An Improved Swarm Optimizer for RFID Network Scheduling

2013 ◽  
Vol 427-429 ◽  
pp. 600-605
Author(s):  
Shi Lei Lu ◽  
Shun Zheng Yu
Keyword(s):  
Radio Frequency Identification ◽  
Network Performance ◽  
Particle Swarm Optimization Algorithm ◽  
Foraging Strategy ◽  
Work Status ◽  
Network Scheduling ◽  
Swarm Optimization ◽  
Network Topologies ◽  
Frequency Identification ◽  
High Diversity

Optimization of network scheduling is a significant way to improve the performance of the radio frequency identification (RFID) networks. This paper proposes an improved particle swarm optimization algorithm (PSO). It uses an animal foraging strategy to maintain a high diversity of swarms, which can protect them from premature convergence. The proposed algorithm is used to optimize the network performance by determining the optimal work status of readers. It has been tested in two different RFID network topologies to evaluate the effectivenesss. The simulation results reveal that the proposed algorithm outperforms the other algorithms in terms of optimization precision.

scholarly journals An optimization model for the operations of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401774801 ◽  
Author(s):  
Jianwei Ren ◽  
Chunhua Chen ◽  
Hao Xu ◽  
Qingqing Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Optimization Algorithm ◽  
Radio Frequency Identification ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Identification System ◽  
Swarm Optimization ◽  
Frequency Identification

In a pallet pool, pallets would be delivered through a supply chain. The operation procedure that consists of at least five operation processes as distribution, reposition, recycling, purchase (or rent), and maintenance is quite complex. These pallets are likely to be damaged, lost, destroyed, and so on. So, it is necessary to monitor the pallets using radio-frequency identification technology. However, there is no literature on the management of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets being put into consideration. In our research, an optimization model is presented to manage such a pallet pool. The objective of the optimization model is to minimize the total operation cost of a pallet pool including distribution cost, reposition cost, recycling cost, purchase or rent cost, loss cost, maintenance cost, loading and unloading cost, storage cost, and punishment cost. A particle swarm optimization algorithm is developed in Microsoft Visual Basic. Our numerical example shows that the optimization model and particle swarm optimization algorithm are effective. It is proved that the model and algorithm also can be used to measure whether the investment of a radio-frequency identification system is valuable or not. We proposed some suggestions for the pallet pools management.

scholarly journals Robust modeling and planning of radio-frequency identification network in logistics under uncertainties

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Bowei Xu ◽  
Junjun Li ◽  
Yongsheng Yang ◽  
Octavian Postolache ◽  
Huafeng Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Cost Efficiency ◽  
Network Planning ◽  
Particle Swarm ◽  
Coverage Rate ◽  
Planning Model ◽  
Swarm Optimization ◽  
Frequency Identification

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.

scholarly journals WallSense: Device-Free Indoor Localization Using Wall-Mounted UHF RFID Tags

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 68 ◽  
Author(s):  
Liang Ma ◽  
Meng Liu ◽  
Hongjun Wang ◽  
Yang Yang ◽  
Na Wang ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Fitness Function ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Swarm Optimization ◽  
Median Error ◽  
Frequency Identification ◽  
Device Free ◽  
Extract Information

To achieve device-free indoor localization without the active participation of the users, this paper presents WallSense, a device-free indoor localization system based on off-the-shelf Radio RFID (Radio-Frequency Identification) equipment. The system deploys two orthogonal tag arrays in adjoining walls and uses the RSSI and phase information measured by RFID readers to localize the target. By differentiating the backscattered signal between adjacent tag pairs, WallSense is able to eliminate most undesirable factors and extract information directly related to the location of the target. By applying Particle Swarm Optimization (PSO) with a novel weighted fitness function and combining the localization result of two orthogonal tag arrays, the system is able to localize the target with high accuracy. Experiments show that the system is able to localize human target with 0.24 m median error. Also, WallSense has low deployment overhead and do not require the users to carry any devices.

scholarly journals Fitness Function Based Particle Swarm Optimization Algorithm for Mobile Adhoc Networks

2018 ◽  
Vol 7 (3.1) ◽  
pp. 31
Author(s):  
Rohan Gupta ◽  
Gurpreet Singh ◽  
Amanpreet Kaur ◽  
Aashdeep Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Network Performance ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Delivery Ratio ◽  
Average Delay ◽  
Swarm Optimization ◽  
Mobile Adhoc Network

Mobile adhoc network is a network which carries out discussion between nodes in the absence of infrastructure. The fitness function based Particle Swarm Optimization Algorithm has been projected for improving the network performance. The effect of changing the number of nodes, communication range and transmission range is investigated on various qualities of service metrics namely packet delivery ratio, throughput and average delay. The investigation has been carried out using NS-2 simulator.  

An Efficient Degradation Particle Swarm Optimization Algorithm for 3D Location in RFID System

2014 ◽  
Vol 1039 ◽  
pp. 544-551
Author(s):  
Jie Lin Li ◽  
Jin Fei Liu ◽  
Wei An Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency Identification ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Rfid Systems ◽  
Acceptable Deviation ◽  
Frequency Identification ◽  
Actual Target ◽  
Production Demand

With the proliferation of radio frequency identification (RFID) systems, existing two dimensional (2-D) location algorithms cannot meet the manufacturing demand anymore. In this paper, an efficient degradation particle swarm optimization (DPSO) algorithm is proposed to solve the three dimensional (3-D) location problems in passive tag RFID systems. Performance evaluation shows this method can approach the actual target tag position with acceptable deviation and stability which can meet the newly generated production demand.

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