Research on RFID Anticollision Algorithms in Industrial Internet of Things

As a perception enabling technology of the Internet of Things, RFID can quickly identify target objects. The tag-to-tag collision problem seriously affects the identification performance of the RFID system, which causes the reader to be unable to accurately identify any tag within the specific time. The mainstream anticollision algorithms are limited by the performance bottleneck under the standard framework. In this paper, we analyze the features and merits of three kinds of algorithms in detail and propose a new algorithm architecture for RFID anticollision. Through the extensive experimental results comparison, we prove that the new architecture is effective to improve the performance of DFSA algorithms. Finally, we summarize the future research trends in the RFID anticollision algorithms.

The innovative design of balancing mechanism by TRIZ

In this paper, an innovative design of balancing mechanism is completed after solving the problem of collision between balancing weight and barrel by TRIZ. For the original design of adding a balancing weight, the Functional Model Analysis is set and the key point of the collision problem is found. For the sake of improving the original design, this paper find out the key point of collision problem and adequate analysis by TRIZ theory, and proposed an innovative solution at last.

An Efficient Q -Algorithm for RFID Tag Anticollision

In large-scale Internet of Things (IoT) applications, tags are attached to items, and users use a radiofrequency identification (RFID) reader to quickly identify tags and obtain the corresponding item information. Since multiple tags share the same channel to communicate with the reader, when they respond simultaneously, tag collision will occur, and the reader cannot successfully obtain the information from the tag. To cope with the tag collision problem, ultrahigh frequency (UHF) RFID standard EPC G1 Gen2 specifies an anticollision protocol to identify a large number of RFID tags in an efficient way. The Q -algorithm has attracted much more attention as the efficiency of an EPC C1 Gen2-based RFID system can be significantly improved by only a slight adjustment to the algorithm. In this paper, we propose a novel Q -algorithm for RFID tag identification, namely, HTEQ, which optimizes the time efficiency of an EPC C1 Gen2-based RFID system to the utmost limit. Extensive simulations verify that our proposed HTEQ is exceptionally expeditious compared to other algorithms, which promises it to be competitive in large-scale IoT environments.

CT-Based Collision Prediction Software for External-Beam Radiation Therapy

PurposeBeam angle optimization is a critical issue for modern radiotherapy (RT) and is a challenging task, especially for large body sizes and noncoplanar designs. Noncoplanar RT techniques may have dosimetric advantages but increase the risk of mechanical collision. We propose a software solution to accurately predict colliding/noncolliding configurations for coplanar and noncoplanar beams.Materials and MethodsIndividualized software models for two different linear accelerators were built to simulate noncolliding gantry orientations for phantom/patient subjects. The sizes and shapes of the accelerators were delineated based on their manuals and on-site measurements. The external surfaces of the subjects were automatically contoured based on computed tomography (CT) simulations. An Alderson Radiation Therapy phantom was used to predict the accuracy of spatial collision prediction by the software. A gantry collision problem encountered by one patient during initial setup was also used to test the validity of the software. Results: In the comparison between the software estimates and on-site measurements, the noncoplanar collision angles were all predicted within a 5-degree difference in gantry position. The confusion matrix was calculated for each of the two empty accelerator models, and the accuracies were 98.7% and 97.3%. The true positive rates were 97.7% and 96.9%, while the true negative rates were 99.8% and 97.9%, respectively. For the phantom study, the collision angles were predicted within a 5-degree difference. The software successfully predicted the collision problem encountered by the breast cancer patient in the initial setup position and generated shifted coordinates that were validated to correspond to a noncolliding geometry.ConclusionThe developed software effectively and accurately predicted collisions for accelerator-only, phantom, and patient setups. This software may help prevent collisions and expand the range of spatially applicable beam angles.

Time Slot Detection-Based M -ary Tree Anticollision Identification Protocol for RFID Tags in the Internet of Things

Recently, a number of articles have proposed query tree algorithms based on bit tracking to solve the multitag collision problem in radio frequency identification systems. However, these algorithms still have problems such as idle slots and redundant prefixes. In this paper, a time slot detection-based M -ary tree (Time Slot Detection based M -ary tree, TSDM) tag anticollision algorithm has been proposed. When a collision occurs, the reader sends a predetection command to detect the distribution of the m -bit ID in the 2m subslots; then, the time slot after predetection is processed according to the format of the frame-like. The idle time slots have been eliminate through the detection. Using a frame-like mode, only the frame start command carries parameters, and the other time slot start commands do not carry any parameters, thereby reducing the communication of each interaction. Firstly, the research status of the anticollision algorithm is summarized, and then the TSDM algorithm is explained in detail. Finally, through theoretical analysis and simulation, it is proved that the time cost of the TSDM algorithm proposed in this paper is reduced by 12.57%, the energy cost is reduced by 12.65%, and the key performance outperforms the other anticollision algorithms.

A Navigation Probability Map in Pedestrian Dynamic Environment Based on Influencer Recognition Model

One of the challenging problems in robot navigation is efficient and safe planning in a highly dynamic environment, where the robot is required to understand pedestrian patterns in the environment, such as train station. The rapid movement of pedestrians makes the robot more difficult to solve the collision problem. In this paper, we propose a navigation probability map to solve the pedestrians’ rapid movement problem based on the influencer recognition model (IRM). The influencer recognition model (IRM) is a data-driven model to infer a distribution over possible causes of pedestrian’s turning. With this model, we can obtain a navigation probability map by analyzing the changes in the effective pedestrian trajectory. Finally, we combined navigation probability map and artificial potential field (APF) method to propose a robot navigation method and verified it on our data-set, which is an unobstructed, overlooked pedestrians’ data-set collected by us.

Drone-Drone Collisions Over New York City

Delivery of packages by drones to homes and workplaces is faster and cheaper than delivery by van and hence has significant potential. That potential can only be realized if delivery by drones is safe. In particular, such drones must not pose a threat from in-air drone-drone collisions. To determine how serious the drone collision problem might be, we: 1) focus on a situation where they would be maximized, New York City, and 2) investigate the “do nothing” alternative, in which the drones do nothing to avoid collisions. Such drones, lacking a collision-avoidance capability, are “dumb” drones, and will have a much higher collision rate than “smart” drones, which do have that capability and will ultimately be deployed. The do nothing alternative is useful for determining the extent to which a collision-avoidance capability is needed. The dumb drone collision rate is found to be unacceptably high. Actual smart drones to be ultimately deployed must therefore have a highly effective collision-avoidance capability to bring collision rates close to zero. The needed effectiveness is quantified.

Comparison on Search Failure between Hash Tables and a Functional Bloom Filter

Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.