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Web services integrate various components in the Internet of Things (IoT). In a Web service-based data-collection system with multiple smart sensor nodes periodically sampling and estimating the same unknown physical parameter of interest, the smart sensor nodes first submit their estimates to the Web server, and then, the server picking the one with the minimum error seems to be a practical way to arrive at a minimum error estimate (MEE). More submissions provide the Web server with more candidates to consider, which can maximize the probability of the server guaranteeing the MEE, while also leading to more network traffic. Therefore, how to make the optimal tradeoff between network traffic and sensing accuracy arises as an interesting problem. This article proposes a network traffic-dependent probability threshold policy within an intended underlying optimization-theoretical framework to address this problem. The policy is such that the smart sensor nodes submit their estimates and corresponding estimation errors (ECEEs) to the Web server within a tolerable network traffic threshold while maximizing the probability of the server delivering the MEE. Theoretical analysis, simulation, and field experiments document and illustrate its performance. <italic>Note to Practitioners</italic>&#x2014;This article addresses the interesting tradeoff between sensing accuracy and network traffic demand in the Web service-based data-collection system that operates in some remote areas with limited network traffic. It helps to improve the operation efficiency of the Internet-of-Things (IoT) systems that employ Web service technology to enable the Web server to deliver minimum error estimate with maximum probability while keeping the network traffic within a given range. Our simulation and experimental investigations show that the solution developed here outperforms existing solutions.

Optimization of Web Service-Based Data-Collection System With Smart Sensor Nodes for Balance Between Network Traffic and Sensing Accuracy