多类别RFID系统中缺失标签冰山查询的MAC方法

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本文档标题为"MAC：针对多类别RFID系统的缺失Tag Iceberg查询"，发表于《IEEE Transactions on Vehicular Technology》第六十七卷第十期，2018年10月。RFID技术随着物联网的快速发展，在库存管理、物体追踪和智能物流等领域得到广泛应用。在这些应用中，根据所附着的对象类型，RFID标签可以被分为多个类别。对于多类别RFID系统，一个重要功能是估计每个类别中缺失的标签数量，尤其是当这个数量超过预设阈值时。作者洪龙、郭磊、志波王和吉冑余提出了解决多类别RFID系统中缺失Tag Iceberg查询问题的方法。缺失Tag Iceberg查询的目标是确定一组类别，它们的缺失标签数量超过了一个给定的阈值，并且需要提供一定的可靠性。首先，论文引入了两种基本的缺失Tag Iceberg查询方案，分别是MAC-SZE（Singleton-Zero Estimator based Missing Tag Iceberg Query）和MAC-HZE（Homogeneous Zero Estimator based Missing Tag Iceberg Query）。这两种方法分别采用了单一值估计器和同质零估计器来处理查询任务。 MAC-SZE策略利用单个标签的存在或缺失信息，通过统计分析来识别那些可能存在大量缺失标签的类别。它可能依赖于对单个标签状态的观察，然后推断出整体的缺失情况。而MAC-HZE则假设不同类别间的标签缺失情况具有相似性，通过比较各类别之间的标签密度差异来定位高缺失率的类别。为了实现这两个查询方案，论文可能会探讨概率模型、假设检验、数据挖掘等技术，以及如何在实际环境中有效地执行查询，考虑到RFID读取的实时性和不确定性。此外，文中还可能讨论了查询效率、计算复杂度和准确性之间的权衡，以及如何通过优化算法或调整参数来提升查询性能。这篇研究论文深入探讨了多类别RFID系统中的一个关键挑战，并提供了创新的解决方案，为RFID系统在大规模数据管理和决策支持中提高效率和准确性提供了理论依据和技术支撑。

CHEN et al.: MAC: MISSING TAG ICEBERG QUERIES FOR MULTI-CATEGORY RFID SYSTEMS 9949

TABLE I

YMBOLS USED IN THIS PAPER

that the reader executes a frame with f slots and each tag will

respond only in its replying slot.

Each slot can be classiﬁed into one of the three types: empty

slot which is not selected as the replying slot by any tag, single-

ton slot which is selected as the replying slot by only one tag

and collision slot which is selected as the replying slot simulta-

neously by multiple tags. The singleton and collision slots are

also called non-empty slots. Moreover, the size of each slot may

be different, which is determined by the length of message to

be transmitted between the reader and tag. In a slot, the tag may

need to transmit a 96-bit ID, a multi-bit long response or just

a one-bit short response, then the slot size is denoted as t

tag

and t

according to the EPCglobal C1G2 standard, respec-

tively. Table I lists the symbols with corresponding descriptions

to be used in this paper. In this paper, we adopt t

tag

= 2.4 ms,

= 0.8 ms and t

= 0.4 ms based on the assumption that the

transmission rate between the reader and tags is 62.5 Kbps [5],

[6], [12], [17].

B. Problem Formulation

In this paper, we consider a multi-category large-scale RFID

system which comprises a server, a reader

and a large amount

of tags. The storage and computation capacity of the server is

assumed to be strong enough. The reader and the server are

considered as an integrity (represented as“reader” hereinafter)

since they can be connected via a high data rate communication

link. We assume that there are l categories, which are denoted as

Our proposed missing tag iceberg query schemes can also work in multi-

reader scenario to cover a larger area.

, C

,...,C

. Each tag has a unique ID (96-bit) and belongs

to one of the categories. The tag cardinality of the RFID system

is n, and each category has a tag size of n

(1 ≤ i ≤ l), i.e.,

n =



i=1

. Assume there are m

(1 ≤ i ≤ l) missing tags in

category C

and the missing tag cardinality in the whole RFID

system is m, which equals



i=1

. We only consider the

known tags, whose IDs are known by the reader in advance. Then

the multi-category RFID missing tag iceberg query problem can

be formulated as follows: given a predeﬁned threshold T ,an

error threshold  ∈ (0, 1] and a reliability requirement δ ∈ [0, 1),

a set of categories T will be obtained satisfying the following

two constraints:

∀C

∈T, m

≥ T,Pr[m

≥ (1 − )T ] ≥ δ, (1)

∀C

/∈T, m

<T,Pr[m

≤ (1 + )T ] ≥ δ. (2)

where m

is the estimated missing tag cardinality of C

. Con-

straint (1) means that for each category in T , its estimated miss-

ing tag cardinality should be not less than the threshold T and

the probability that its real missing tag cardinality is larger than

or equal to (1 − )T should be at least δ. While Constraint (2)

means that for each category out of T , its estimated missing tag

cardinality should be less than the threshold T and the proba-

bility that its real missing tag cardinality is less than or equal to

(1 + )T should be at least δ. Note that δ and  are the system

parameters, which are known in advance.

IV. B

ASIC SCHEMES

In this section we ﬁrst propose the MAC-SZE scheme, which

adopts the singleton-zero estimator (SZE) to estimate the miss-

ing tag cardinality for the missing tag iceberg query. After

that we propose the MAC-HZE scheme, which adopts the

homogeneous-zero estimator (HZE). Finally, we analyze the

accuracy of the proposed MAC-SZE and MAC-HZE schemes

to determine the parameters to satisfy the constraints.

A. MAC-SZE Scheme

The MAC-SZE scheme comprises w rounds. In round k,the

reader ﬁrst broadcasts f

 before executing a frame for

each tag to determine its response slot. Note that the reader

will use a uniform frame size among different rounds and f

set as the tag cardinality of the RFID system, i.e., f

= n,to

maximize the efﬁciency [6]. As the reader knows each tag’s ID, it

can pre-compute the responding slot of each tag. Therefore, the

reader can predict the status of each slot, i.e., empty, singleton or

collision in the current frame, which is called as expected frame

as shown in Fig. 1. After that, the reader executes the current

frame and detects whether each slot is empty or not. The reader

compares the slot status difference between the expected frame

and the executed one, based on which it can estimate missing tag

cardinality of each category. The above procedure is conducted

for w rounds, after which the set of categories T satisfying the

constraints Eqs. (1) and (2) can be obtained.

We set X

i,j,k

s→0

= 1ifinthek

round slot j is expected to

be selected only by one tag in C

and is actually an empty slot

in the executed frame, and X

i,j,k

s→0

= 0 otherwise. As shown in

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多类别RFID系统中缺失标签冰山查询的MAC方法

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