The radio map built during the offline phase of any WiFi fingerprint-based localization system usually scales with the number of WiFi access points (APs) that can be detected at a single location by any mobile device, but this number in practice can be as large as 100, only a few of which essentially contribute to localization. Simply involving all the APs in location fingerprints and thus in the radio map not only wastes excessive storage but also leads to a large computational overhead. In this paper, a theoretical analysis of WiFi-based location determination is conducted to investigate the resulting localization errors and reveals that some critical APs contribute significantly more to localization than the others, implying that it is unnecessary to include every AP in localization. Consequently, a heuristic AP selection algorithm based on the error analysis is proposed to efficiently select a subset of APs for use in localization. Finally, extensive experiments are carried out by using both the UJI dataset available online and the dataset collected in our laboratory, and it is shown that the proposed algorithm not only significantly reduces the redundancy of APs in WiFi fingerprint-based localization but also substantially improves the localization accuracy of the k nearest neighbor (KNN) method.

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The Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual needs of users in the IoT service set, the elements of which have the same function, is an unavoidable issue. Therefore, for the robustness of the IoT system, evaluating the quality level of the IoT service to provide a reference for the users choosing the most appropriate service has become a hot topic. Most of the current methods just use some static data to evaluate the quality of the service and ignore the dynamic changing trend of the service performance. In this paper, an estimation mechanism for the quality level of the IoT service based on fuzzy logic is conducted to grade the quality of the service. Specifically, the comprehensive factors are taken into account according to the defined level changing rules and the effect of the service in the previous execution process, so that it can provide users with an effective reference. Experiments are carried out by using a simulated service set. It is shown that the proposed algorithm can estimate the quality level of the service more comprehensively and reasonably, which is evidently superior to the other two common methods, i.e., the estimating method by a Randomization Test (RT) and the estimating method by a Single Test in Steps (STS).

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