Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. 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 lightweight 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 adds only 12.6 milliseconds of latency to a current informativeness-unaware system. Next, we test the networking performance of AICP at scale and show that AICP effectively filters out less relevant packets and decreases the channel busy time.

Radio frequency identification (RFID) has been widely used in many fields such as object tracking and inventory management. For RFID systems, grouping is a fundamental issue which can support efficient multicast transmissions, dynamic tag management, and accurate aggregate queries. Existing grouping protocols have drawbacks of unknown theoretical communication time, high computational cost on the server end and inability to deal with unexpected tags which are those tags whose IDs have not been collected by readers. In this paper, we would like to address the above limitations and consider a more general grouping problem that allows an arbitrary number of unexpected tags to present. Our objective is to design a protocol that guarantees the reader to efficiently and correctly notify each known tag of its group-ID, while the probability that an unexpected tag is mistakenly notified of any group-ID is smaller than a pre-determined value. In this paper, we first obtain a lower bound on the communication time for solving this generalized grouping problem. Then, we propose a near-optimal protocol, called OPT-G, and prove that its communication time approximately equals the lower bound. Finally, we report extensive simulation results that demonstrate OPT-G's near-optimal performance and its superiority over existing baseline schemes.

Mobile radio frequency identification (RFID) systems typically experience the continual movement of many tags rapidly going in and out of the interrogating range of readers. Readers that are deployed to maintain a current, real-time list of tags, which are present in the interrogating zone at any moment, must repeatedly execute a series of reading cycles. Each of these reading cycles provides the readers very limited time to identify unknown tags (those newly entering into the reader’s range), and, at the same time, to detect missing tags (those just leaving the reader’s range). In this paper, we study the continuous tag recognition problem, which is critical for mobile RFID systems. First, we obtain a lower bound on communication time for solving this problem. We then design a near-OPTimal protocoL, called OPT-L, and prove that its communication time is approximately equal to the lower bound. Finally, we present extensive simulation and experimental results that demonstrate OPT-L’s superior performance over other existing protocols.

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 Δ(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 state-of-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.

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 ". 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 Information-Centric Internet of Things (IC-IoT) applications, where 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 continuously generated IoT data. In this work, we study the Approximate Range Emptiness problem over Sliding windows in 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 log2(nL/ε) + Θ(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. Extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.