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Sustainable freight transportation is one of the essential concepts in the smart city. Under this concept, many people connected with mobile devices produce location data and potential opportunities for transporting small objects in a more environmentally friendly and sustainable way. Crowdshipping, which utilises public people as transportation, is one of the terminal solutions in the last mile delivery scenario. Nevertheless, precisely assigning the delivery to the right crowd willing to accept the job is challenging because the solution space is too large to perform a full search. This article proposes a trajectory feature extraction algorithm and a task‐to‐crowd matching (T2CM) algorithm for coping with the job‐to‐crowd assignment problem. A simulation based on the real‐world dataset is conducted on three different scenarios to justify the outcome from our proposed method to the job assignment results. [ABSTRACT FROM AUTHOR]

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods. [ABSTRACT FROM AUTHOR]