Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

Abstract

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 ε. 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 the information-centric Internet of Things applications, where the 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 the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the 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 log 2 (nL/ε) + (n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.INDEX TERMS Approximate range emptiness, data structure, information-centric network, Internet of Things, space lower bound.

Fast RFID Tag Sorting at the Edge for Internet of Things

“…Also, an efficient tag-sorting protocol makes it simple to build a fast-writing process, since every identified tag bears a unique integer, while unidentified tags do not. More practical examples can be found in [3], [4], [6], [8], [21], [26], [29], [32].…”

“…When interrogated by a nearby reader, a tag shall backscatter the stored ID to this reader for showing its existence. A tag may also transmit other information related to the attached object back to the reader if required [3], [4], [21], [24]- [27], [29]- [31]. If the 96-bit or 128-bit ID of a tag t has been collected by a nearby reader (t has been recognized by this reader), t is called as an identified tag (the system knows t's existence); otherwise, t is called an unidentified tag.…”

EPC-Based Efficient Tag Selection in RFID Systems

“…However, since reader R knows the EPCs in S, it can analyze the 4 EPCs in P carefully and find out that t 1 , t 2 , t 3 and t 4 share a common substring ''0 in the first bit of their EPCs, which is not shared by the tags in S − P. Hence, reader R can find out that a single command: Select MemBank = 1, Pointer = 1, Length = 1, Mask = ''0 is capable of picking P from S, and it needs only 1 × (3 + log 2 (96) + 8 + 1) = 19 bits for executing this command. 6 If reader R wants to pick P = {t 1 , t 2 , t 3 , t 7 }, instead of using four Select commands (one for each tag in P), R can issue two Select commands, after it finds out: (1) t 1 and t 2 share a common substring ''00 in the first two bits in their EPCs, which is not shared by other tags in S − P; (2) t 3 and t 7 share a common substring ''10 in the second and third bits in their EPCs, which is not shared by other tags in S − P. Furthermore, we can see that when P = {t 1 , t 2 , t 3 , t 7 }, reader R needs at least two Select commands to pick P from S. This is because these four tags do not share a common substring in their EPCs, which is not shared by the tags in S − P.…”

“…According to Table 6-29, we know that parameter MemBank, Pointer, and Length need 3, log 2 (96) , and 8 bits, respectively. The number of bits needed in Mask is equal to the value of Length, which is the number of bits in the filter-string 6. The filter-string in this command is defined by three parameters: Pointer = 1, Length = 1, Mask=''0 , and this filter-string can separate t 1 , t 2 , t 3 and t 4 from t 5 , t 6 , t 7 , t 8 (t 1 , t 2 , t 3 and t 4 match this filter string, but t 5 , t 6 , t 7 , t 8 do not)…”