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Journal articles on the topic 'Information freshness'

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Author: Grafiati

Published: 10 December 2022

Last updated: 20 February 2023

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1

Sun, Yin, Igor Kadota, Rajat Talak, and Eytan Modiano. "Age of Information: A New Metric for Information Freshness." Synthesis Lectures on Communication Networks 12, no. 2 (December 11, 2019): 1–224. http://dx.doi.org/10.2200/s00954ed2v01y201909cnt023.

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2

Xu, Jin, and Yu Chen. "Information Freshness in Sleep-Wake Server Systems." IEEE Communications Letters 25, no. 7 (July 2021): 2186–90. http://dx.doi.org/10.1109/lcomm.2021.3072071.

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3

Bastopcu, Melih, Baturalp Buyukates, and Sennur Ulukus. "Selective Encoding Policies for Maximizing Information Freshness." IEEE Transactions on Communications 69, no. 9 (September 2021): 5714–26. http://dx.doi.org/10.1109/tcomm.2021.3059871.

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4

Xu, Chao, Howard H. Yang, Xijun Wang, and Tony Q. S. Quek. "Optimizing Information Freshness in Computing-Enabled IoT Networks." IEEE Internet of Things Journal 7, no. 2 (February 2020): 971–85. http://dx.doi.org/10.1109/jiot.2019.2947419.

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5

ArceLopera, C., K. Okajima, Y. Wada, and T. Masuda. "Luminance Information Suffices to Model Vegetable Freshness Perception." Journal of Vision 12, no. 9 (August 10, 2012): 865. http://dx.doi.org/10.1167/12.9.865.

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6

Lee, Y. "Freshness ratio of information: a new metric for age of information." Electronics Letters 56, no. 3 (February 2020): 139–41. http://dx.doi.org/10.1049/el.2019.2020.

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7

Cho, Junghoo, and Hector Garcia-Molina. "Synchronizing a database to improve freshness." ACM SIGMOD Record 29, no. 2 (June 2000): 117–28. http://dx.doi.org/10.1145/335191.335391.

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8

Liu, Pan. "Investment Decisions of Blockchain-Based Anti-Counterfeiting Traceability Services in a High-Quality Fresh Supply Chain of China." Agriculture 12, no. 6 (June 9, 2022): 829. http://dx.doi.org/10.3390/agriculture12060829.

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The application of a blockchain-based anti-counterfeiting traceability system (hereafter, blockchain-based ACTS) presents a positive effect on improving the unreliability of the freshness information. However, using a blockchain-based ACTS requires additional expenditures from chain members. Chain members want to know the investment conditions of a blockchain-based ACTS and how to coordinate their supply chain. To solve these problems, a supply chain with one fresh producer and one retailer was chosen as the study subject. Afterwards, considering the unreliability of the freshness information, the demand function was revised. Then, the profit functions before and after adopting a blockchain-based ACTS were constructed, and then a price discount and revenue-sharing contract was put forward to coordinate the supply chain. Findings: With the growth of the unreliability coefficient of the freshness information, benefits to chain members in the proposed three situations would be reduced. Thus, we can know that after using a blockchain-based ACTS, if chain members want to gain more benefits, they should try their best to excavate the value of the blockchain-based ACTS and reduce the unreliability coefficient of the freshness information.

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9

Chen, Zhengchuan, Mingjun Xu, Min Wang, and Yunjian Jia. "Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions." Entropy 24, no. 2 (January 28, 2022): 200. http://dx.doi.org/10.3390/e24020200.

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Motivated by big data applications in the Internet of Things (IoT), abundant information arrives at the fusion center (FC) waiting to be processed. It is of great significance to ensure data freshness and fidelity simultaneously. We consider a wireless sensor network (WSN) where several sensor nodes observe one metric and then transmit the observations to the FC using a selection combining (SC) scheme. We adopt the age of information (AoI) and minimum mean square error (MMSE) metrics to measure the data freshness and fidelity, respectively. Explicit expressions of average AoI and MMSE are derived. After that, we jointly optimize the two metrics by adjusting the number of sensor nodes. A closed-form sub-optimal number of sensor nodes is proposed to achieve the best freshness and fidelity tradeoff with negligible errors. Numerical results show that using the proposed node number designs can effectively improve the freshness and fidelity of the transmitted data.

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10

Nguyen, Gam D., Sastry Kompella, Clement Kam, and Jeffrey E. Wieselthier. "Information freshness over a Markov channel: The effect of channel state information." Ad Hoc Networks 86 (April 2019): 63–71. http://dx.doi.org/10.1016/j.adhoc.2018.10.010.

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11

Chiariotti, Federico, Josefine Holm, Anders E. Kalor, Beatriz Soret, Soren K. Jensen, Torben B. Pedersen, and Petar Popovski. "Query Age of Information: Freshness in Pull-Based Communication." IEEE Transactions on Communications 70, no. 3 (March 2022): 1606–22. http://dx.doi.org/10.1109/tcomm.2022.3141786.

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12

Ferguson, Mark, and Michael E. Ketzenberg. "Information Sharing to Improve Retail Product Freshness of Perishables." Production and Operations Management 15, no. 1 (March 2006): 57–73. http://dx.doi.org/10.1111/j.1937-5956.2006.tb00003.x.

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13

Sakagami, Hidekazu, Tomonari Kamba, Atsushi Sugiura, and Yoshiyuki Koseki. "Effective personalization of push-type systems — visualizing information freshness." Computer Networks and ISDN Systems 30, no. 1-7 (April 1998): 53–63. http://dx.doi.org/10.1016/s0169-7552(98)00060-9.

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14

SATO, NOBUYOSHI, MINORU UEHARA, and YOSHIFUMI SAKAI. "EVALUATIONS OF FRESHNESS CONSIDERING SCORING ON FRESH INFORMATION RETRIEVAL." Journal of Interconnection Networks 06, no. 03 (September 2005): 265–82. http://dx.doi.org/10.1142/s0219265905001423.

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Fresh information is important for real business. In order to realize fresh information retrieval, we need not only to collect documents in a short time, but also to rank the results in the suitable order. However, conventional ranking methods are not suited for fresh information retrieval because they ignore temporal value of information. So, we have proposed the novel ranking method FTF·IDF for fresh information retrieval. FTF·IDF extends TF·IDF by means of using FTF (Fresh Term Frequency) instead of TF (Term Frequency). FTF differs from TF because FTF decreases as time goes. The speed of decreasing FTF is determined by the dumping factor. The dumping factor is sensitive against small changes of documents. So, we use a threshold to ignore such small changes. In some papers we published, we detect the optimal threshold manually. In this paper, we proposed an adaptive calculating method in order to detect threshold automatically. In this method, the optimal value is determined by iterating to test generated thresholds. In this paper, we describe our method and its evaluation.

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15

Wei, Kefeng, Lincong Zhang, and Shupeng Wang. "Intelligent Channel Allocation for Age of Information Optimization in Internet of Medical Things." Wireless Communications and Mobile Computing 2021 (August 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/6645803.

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Along with the development of realtime applications, the freshness of information becomes significant because the overdue information is worthless and useless and even harmful to the right judgement of system. Therefore, The Age of Information (AoI) used for marking the freshness of information is proposed. In Internet of Medical Things (IoMT), which is derived from the requirement of Internet of Thins (IoT) in medicine, high freshness of medical information should be guaranteed. In this paper, we introduce the AoI of medical information when allocating channels for users in IoMT. Due to the advantages of Deep Q-learning Network (DQN) applied in resource management in wireless network, we propose a novel DQN-based Channel Allocation (DQCA) algorithm to provide the strategy for channel allocation under the optimization of the system cost considering the AoI and energy consumption of coordinator nodes. Unlike the traditional centralized channel allocation methods, the DQCA algorithm is distributed as each user performs the DQN process separately. The simulation results show that our proposed DQCA algorithm is superior to Greedy algorithm and Q-learning algorithm in terms of the average AoI, average energy consumption and system cost.

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16

Yang, Xiao Jing, and Zheng Hu Yan. "Identification of Freshwater Fish Meat Freshness Based on Multi-Sensor Fusion Technology." Applied Mechanics and Materials 303-306 (February 2013): 912–17. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.912.

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In order to explore the application of multi-sensor fusion technology in nondestructive testing of agricultural products and develop new detection methods for agriculture, multi-sensor information fusion technology are performed to identify freshness of freshwater fish meat. Different freshness of grass carp specimens were identified by multi-sensor fusion method which three characteristic data that are PH value, conductance and smell were measured, collected, and then fused by fuzzy theory method. During the experiment process the value of the total volatile basic nitrogen (TVB-N) of standard samples and test samples were measured. The freshness standard was established by the TVB-N value of standard samples. Correctness of the results of multi-sensor data fusion was verified by comparing the TVB-N value of the test samples with the freshness standard. The results show that the freshwater fish meat with different freshness can be identified correctly by multi-sensor data fusion method which is fuzzy theory and the accuracy rate is 94%.

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17

Sambodo, Reo. "Analysis of Consumer Behavior in The Decision-Making Process of Vegetable Purchase During the Covid-19 Pandemic at Bu Dipo’s Vegetable Store." JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian 7, no. 2 (April 30, 2022): 40. http://dx.doi.org/10.37149/jia.v7i2.23832.

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Research to explain the characteristics of vegetable consumers at Bu Dipo’s store, analyze the decision-making process of buying vegetables, and explore the behaviour of vegetable consumers at Bu Dipo’s store. Data analysis used descriptive analysis, Fishbein multi-attribute, and Rank Spearman correlation. The attributes studied include; price, taste, nutritional content, colour, aroma, freshness, packaging, neatness, durability, shape and size, brand, product quality, and expiration information. Vegetables have the advantage of freshness attributes, nutritional content, and expiration information from other features after consuming them. The characteristics of consumers who buy vegetables at BU Dipo’s store are primarily women aged 31-40 years old, working as private employees with an income of IDR3.000.000 - IDR4.000.000 per month, and having two family members to support. In making vegetable purchasing decisions, the freshness factor is an attribute that influences consumers in buying vegetables at Bu Dipo’s store. Characteristics of consumers, namely age, income level, education level, and the number of family members covered, have no significant relationship with consumer behaviour towards purchasing vegetables at Bu Dipo’s store. Researchers can give suggestions to improve the quality of vegetable freshness and open branches to reach more consumers.

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18

Munari, Andrea, and Gianluigi Liva. "Information Freshness Analysis of Slotted ALOHA in Gilbert-Elliot Channels." IEEE Communications Letters 25, no. 9 (September 2021): 2869–73. http://dx.doi.org/10.1109/lcomm.2021.3092193.

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19

Talak, Rajat, Sertac Karaman, and Eytan Modiano. "Optimizing Information Freshness in Wireless Networks Under General Interference Constraints." IEEE/ACM Transactions on Networking 28, no. 1 (February 2020): 15–28. http://dx.doi.org/10.1109/tnet.2019.2946481.

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20

Peralta, Verónika, Raúl Ruggia, and Mokrane Bouzeghoub. "Analyzing and Evaluating Data Freshness in Data Integration Systems." Ingénierie des systèmes d'information 9, no. 5-6 (December 24, 2004): 145–62. http://dx.doi.org/10.3166/isi.9.5-6.145-162.

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21

Lee, Jae Moon, In Hwan Jung, and Kitae Hwang. "Classification of Beef by Using Artificial Intelligence." Webology 19, no. 1 (January 20, 2022): 4639–47. http://dx.doi.org/10.14704/web/v19i1/web19308.

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This paper aims to develop an application that classifies the quality of beef via Artificial Intelligence technology, which has experienced rapid technological growth in recent years. The application will allow users to obtain information including, but not limited to, cuts of beef, freshness, and marbling of the beef they are about to purchase. Deep learning image classification was used to classify the cuts of beef, and OpenCV technology was used to determine the freshness and marbling of the beef. The application was developed in a client-server system for real-time action. The mobile phone of the user (the client) will take a photo of the beef and send it to the server, and the server will analyze the received image to identify and determine the cuts of beef, freshness, and marbling of the beef. The results will then be sent back to the client from the server. Artificial Intelligence technology is used to develop applications with these functions. Image classification technology is used for the classification function of beef parts, and OpenCV's clustering technology is used to determine the freshness and marbling grade of beef. Also, Flask web server is used to apply the client-server structure. The developed system worked well for tenderloin, sirloin, and ribs. It provided high confidence over 75% for these cuts. However, it worked poor for other beef cuts. This is simply a learning problem for image classifiers.

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22

Huh, Sooin, Hye-Jin Kim, Seungah Lee, Jinwoo Cho, Aera Jang, and Joonsung Bae. "Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness." Sensors 21, no. 3 (February 2, 2021): 1001. http://dx.doi.org/10.3390/s21031001.

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This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.

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23

Kavanagh, S., P. B. Lynch, P. J. Caffrey, and W. D. Henry. "An assessment of the effect of feed freshness on intake and performance of suckling and weaned pigs." Proceedings of the British Society of Animal Science 1996 (March 1996): 137. http://dx.doi.org/10.1017/s1752756200593338.

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While there is little information in the literature on the effect of feed freshness on acceptability, feeds containing oxidised or rancid fats are thought to be unpalatable. Both free fatty acid level and peroxide level have been used as measures of fat quality and suitability for feeding especially to young animals. Neither is now considered a reliable predictor of animal performance (Carpenter, 1968). Connolly et al. (1970) found that pig performance was unaffected by high levels of peroxide in the diet or prolonged storage and concluded that it is doubtful if toxicity from lipid peroxides would arise in practical feeding. The aim of this study was the examine the effect of feed freshness on intake and growth of suckling and weaned pigs, using free fatty acid and peroxide levels as indicators of freshness.

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24

Kavanagh, S., P. B. Lynch, P. J. Caffrey, and W. D. Henry. "An assessment of the effect of feed freshness on intake and performance of suckling and weaned pigs." Proceedings of the British Society of Animal Science 1996 (March 1996): 137. http://dx.doi.org/10.1017/s0308229600031044.

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While there is little information in the literature on the effect of feed freshness on acceptability, feeds containing oxidised or rancid fats are thought to be unpalatable. Both free fatty acid level and peroxide level have been used as measures of fat quality and suitability for feeding especially to young animals. Neither is now considered a reliable predictor of animal performance (Carpenter, 1968). Connolly et al. (1970) found that pig performance was unaffected by high levels of peroxide in the diet or prolonged storage and concluded that it is doubtful if toxicity from lipid peroxides would arise in practical feeding. The aim of this study was the examine the effect of feed freshness on intake and growth of suckling and weaned pigs, using free fatty acid and peroxide levels as indicators of freshness.

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25

Migu´elez, Fernando, Josu Doncel, and Urtzi Ayesta. "A Resource Sharing Game for the Freshness of Status Updates." ACM SIGMETRICS Performance Evaluation Review 49, no. 2 (January 17, 2022): 15–17. http://dx.doi.org/10.1145/3512798.3512805.

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Timely information is a crucial factor in a wide range of information, communication, and control systems. For instance, in autonomous driving systems, the state of the traffic and the location of the vehicles must be as recent as possible. The Age of Information is a relatively new metric that measures the freshness of the knowledge we have about the status of a remote system. More specifically, the Age of Information is the time elapsed since the generation of the last successfully received packet by the monitor containing information about the source. Since the seminal paper [2], in several models it has been observed that the policies that optimize performance metrics of interest in queueing theory do not necessarily minimize the Age of Information. Hence, there is a large number of queueing models that are open research problems regarding the Age of Information metric. We refer to [6] for a recent survey of the Age of Information.

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26

Chang, Zhi Yong, Dong Hui Chen, Zhi Hong Zhang, Yue Ying Tong, Jin Tong, and Lu Dai. "Design of a Bionic Olfactory, Tactile Integrated System and its Application in Chicken Meat Quality Inspection." Applied Mechanics and Materials 461 (November 2013): 814–21. http://dx.doi.org/10.4028/www.scientific.net/amm.461.814.

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This study is aiming at the practical problem of meat freshness evaluation. Since meat putrefaction is a complex process that is influenced by many factors, it is necessary to have a comprehensive investigation of the various indicators to determine the freshness of meat. This research integrated information from a multisensory system to reduce uncertainty of evaluation. According to the odor mechanism model of rotten chicken, six types of sensors were chosen, which were combined as array for olfactory experiments. WDW-20 electronic universal testing machine (UTM) was adopted as tactile sensing device. As a bionic tactile test part, the UTM head is to obtain pressure characteristic curves of the meat. According to the odor model and elastic mechanics parameters of the chicken, the mechanical parameters were analyzed under the condition of cold storage, as well as time-varying results of fingerprint odor signal and salt base nitrogen volatile signal. Then, established the meat odor, elastic mechanics and freshness parameters, which were integrated into a fusion system and combined with the data through the experimental test. Eventually, established the mathematical model among meat odor, elastic mechanics parameters and meat freshness. This study provides theory reference for the evaluation of meat freshness, and delivers new thought and method for the design of multiphase bionic intelligent electrical measuring equipment.

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27

Ma, Xiao, Ao Zhou, Qibo Sun, and Shangguang Wang. "Freshness-Aware Information Update and Computation Offloading in Mobile-Edge Computing." IEEE Internet of Things Journal 8, no. 16 (August 15, 2021): 13115–25. http://dx.doi.org/10.1109/jiot.2021.3082281.

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28

Lee, Sungho, Minsu Kim, Jemin Lee, Ruei-Hau Hsu, and Tony Q. S. Quek. "Is Blockchain Suitable for Data Freshness? An Age-of-Information Perspective." IEEE Network 35, no. 2 (March 2021): 96–103. http://dx.doi.org/10.1109/mnet.011.2000044.

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29

Mohammed, Saber F., Feng Ke, and Yingloong Lee. "Information Freshness for Vehicles Communications of Safety-critical Vulnerable Road Users." Journal of Physics: Conference Series 1933, no. 1 (June 1, 2021): 012057. http://dx.doi.org/10.1088/1742-6596/1933/1/012057.

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30

Chen, Zheng, Nikolaos Pappas, Emil Bjornson, and Erik G. Larsson. "Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices." IEEE Open Journal of the Communications Society 2 (2021): 456–70. http://dx.doi.org/10.1109/ojcoms.2021.3062678.

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31

Yoshizawa, T., A. Yoshiga, and H. Nakagawa. "Chromatic and spatial information in visual estimation of freshness of vegetables." Journal of Vision 12, no. 14 (December 27, 2012): 56. http://dx.doi.org/10.1167/12.14.56.

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32

Sarkar, M., N. Gupta, and M. Assaad. "Monitoring of fruit freshness using phase information in polarization reflectance spectroscopy." Applied Optics 58, no. 23 (August 8, 2019): 6396. http://dx.doi.org/10.1364/ao.58.006396.

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33

Lopera-Valle, Adrian, and Anastasia Elias. "Colorimetric indicators for volatile amines based on succinic anhydride (SAh)-grafted poly (lactic acid) (PLA)." Analytical Methods 12, no. 19 (2020): 2499–508. http://dx.doi.org/10.1039/d0ay00550a.

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34

Li, Wen-Syan, Oliver Po, Wang-Pin Hsiung, K. Selçuk Candan, and Divyakant Agrawal. "Freshness-driven adaptive caching for dynamic content Web sites." Data & Knowledge Engineering 47, no. 2 (November 2003): 269–96. http://dx.doi.org/10.1016/s0169-023x(03)00093-4.

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35

Peng, Yingsheng, Yong Liu, Dong Li, and Han Zhang. "Deep Reinforcement Learning Based Freshness-Aware Path Planning for UAV-Assisted Edge Computing Networks with Device Mobility." Remote Sensing 14, no. 16 (August 18, 2022): 4016. http://dx.doi.org/10.3390/rs14164016.

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As unmanned aerial vehicles (UAVs) can provide flexible and efficient services concerning the sparse network distribution, we study a UAV-assisted mobile edge computing (MEC) network. To satisfy the freshness requirement of IoT applications, the age of information (AoI) is incorporated as an important performance metric. Then, the path planning problem is formulated to simultaneously minimize the AoIs of mobile devices and the energy consumption of the UAV, where the movement randomness of IoT devices are taken into account. Concerning the dimension explosion, the deep reinforcement learning (DRL) framework is exploited, and a double deep Q-learning network (DDQN) algorithm is proposed to realize the intelligent and freshness-aware path planning of the UAV. Extensive simulation results validate the effectiveness of the proposed freshness-aware path planning scheme and unveil the effects of the moving speed of devices and the UAV on the achieved AoI.

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36

Zhang, Tianci, Shutong Chen, and Zhengchuan Chen. "Internet of Things: The Optimal Generation Rates under Preemption Strategy in a Multi-Source Queuing System." Entropy 23, no. 8 (August 16, 2021): 1055. http://dx.doi.org/10.3390/e23081055.

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With the rapid development and wide application of the Internet of Things (IoT), how to provide timely and fresh information for strategic analysis and decision-making has become a key issue. Recent studies have shown that preemption strategies are of great importance to the improvement of information freshness. In view of this, we focus on the multi-source preemptive queuing model and investigate how to control the generation rate of each source to achieve the optimal overall information freshness. Specifically, we consider two typical preemption strategies: self-preemption strategy and global-preemption strategy. Noting that the urgency requirements of the systems on the data of each source are different, we propose the weighted average age of information (AoI) to characterize the overall information freshness of the system. For the self-preemption strategy, we prove that the optimal generation rate allocation is a convex problem and present an efficient algorithm to find the optimal solution. Additionally, we also derive a closed-form approximate optimal solution under light load cases to meet the demands for rapid deployment. For the global-preemption strategy, we directly derive the closed-form optimal solution of the corresponding problem. By comparing the optimized weighted average AoIs, the performance achieved by the global-preemption system was better than that achieved by the self-preemption system in terms of the overall timeliness. The numerical analysis verified the correctness of the theoretical analysis and that the proposed approximate solution had high accuracy not only under light load cases but also under other cases.

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37

Li, Xinmin, Jiahui Li, and Dandan Liu. "Energy-Efficient UAV Trajectory Design with Information Freshness Constraint via Deep Reinforcement Learning." Mobile Information Systems 2021 (December 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/1430512.

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Unmanned aerial vehicle (UAV) technique with flexible deployment has enabled the development of Internet of Things (IoT) applications. However, it is difficult to guarantee the freshness of information delivery for the energy-limited UAV. Thus, we study the trajectory design in the multiple-UAV communication system, in which the massive ground devices send the individual information to mobile UAV base stations under the demand of information freshness. First, an energy-efficiency (EE) maximization optimization problem is formulated under the rest energy, safety distance, and age of information (AoI) constraints. However, it is difficult to solve the optimization problem due to the nonconvex objective function and unknown dynamic environment. Second, a trajectory design based on the deep Q-network method is proposed, in which the state space considering energy efficiency, rest energy, and AoI and the efficient reward function related with EE performance are constructed, respectively. Furthermore, to avoid the dependency of training data for the neural network, the experience replay and random sampling for batch are adopted. Finally, we validate the system performance of the proposed scheme. Simulation results show that the proposed scheme can achieve a better EE performance compared with the benchmark scheme.

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38

Wang, Zijing, Mihai-Alin Badiu, and Justin P. Coon. "Relationship between Age and Value of Information for a Noisy Ornstein–Uhlenbeck Process." Entropy 23, no. 8 (July 23, 2021): 940. http://dx.doi.org/10.3390/e23080940.

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The age of information (AoI) has been widely used to quantify the information freshness in real-time status update systems. As the AoI is independent of the inherent property of the source data and the context, we introduce a mutual information-based value of information (VoI) framework for hidden Markov models. In this paper, we investigate the VoI and its relationship to the AoI for a noisy Ornstein–Uhlenbeck (OU) process. We explore the effects of correlation and noise on their relationship, and find logarithmic, exponential and linear dependencies between the two in three different regimes. This gives the formal justification for the selection of non-linear AoI functions previously reported in other works. Moreover, we study the statistical properties of the VoI in the example of a queue model, deriving its distribution functions and moments. The lower and upper bounds of the average VoI are also analysed, which can be used for the design and optimisation of freshness-aware networks. Numerical results are presented and further show that, compared with the traditional linear age and some basic non-linear age functions, the proposed VoI framework is more general and suitable for various contexts.

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39

Peng, Fuzhou, Xiang Chen, and Xijun Wang. "Channel Quality-Based Optimal Status Update for Information Freshness in Internet of Things." Entropy 23, no. 7 (July 18, 2021): 912. http://dx.doi.org/10.3390/e23070912.

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This paper investigates the status updating policy for information freshness in Internet of things (IoT) systems, where the channel quality is fed back to the sensor at the beginning of each time slot. Based on the channel quality, we aim to strike a balance between the information freshness and the update cost by minimizing the weighted sum of the age of information (AoI) and the energy consumption. The optimal status updating problem is formulated as a Markov decision process (MDP), and the structure of the optimal updating policy is investigated. We prove that, given the channel quality, the optimal policy is of a threshold type with respect to the AoI. In particular, the sensor remains idle when the AoI is smaller than the threshold, while the sensor transmits the update packet when the AoI is greater than the threshold. Moreover, the threshold is proven to be a non-increasing function of channel state. A numerical-based algorithm for efficiently computing the optimal thresholds is proposed for a special case where the channel is quantized into two states. Simulation results show that our proposed policy performs better than two baseline policies.

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Zuo, Xiang Lin, Wen Bo Wang, Ying Wang, and Wan Li Zuo. "Research and Implementation of Improved Real-Time Crawler Modeling." Applied Mechanics and Materials 312 (February 2013): 791–95. http://dx.doi.org/10.4028/www.scientific.net/amm.312.791.

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The past decade has witnessed the rapid development of search engines, which has become an indispensable part of everyday life. However, people are no longer satisfied with accessing to ordinary information, and they may instead pay more attention to fresh information. This demand poses challenges to traditional search engines, which concern more about relevance and importance of web pages. A search engine compresses three modules: crawler, indexer and searcher. Changes are needed for all these three parts to improve search engine's freshness. This paper investigates the first part of search engine crawler, we analyze the requirements for real-time crawler, and propose a novel real-time crawler based on more accurate estimation of refresh time. Experimental results demonstrate that the proposed real-time crawler can help search engine improve its freshness.

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Mallouchos, Athanasios, Theano Mikrou, and Chrysavgi Gardeli. "Gas Chromatography–Mass Spectrometry-Based Metabolite Profiling for the Assessment of Freshness in Gilthead Sea Bream (Sparus aurata)." Foods 9, no. 4 (April 9, 2020): 464. http://dx.doi.org/10.3390/foods9040464.

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Gilthead sea bream (Sparus aurata) is one of the most important farmed Mediterranean fish species, and there is considerable interest for the development of suitable methods to assess its freshness. In the present work, gas chromatography–mass spectrometry-based metabolomics was employed to monitor the hydrophilic metabolites of sea bream during storage on ice for 19 days. Additionally, the quality changes were evaluated using two conventional methods: sensory evaluation according to European Union’s grading scheme and K-value, the most widely used chemical index of fish spoilage. With the application of chemometrics, the fish samples were successfully classified in the freshness categories, and a partial least squares regression model was built to predict K-value. A list of differential metabolites were found, which were distinguished according to their evolution profile as potential biomarkers of freshness and spoilage. Therefore, the results support the suitability of the proposed methodology to gain information on seafood quality.

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Chi, Yunyang, Liping Luo, Xueyong Huang, Meng Cui, Ximo Dai, Yingbin Hao, Xiali Guo, and Huolin Luo. "Rapid Determination of the Freshness of Lotus Seeds Using Surface Desorption Atmospheric Pressure Chemical Ionization-Mass Spectrometry with Multivariate Analyses." Journal of Food Quality 2019 (August 18, 2019): 1–7. http://dx.doi.org/10.1155/2019/1628796.

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In order to explore a new method to detect the freshness of lotus seeds, the lotus seeds stored for 0, 1, 2, and 3 years, respectively, were used as experimental materials and analyzed by DAPCI-MS (desorption atmospheric pressure chemical ionization-mass spectrometry). The obtained data were processed by principal component analysis (PCA) and backpropagation artificial neural networks (BP-ANNs). The result showed that DAPCI-MS could obtain abundant chemical material information from the slice surface of lotus seeds. The BP-ANNs model could be applied not only to distinguish fresh and aged lotus seeds with the testing set accuracies of 95.0% and 91.7%, respectively, but also to classify lotus seeds with different storage times with the testing set accuracies of 90.0%, 85.0%, 85.0%, and 90.0%, respectively. The paper developed a fast, convenient, and accurate method for the freshness detection of lotus seed and would provide reliable reference value for rapid authentication of food freshness by the rapid mass spectrometry technique.

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Chen, Po-Yu. "Economic Order Quality Model for Determining the Sales Prices of Fresh Goods at Various Points in Time." Journal of Food Quality 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6967501.

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Although the safe consumption of goods such as food products, medicine, and vaccines is related to their freshness, consumers frequently understand less than suppliers about the freshness of goods when they purchase them. Because of this lack of information, apart from sales prices, consumers refer only to the manufacturing and expiration dates when deciding whether to purchase and how many of these goods to buy. If dealers could determine the sales price at each point in time and customers’ intention to buy goods of varying freshness, then dealers could set an optimal inventory cycle and allocate a weekly sales price for each point in time, thereby maximizing the profit per unit time. Therefore, in this study, an economic order quality model was established to enable discussion of the optimal control of sales prices. The technique for identifying the optimal solution for the model was determined, the characteristics of the optimal solution were demonstrated, and the implications of the solution’s sensitivity analysis were explained.

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Li, Bin, and Jia Liu. "Achieving Information Freshness With Selfish and Rational Users in Mobile Crowd-Learning." IEEE Journal on Selected Areas in Communications 39, no. 5 (May 2021): 1266–76. http://dx.doi.org/10.1109/jsac.2021.3065092.

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Chen, Xianfu, Celimuge Wu, Tao Chen, Zhi Liu, Honggang Zhang, Mehdi Bennis, Hang Liu, and Yusheng Ji. "Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems." IEEE Journal on Selected Areas in Communications 40, no. 1 (January 2022): 243–58. http://dx.doi.org/10.1109/jsac.2021.3126075.

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Herbon, Avi. "Dynamic pricing vs. acquiring information on consumers’ heterogeneous sensitivity to product freshness." International Journal of Production Research 52, no. 3 (October 15, 2013): 918–33. http://dx.doi.org/10.1080/00207543.2013.843800.

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Feng, Huanhuan, Mengjie Zhang, Pengfei Liu, Yiliu Liu, and Xiaoshuan Zhang. "Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage." Foods 9, no. 11 (October 30, 2020): 1579. http://dx.doi.org/10.3390/foods9111579.

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Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.

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Lewandowski, Dirk. "A three-year study on the freshness of web search engine databases." Journal of Information Science 34, no. 6 (May 8, 2008): 817–31. http://dx.doi.org/10.1177/0165551508089396.

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Cardoso, Patrícia G., Odete Gonçalves, Maria F. Carvalho, Rodrigo Ozório, and Paulo Vaz-Pires. "Seasonal Evaluation of Freshness Profile of Commercially Important Fish Species." Foods 10, no. 7 (July 6, 2021): 1567. http://dx.doi.org/10.3390/foods10071567.

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Freshness is considered one of the most important parameters to judge the quality of most fish products. In the current study, the seasonality effect on the freshness profile of different economic fish species was evaluated for the first time, using three different approaches (sensory: Quality Index Method (QIM) and European (EC) Scheme; physical: Torrymeter (TRM) values; and microbiological analyses: Total Viable Counts (TVC) and degradative bacteria). Over a year, individuals of farmed fish Sparus aurata and Dicentrarchus labrax, as well as the wild fish Trachurus trachurus, Scomber colias, and Sardina pilchardus, were sampled seasonally for the evaluation of their freshness profile over 10 days on ice. In general, data showed an increase in QIM values, a decline in TRM, and an increase of spoilage bacteria throughout the storage time, revealing a clear temporal degradation of the quality of the fish. Additionally, some signs of seasonality effect could only be observed for some species. For example, the seabass D. labrax showed lower numbers of degradative bacteria in winter than in the other seasons, suggesting a high potential to be marketed in a fresher condition, especially during that season. On the other hand, S. colias showed higher freshness scores (i.e., higher TRM values in spring and autumn and lower numbers of bacteria in summer) from spring to autumn. However, from the five studied species, S. colias presented the lowest freshness values, indicating a higher fragility of this species. This information is extremely relevant for consumers and retailers that want to invest in higher quality products, as they would thus be able to choose certain species in detriment of others. Additionally, obtained data showed that farmed species reached day 10 of storage time with lower values of QIM and microbial counts (cfu), as well as higher values of TRM, in relation to wild species. These results reinforce the idea that farmed fish can, under proper conditions, present high quality/freshness profile.

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Sun, Yin, and Benjamin Cyr. "Sampling for data freshness optimization: Non-linear age functions." Journal of Communications and Networks 21, no. 3 (June 2019): 204–19. http://dx.doi.org/10.1109/jcn.2019.000035.

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