formulation of taxi carpooling policy

An Improved Genetic Algorithm for the Large-Scale Rural Highway Network Layout

For the layout problem of rural highway network, which is often characterized by a cluster of geographically dispersed nodes, neither the Prim algorithm nor the Kruskal algorithm can be readily applied, because the calculating speed and accuracy are by no means satisfactory. Rather than these two polynomial algorithms and the traditional genetic algorithm, this paper proposes an improved genetic algorithm. It encodes the minimum spanning trees of large-scale rural highway network layout with Prufer array, a method which can reduce the length of chromosome; it decodes Prufer array by using an efficient algorithm with time complexity o(n) and adopting the single transposition method and orthoposition exchange method, substitutes for traditional crossover and mutation operations, which can effectively overcome the prematurity of genetic algorithm. Computer simulation tests and case study confirm that the improved genetic algorithm is better than the traditional one

Road traffic flow forewarning and control model with the slope of the change rate

Zadnjih je godina točno i učinkovito kratkoročno predviđanje toka prometa u realnom vremenu jedna od ključnih tehnologija u ostvarenju upravljanja i reguliranja tokom cestovnog prometa iz ITS područja (Intelligent Transport System). Analizirajući postojeći model predviđanja toka prometa, predlaže se model za reguliranje cestovnog toka prometa, Model može pronaći nenormalnu točku analizom vremenskih serija toka prometa primjenom pada promjene brzine (slope change rate), i može analizirati taj trend promjena toka prometa u svrhu reguliranja toka prometa. Rezultati pokazuju da je algoritam pogodan za problem reguliranja vršnog cestovnog opterećenja prometa , a može biti učinkovit u reguliranju cestovnog prometa.Real-time, accurate and efficiency short term traffic flow prediction is one of the key technologies to realize traffic flow guidance and traffic control, which has been widely concerned in the domain of ITS (Intelligent Transport System) during recent years. Through the study of the existing traffic flow prediction model, road traffic flow control model with the slope of the change rate is proposed. The model can find out abnormal point from the traffic flow time series by the use of the slope change rate, and it can analyse this trend of traffic flow changes for control purposes of traffic flow. The achieved results indicate that the algorithm is suitable for road traffic flow peak control problem and could be effective for road traffic flow control

Path optimization of taxi carpooling.

The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers

An Extended Car-Following Model at Signalised Intersections

An extended car-following model is proposed on the basis of experimental analysis to improve the performance of the traditional car-following model and simulate a microscopic car-following behaviour at signalised intersections. The new car-following model considers vehicle gather and dissipation. Firstly, the parameters of optimal velocity, generalised force and full velocity difference models are calibrated by measured data, and the problems and causes of the three models are analysed with a realistic trajectory simulation as an evaluation criterion. Secondly, an extended car-following model based on the full optimal velocity model is proposed by considering the vehicle gather and dissipation. The parameters of the new car-following model are calibrated by the measured data, and the model is compared with comparative models on the basis of isolated point data and the entire car-following process. Simulation results show that the optimal velocity, generalised force, and full velocity difference models cannot effectively simulate a microscopic car-following behaviour at signalised intersections, whereas the new car-following model can avoid a collision and has a high fit degree for simulating the measured data of the car-following behaviour at signalised intersections

Research on Taxi Driver Strategy Game Evolution with Carpooling Detour

For the problem of taxi carpooling detour, this paper studies driver strategy choice with carpooling detour. The model of taxi driver strategy evolution with carpooling detour is built based on prospect theory and evolution game theory. Driver stable strategies are analyzed under the conditions of complaint mechanism and absence of mechanism, respectively. The results show that passenger’s complaint mechanism can effectively decrease the phenomenon of driver refusing passengers with carpooling detour. When probability of passenger complaint reaches a certain level, the stable strategy of driver is to take carpooling detour passengers. Meanwhile, limiting detour distance and easing traffic congestion can decrease the possibility of refusing passengers. These conclusions have a certain guiding significance to formulating taxi policy

A Multiobjective Route Robust Optimization Model and Algorithm for Hazmat Transportation

Aiming at route optimization problem of hazardous materials transportation in uncertain environment, this paper presents a multiobjective robust optimization model by taking robust control parameters into consideration. The objective of the model is to minimize not only transportation risk but also transportation time, and a robust counterpart of the model is introduced through applying the Bertsimas-Sim robust optimization theory. Moreover, a fuzzy C-means clustering-particle swarm optimization (FCMC-PSO) algorithm is designed, and the FCMC algorithm is used to cluster the demand points. In addition the PSO algorithm with the adaptive archives grid is used to calculate the robust optimization route of hazmat transportation. Finally, the computational results show the multiobjective route robust optimization model with 3 centers and 20 demand points’ sample studied and FCMC-PSO algorithm for hazmat transportation can obtain different robustness Pareto solution sets. As a result, this study will provide basic theory support for hazmat transportation safeguarding