VSMURF: A Novel Sliding Window Cleaning Algorithm for RFID Networks

Abstract

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things (IoT) and is used in many areas, such as mobile payments, public transportation, smart lock, and environment protection. However, the performance of RFID equipment can be easily affected by the surrounding environment, such as electronic productions and metal appliances. These can impose an impact on the RF signal, which makes the collection of RFID data unreliable. Usually, the unreliability of RFID source data includes three aspects: false negatives, false positives, and dirty data. False negatives are the key problem, as the probability of false positives and dirty data occurrence is relatively small. This paper proposes a novel sliding window cleaning algorithm called VSMURF, which is based on the traditional SMURF algorithm which combines the dynamic change of tags and the value analysis of confidence. Experimental results show that VSMURF algorithm performs better in most conditions and when the tag's speed is low or high. In particular, if the velocity parameter is set to 2 m/epoch, our proposed VSMURF algorithm performs better than SMURF. The results also show that VSMURF algorithm has better performance than other algorithms in solving the problem of false negatives for RFID networks.

A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data

“…However, the effectiveness of the sliding window method depends on the selection of attribute fields and the size of the window. Too large of a window size may result in higher time complexity, while a small window size may result in incomplete filtering [13].…”

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

“…In the Information-Centric Internet of Things (IC-IoT), IoT data, such as radio frequency identification (RFID) tag data [6], or sensor data [7] are continuously generated from a large number of resource-constrained devices, and then are delivered and cached throughout a network for the convenience of close data copy retrieval, see [6], [8]- [13]. However, as the potential amount of IoT data is huge, (for example, an RFID tag-ID can be 128-bit long [6]-there are 2 128 possible tag-IDs), and these IoT data are continuously generated at a fast speed [8], [14]- [17], [19], quickly processing a query interval over IoT data streams poses a challenge for facilitating real-time services and monitoring in these systems. In Fig.…”

“…Goswami et al [4] study the approximate range emptiness problem by focusing on the simple case in which S (the set to be represented) is static, rendering the derived lower bound and proposed data structure [4] unsuitable for the more general scenarios of IoT data streams, which are commonly seen in IoT environments [8], [14]- [17], [19]- [22].…”

“…(2) Usually, we are more interested in new data (for example, the latest n elements in an IoT data stream) rather than old data in IoT data streams, and the value or the hit ratio of cached IoT data in IC-IoT systems decreases with time [8], [14], [15], [19], [20], [23].…”

“…There are some studies [8], [14]- [17], [19], [30] on efficient streaming processing in IC-IoT systems. For example, in [15], Ahlgren et al propose a method for finding the newest element in an IoT data stream.…”