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Wireless sensor networks (WSNs) have been widely applied in various industrial applications, which involve collecting a massive amount of heterogeneous sensory data. However, most of the data-gathering strategies for WSNs cannot avoid the hotspot problem in local or whole deployment area. Hotspot problem affects the network connectivity and decreases the network lifetime. Hence, we propose a tree-cluster-based data-gathering algorithm (TCBDGA) for WSNs with a mobile sink. A novel weight-based tree-construction method is introduced. The root nodes of the constructed trees are defined as rendezvous points (RPs). Additionally, some special nodes called subrendezvous points (SRPs) are selected according to their traffic load and hops to root nodes. RPs and SRPs are viewed as stop points of the mobile sink for data collection, and can be reselected after a certain period. The simulation and comparison with other algorithms show that our TCBDGA can significantly balance the load of the whole network, reduce the energy consumption, alleviate the hotspot problem, and prolong the network lifetime.INDEX TERMS Data-gathering scheme, cluster, mobile sink, wireless sensor networks.

Due to limited functionalities and potentially large number of sensors, existing routing strategies proposed for mobile ad hoc networks are not directly applicable to wireless sensor networks. In this paper, we present a meshed multipath routing (M-MPR) protocol with selective forwarding (SF) of packets and end-to-end forward error correction (FEC) coding. We also describe a meshed multipath searching scheme suitable for sensor networks, which has a reduced signaling overhead and nodal database. Our performance evaluations show that (1) M-MPR achieves a much improved throughput over conventional disjoint multipath routing with comparable power consumption and receiver complexity; (2) to successfully route a message using FEC coding, selective forwarding (SF) consumes much less network resources, such as channel bandwidth and battery power, than packet replication (or limited flooding).

Land surface albedo is one of the key geophysical variables controlling the surface radiation budget. In recent years, land surface albedo products have been generated using data from various satellites. However, some problems exist in those products due to either the failure of the current retrieving procedures resulting from persistent clouds and/or abrupt surface changes, or the reduced temporal or spatial coverage, which may limit their applications. Rapidly generated albedo products that help reduce the impacts of cloud contamination and improve the capture of events such as ephemeral snow and vegetation growth are in demand. In this study, we propose a method for estimating the land surface albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) data using a short temporal window. Instead of executing the atmospheric correction first and then fitting the surface reflectance in the current MODIS albedo procedure, the atmospheric properties (e.g., aerosol optical depth) and surface properties (e.g., surface bidirectional reflectance) were estimated simultaneously. Validations were carried out using various data sources including ground measurements (e.g., from the Surface Radiation (SURFRAD) Network and Greenland Climate Network (GC-Net)) and MODIS AERONET-based Surface Reflectance Validation Network (MODASRVN) data. The results showed comparable albedo estimates with both MODIS data and ground measurements, and the MODASRVN instantaneous surface reflectance was in good agreement with the reflectance estimation from our method. Aerosol optical depth (AOD) retrievals over SURFRAD and MODASRVN sites were also compared with ground measurements. Validation results showed estimation accuracies similar to those of MODIS aerosol products.

Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize---which is fixed in conventional Tullock contests---with a prize function that is dependent on the (unknown) winner's contribution, in order to maximize the crowdsourcer's utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players' antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcer's utility cum profit and up to nine folds on the players' social welfare.Comment: 9 pages, 4 figures, 3 table