Environmental Monitoring via Vehicular Crowdsensing

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…Focusing on vehicles and road transportation systems as crowdsensing platforms, the paradigm of drive-by sensing has been coined, and interesting experimental campaigns have been conducted in New York City to first quantify the sensing power of crowdsourced vehicle fleets [212,213] and then assess how the different mobility patterns (either predictable or completely random) impact the discrete-time sampling process [214,215]. Some theoretical work considered the adoption of a network of crowdsensing vehicles and mapped the problem of estimating the air pollution levels into a problem of spatial field reconstruction from samples randomly gathered in a multidimensional space [216]. Improved accuracy and efficiency can be obtained when the correlations among the sensed data are explicitly considered in the model and the unsensed regions are properly characterized [217].…”

“…An interesting comparison analysis between the deterministic and random sampling schemes has been conducted in [328], showing that the former provides improved reconstruction performance mainly in the high-SNR regime, while in the presence of low SNRs, both schemes behave in essentially the same way. A recent work explicitly analyzed the problem of reconstructing an environmental phenomenon f (p, t) from a set of samples randomly collected by crowdsensing vehicles [216]. By taking into account also the presence of a WSN infrastructure gathering data at fixed locations, it is demonstrated that stochastic sampling via crowdsensing leads to significantly improved reconstruction accuracy, especially when the WSN provides insufficient sampling information.…”

Towards Uniform Urban Map Coverage in Vehicular Crowd-Sensing: A Decentralized Incentivization Solution

“…Many new solutions can be developed on top of the IoV paradigm, including Vehicular Crowd-Sensing (VCS), a special case of Mobile Crowd-Sensing, in which the data collection is performed by vehicles acting as probes, sensing information from the environment around them in an opportunistic fashion, i.e., without requiring the driver to explicitly trigger the sensing [12], [48]. When data sensed from swarms of vehicles is aggregated on a back-end, it can be used to generate an unprecedented amount of spatio-temporal information/knowledge, which could be used to enable many new exciting and valuable use cases, such as more accurate traffic predictions [40] and real-time on-street parking availability [5], air quality [11] or weather monitoring [33], better surveillance of urban scenarios [27], and so on. Hence, it is not surprising that the value of IoV data is estimated to be worth between US$11.6 billion and US$92.6 billion for the US market alone [39].…”