Vehicular crowd-sensing: a parametric routing algorithm to increase spatio-temporal road network coverage

Author(s):  
Dario Asprone ◽  
Sergio Di Martino ◽  
Paola Festa ◽  
Luigi Libero Lucio Starace
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.


2011 ◽  
Vol 14 (4) ◽  
pp. 389-413 ◽  
Author(s):  
Tao Cheng ◽  
James Haworth ◽  
Jiaqiu Wang

2021 ◽  
Author(s):  
Martijn Pallandt ◽  
Jitendra Kumar ◽  
Marguerite Mauritz ◽  
Edward Schuur ◽  
Anna-Maria Virkkala ◽  
...  

Abstract. Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy covariance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatio-temporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network’s representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets, and changes therein. This study provides an inventory of Arctic (here >= 60° N) EC sites, which has also been made available online (https://cosima.nceas.ucsb.edu/carbon-flux-sites/). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, based on 18 bioclimatic and edaphic variables, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arctic study domain. With the majority of sites located in Fennoscandia and Alaska, these regions were assigned the highest level of network representativeness, while large parts of Siberia and patches of Canada were classified as under-represented. This division between regions is further emphasized for wintertime and methane flux data coverage. Across the Arctic, particularly mountainous regions were poorly represented by the current EC observation network. We tested three different strategies to identify new site locations, or upgrades of existing sites, that optimally enhance the representativeness of the current EC network. While 15 new sites can improve the representativeness of the pan-Arctic network by 20 percent, upgrading as few as 10 existing sites to capture methane fluxes, or remain active during wintertime, can improve their respective network coverage by 28 to 33 percent. This targeted network improvement could be shown to be clearly superior to an unguided selection of new sites, therefore leading to substantial improvements in network coverage based on relatively small investments.


Author(s):  
Altin Zejnullahu ◽  
Zhilbert Tafa

Wireless sensor networks (WSNs) compose the fundamental platform for a number of Internet of Things (IoT) applications, especially those related to the environmental, health, and military surveillance. While being autonomous in power supply, the main challenge in node’s processing and communication architecture design remains the energy efficiency. However, this goal should not limit the main functionality of the system which is often related to the network coverage and connectivity. This paper shows the implementation of the Ad-hoc On-demand Distance Vector (AODV) routing algorithm in an XBee based platform. As shown, the network can achieve low power consumption per node primarily due to the energy efficiency of the wireless transceivers and the due to the capability of the firmware to enable different operation modes. On the other hand, while inheriting the advantages of flooding-based route discovery protocols, the implemented AODV algorithm further minimizes the data and processing overhead, which implies the additional lifetime prolongation of the energy-constrained mesh network.


2021 ◽  
Vol 23 (05) ◽  
pp. 694-707
Author(s):  
Dr. D. I. George Amalarethinam ◽  
◽  
Ms. P. Mercy ◽  

The Internet of Things (IoT) is a network that includes physical things capable of aggregating and communicating electronic information. With the advancement in wireless sensor networks, IoT provides highly efficient communication for various real-time applications. IoT networks are large-scale networks where routing can be improved by focusing on the Quality of Service (QoS) Parameter. Network coverage can be enhanced by hierarchical clustering of the nodes which increases the network lifetime. The proposed algorithm Enhanced Fuzzy Based Clustering and Routing Algorithm (EFCRA) performs distance and energy-based cluster head selection to find a new path from source to destination. The algorithm uses Fuzzy c-means clustering to provide optimization in forming cluster centers. The cluster head (CH) is identified based on the minimum distance and maximum energy of the sensor node. The cluster head is updated when its energy is lesser than the threshold value. The distance between sensor nodes and its CH node and then to the destination is computed using Dijkstra’s algorithm. The proposed routing strategy provides improved network coverage and throughput which extends the lifetime of the IoT network.


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