Internet of Things (IoT) has gained great popularity in various fields including smart warehouse and intelligent manufacturing. As a building block of IoT network, the Radio Frequency IDentification (RFID) technology enables a large and ever-increasing number of physical objects to be monitored across the Internet via tag identification. Efficiently managing massive tags in RFID systems becomes an important research issue for IoT networks. This paper focuses on a fundamental management problem -tag-sorting, which is to (1) put a set S of identified tags into a certain order by informing each tag t ∈ S of a unique integer ∆(t) ∈ {1, 2, • • • , |S|}, and meanwhile (2) keep unidentified tags from receiving any of these integers. For RFID systems, it is critical to solve this problem as quickly as possible in the sense that, once sorted, every identified tag t ∈ S can be manipulated via t's log 2 (|S|)-bit integer significantly shorter than t's 96-bit long tag-ID (log 2 (|S|) 96), boosting efficiency substantially. The existing works of literature, however, fails to solve this problem rapidly, as they accomplish (1) and (2) separately by using aloha-like protocols and Bloom filters, which incur a long communication time far from the optimum. In this paper, we overcome this drawback by proposing a protocol P sort capable of solving the problem fastly. In particular, this protocol is built with a novel data structure and communication scheme to achieve (1) and (2) simultaneously by using a communication time proven to be much less than the stateof-the-art protocols. The simulation results demonstrate the competence of P sort in achieving about 1.4× speedup than the state-of-the-art solutions.INDEX TERMS IoT network, edge server, RFID systems, tag management, tag-sorting, unidentified tag, identified tag.
Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, are currently constrained to visual information in their lines-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception ranges. Existing solutions imply significant network and computation load, as well as high flow of not-always-relevant data received by vehicles. To address such issues, and thus account for the inherently diverse informativeness of the data, we present Augmented Informative Cooperative Perception (AICP) as the first fast-filtering system which optimizes the informativeness of shared data at vehicles. AICP displays the filtered data to the drivers in augmented reality head-up display.To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and light-weight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype realizes the informative-optimized cooperative perception with only 12.6 milliseconds additional latency. Next, we test the networking performance of AICP at scale and show that AICP effectively filter out less relevant packets and decreases the channel busy time.
The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U = {0, 1, • • • , N − 1} and answer an emptiness query of the form ''S ∩ [a; b] = ∅?'' for an interval [a; b] of length L (a, b ∈ U ), with a false positive rate ε. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by ε-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for ε-ARESD-problem is n log 2 (nL/ε) + (n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.INDEX TERMS Approximate range emptiness, data structure, information-centric network, Internet of Things, space lower bound.
Tag-selection problem, which selects a set of wanted tags from a tag population, is vital for boosting efficiencies of the real-time applications in RFID systems. However, prior arts for the problem can not be applied to RFID systems directly, given that they either require additional computing functions implemented in tag's chips or require a time-consuming pre-process with a large communication cost. This paper studies the tag-selection problem and propose an efficient Electronic Product Code (EPC)-based tag selection method with theoretical analysis. In particular, firstly, we prove a nontrivial lower bound of communication overhead for a protocol which is capable of solving the tag-selection problem. Secondly, we propose an efficient protocol, denoted by TagSP, which only uses the ''select'' command (a mandatory command that all RFID tags shall support) and EPC. The proposed TagSP can be applied directly into offthe-shelf RFID systems with a communication overhead close to the lower bound. Extensive simulations are conducted and the simulation results show TagSP's superiority compared with existing protocols. INDEX TERMS RFID systems, tag selection, lower bound of communication overhead, EPC.
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