Radio Frequency IDentification (RFID) systems are becoming more and more popular in the field of ubiquitous computing, in particular for objects identification. An RFID system is composed by one or more readers and a number of tags. One of the main issues in an RFID network is the fast and reliable identification of all tags in the reader range. The reader issues some queries, and tags properly answer. Then, the reader must identify the tags from such answers. This is crucial for most applications. Since the transmission medium is shared, the typical problem to be faced is a MAC-like one, i.e. to avoid or limit the number of tags transmission collisions. We propose a protocol which, under some assumptions about transmission techniques, always achieves a 100 perfomance. It is based on a proper recursive splitting of the concurrent tags sets, until all tags have been identified. The other approaches present in literature have performances of about 42 in the average at most. The counterpart is a more sophisticated hardware to be deployed in the manufacture of low cost tags.
All the protocols proposed so far exhibit an average performance (both in terms of messages and in terms of transmitted bits, two metrics strictly related to system efficiency) well below 50 , the best performing protocols, namely QT and TSA, perform around 40 @cite_6 @cite_27 . Such results are reported in the above papers, where the protocols have been proposed, and were substantially confirmed in a paper @cite_28 describing an extensive simulation experiment set up for assessing the average performance of several tag identification protocols.
@cite_28 @cite_27 @cite_6
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There are several analytical results on distributed hash tables (DHTs) that can tolerate Byzantine faults. Unfortunately, in such systems, operations such as data retrieval and message sending incur significant communication costs. For example, a simple scheme used in many Byzantine fault-tolerant DHT constructions of @math nodes requires @math messages, this is likely impractical for real-world applications. The previous best known message complexity is @math in expectation , however, the corresponding protocol suffers from prohibitive costs owing to hidden constants in the asymptotic notation and setup costs. In this paper, we focus on reducing the communication costs against a computationally bounded adversary. We employ threshold cryptography and distributed key generation to define two protocols both of which are more efficient than existing solutions. In comparison, our first protocol is deterministic with @math message complexity and our second protocol is randomized with expected @math message complexity. Further, both the hidden constants and setup costs for our protocols are small and no trusted third party is required. Finally, we present results from micro benchmarks conducted over PlanetLab showing that our protocols are practical for deployment under significant levels of churn and adversarial behaviour. Shor and Preskill (see Phys. Rev. Lett., vol.85, p.441, 2000) have provided a simple proof of security of the standard quantum key distribution scheme by Bennett and Brassard (1984) by demonstrating a connection between key distribution and entanglement purification protocols (EPPs) with one-way communications. Here, we provide proofs of security of standard quantum key distribution schemes, Bennett and Brassard and the six-state scheme, against the most general attack, by using the techniques of two-way entanglement purification. We demonstrate clearly the advantage of classical post-processing with two-way classical communications over classical post-processing with only one-way classical communications in quantum key distribution (QKD). This is done by the explicit construction of a new protocol for (the error correction detection and privacy amplification of) Bennett and Brassard that can tolerate a bit error rate of up to 18.9 , which is higher than what any Bennett and Brassard scheme with only one-way classical communications can possibly tolerate. Moreover, we demonstrate the advantage of the six-state scheme over Bennett and Brassard by showing that the six-state scheme can strictly tolerate a higher bit error rate than Bennett and Brassard. In particular, our six-state protocol can tolerate a bit error rate of 26.4 , which is higher than the upper bound of 25 bit error rate for any secure Bennett and Brassard protocol. Consequently, our protocols may allow higher key generation rate and remain secure over longer distances than previous protocols. Our investigation suggests that two-way entanglement purification is a useful tool in the study of advantage distillation, error correction, and privacy amplification protocols. A low-complexity message-passing algorithm, called Split-Row Threshold, is used to implement low-density parity-check (LDPC) decoders with reduced layout routing congestion. Five LDPC decoders that are compatible with the 10GBASE-T standard are implemented using MinSum Normalized and MinSum Split-Row Threshold algorithms. All decoders are built using a standard cell design flow and include all steps through the generation of GDS II layout. An Spn = 16 decoder achieves improvements in area, throughput, and energy efficiency of 4.1 times, 3.3 times, and 4.8 times, respectively, compared to a MinSum Normalized implementation. Postlayout results show that a fully parallel Spn = 16 decoder in 65-nm CMOS operates at 195 MHz at 1.3 V with an average throughput of 92.8 Gbits s with early termination enabled. Low-power operation at 0.7 V gives a worst case throughput of 6.5 Gbits s-just above the 10GBASE-T requirement-and an estimated average power of 62 mW, resulting in 9.5 pj bit. At 0.7 V with early termination enabled, the throughput is 16.6 Gbits s, and the energy is 3.7 pJ bit, which is 5.8× lower than the previously reported lowest energy per bit. The decoder area is 4.84 mm2 with a final postlayout area utilization of 97 . We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.