Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged. However, due to the multi-dimensionality and randomness of STK transactions, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this article takes the first step to propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

The rapid development of the blockchain leads to a blowout of on-chain transactions, contracts, and currencies, which will further accelerate the increase of data. The existing blockchain systems typically support exact transaction queries, which, however, cannot satisfy the QoS requirements with intelligent adjustment in the blockchain systems. To this end, this paper takes the first step to define and address the practically important problem of transaction cardinality estimation for QoS-adjustable intelligent blockchain systems. We first establish a mathematical relationship between the bit string and transaction cardinality. Thus, we can leverage the number of leading 1s of the obtained bit string to estimate the transaction cardinality. We then improve the block header and body with a corresponding search algorithm to access bit strings in blocks. We also propose an estimation protocol with intelligent adjustable QoS to support accuracy-guaranteed and efficiency-optimized estimation. Finally, we design an authentication scheme and guarantee the reliability of our protocol through rigorous theoretical derivation. When achieving the transaction cardinality estimation in blockchain, two technical challenges need to be addressed. (i) To ensure efficient, verifiable, and overhead-saving bit string accessing mechanism in blockchain, we propose the Merkle Cardinality Tree (MCT) and target block filtering mechanism based on Bloom Filter (BF) in off-chain and improve on-chain block header by joining the abstract of MCT and BF. (ii) To improve estimation efficiency while guaranteeing accuracy requirements in hybrid blockchain scheme, we propose a Dynamic One-round Sampling-based cardinality Estimation (DOSE) protocol and integrate BF-DOSE to intelligently accelerate estimation. We build MCT in Ethereum and store the MCT Root in the block header for estimation authentication. Extensive experiments reveal that our BF-DOSE protocol can well satisfy various accuracy and efficiency requirements of QoS-adjustable intelligent blockchain systems, and is one to two orders of magnitude faster compared with benchmark schemes.

e-Auction improves the efficiency of bid transaction. However, the protection of bidders’ privacy, transaction fairness and verifiability, transaction data security, high cost of third-party auction center, and other issues have attracted more attention. According to the transaction process and basic principles of the sealed auction, we explored the problems existing in the current sealed-bid e-auction schemes. Based on the blockchain technology, we proposed a sealed-bid e-auction scheme with smart contract technology, Bulletproofs zero-knowledge proof protocols, and Pedersen commitment algorithm. The proposed scheme constructed an auction mechanism without the third-party auctioneer so as to restrict the behaviors of auction parties for the sake of auction security, reliability, fairness, and privacy protection. Compared with the related sealed e-auction schemes based on blockchain technologies in six metrics, we conducted the experiment to show that the proposed scheme protected the bid information from leakage well and successfully verified the winning bid price and the related bidder by all transaction participants without the third-party auctioneer.

Tag inventory is one of the most fundamental tasks for RFID systems. However, the Framed Slotted Aloha (FSA) protocol specified in the C1G2 standard is of low time-efficiency, because it needs to collect all tags in the system. To improve time-efficiency, research communities proposed a batch of sampling-based approaches, in which the reader only needs to collect a small set of sampled tags instead of all. Although time-efficiency has been improved, existing sampling-based approaches still have two common limitations. First, all tags in the system are assumed to have the same sampling probability. It is unfair that tags attached to differential items (e.g., different values) have the same chance to be sampled and collected. Second, all existing sampling-based approaches stay in theory level and cannot be deployed on Commercial Off-The-Shelf (COTS) RFID devices, because the C1G2 standard does not support the sampling function at all. To deal with the above two limitations, this paper studies the new problem of differential tag sampling-letting each RFID tag be identified with a given sampling probability. In this paper, we use the COTS RFID devices including Impinj Speedway R420 reader and Monza 4QT tags to implement the Differential Tag Sampling (DTS) operation. Then, we apply probabilistic analytics on the collected tag data to address some practically important problems such as Multi-category Tag Cardinality Estimation (MTCE), and Value-based Missing Tag Detection (VMTD). Although the analytics results are not 100 percent accurate, the deviation in the results can be controlled below a small threshold and DTS can significantly improve the time-efficiency. DTS can be easily deployed on the COTS RFID systems, because it is totally compliant with the C1G2 standard. Extensive experiments demonstrate that DTS is able to let each tag take the given sampling probability to be sampled and identified. Moreover, the proposed DTS protocol can significantly reduce the execution time of MTCE and VMTD by nearly 70 percent than the FSA protocol.

To solve the spectrum and power allocation problem of distributed cognitive radio networks for D2D users, we propose a joint spectrum and power allocation algorithm based on potential game. Through all the D2D nodes uploading their own strategy selections, the model enables distributed nodes to make decisions using limited information, and establishes a resource allocation model that maximizes the overall benefit. The utility function takes the throughput as the gain, and the total mutual interference as the cost. Cognitive users detect the channel condition according to the interference criterion of SINR constraint, in order to guarantee that the node can select the strategy which meets the user's requirements and achieves Nash equilibrium quickly. In this paper, the convergence of the algorithm is proved theoretically. Simulation results show that the algorithm achieves the goal of joint allocation of spectrum and power with a fast convergence speed, and demonstrate superior throughput and fair allocation of resources in comparison with other non game-theoretic methods.

Summary In wireless sensor networks, a lot of applications need the sensed information be transmitted to the sink node within a predefined time threshold. So end‐to‐end delay is an important performance metric in wireless sensor networks. Opportunistic routing protocols have been proposed to reduce the waiting delay. In the duty cycle networks, increasing the duty cycle ratio can also reduce the end‐to‐end delay. However, this method will consume more energy. It is obvious that there exists a trade‐off between delay and energy consumption. So adjusting the duty cycle ratio of each node can investigate this trade‐off. To the best of our knowledge, no existing work takes both of end‐to‐end delay and energy efficiency into consideration in the opportunistic routing networks. In this paper, we want to minimize the whole energy consumption while guaranteeing the expected end‐to‐end delay between the source nodes and the sink node is below the given threshold. To deal with this problem, we propose a dynamic duty cycle scheme which can significantly reduce the energy consumption and guarantee the expected end‐to‐end delay demand in the opportunistic routing network. To be specific, firstly, we formulate a new metric with the wake‐up time slots as the variable to measure the end‐to‐end delay. Secondly, for simplifying the complex problem, we decompose it into a set of single‐hop delay guarantee problems. Feedback controller has been used to solve the problem. We also analyze the influence of the multiple receivers in the same forwarding set. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithm. The experimental results reveal that our scheme can guarantee the delay requirement, meanwhile, significantly reduce the energy consumption compared with prior schemes.

This paper studies the practically important problem of top-k queries, which is to find the top k largest categories and their corresponding sizes. In this paper, we propose a Top-k Query (TKQ) protocol and a technique that we call Segmented Perfect Hashing (SPH) for optimizing TKQ. Specifically, TKQ is based on the framed slotted Aloha protocol. Each tag responds to the reader with a Single-One Geometric (SOG) string using the ON-OFF Keying modulation. TKQ leverages the length of continuous leading 1s in the combined signal to estimate the corresponding category size. TKQ can quickly eliminate the sufficiently small categories, and only needs to focus on a limited number of large-size categories that require more accurate estimation. We conduct rigorous analysis to guarantee the predefined accuracy constraints. To further improve time-efficiency, we propose the SPH scheme, which improves the average frame utilization of TKQ from 36.8% to nearly 100% by establishing a bijective mapping between tag categories and slots. To minimize the overall time cost, we optimize the key parameter that trades off between communication cost and computation cost. Experimental results show that our TKQ+SPH protocol not only achieves the required accuracy constraints, but also achieves a 2.6~7x faster speed than the existing protocols.

This paper studies the problem of data collection in sensor-augmented RFID networks: how to quickly obtain the error-bounded data from sensor-augmented RFID tags. Existing data collection protocols require each tag to transmit the sensor data to the reader through a low-rate channel. However, in large-scale RFID system, they take too long time and block other time-sensitive operations. By exploring the correlation of sensor data, our Sampling-based Information Collection (SIC) protocol significantly reduces the number of responding tags. Specifically, SIC obtains an error bound based on the estimation model by using some randomly-sampled data. The error bound is expected to maximize the number of data within it. These data can be seen as a cluster and be approximated by one value within the error bound. Then, SIC only needs to collect the data of out this cluster, thereby significantly reducing the data transmission. It minimizes the execution time by optimizing the sample size and estimating the number of tags out of the error bound. We conduct extensive simulations to evaluate the performance of SIC and compare it with three major related work. The results demonstrate that SIC is 1 to 10 times faster than the state-of-the-art solution.

In Wireless Sensor Networks (WSNs), end-to-end delay is an important metric because the sensed information is necessary to be transmitted to the sink node within a predefined time threshold. Therefore, opportunistic routing protocols are proposed to reduce the end-to-end delay. As a matter of fact, increasing the number of wake-up slots will certainly reduce the transmission delay, however, also consumes more energy. Hence, it is interesting to control the number of wake-up slots to investigate the trade-off between the end-to-end delay and the energy-efficiency. To the best of our knowledge, no existing work takes both of end-to-end delay and energy-efficiency into consideration in the opportunistic routing networks. Therefore, this paper studies how to minimize the energy-consumption while guaranteeing that the expected end-to-end delay is below a given threshold. To solve this problem, we propose an energy-based Dynamic Duty Cycle Scheme(D2CS) in opportunistic routing network. Specifically, we first present an analytical model to measure the expected end-to-end delay. Then, we decompose the studied problem into a set of single-hop delay guarantee problems and using the feedback controller to approximate the optimal solution. Finally, extensive simulations are conducted to evaluate the performance of the proposed D2CS algorithm. The experimental results reveal that our D2CS can guarantee the delay requirement, meanwhile, significantly reduce the energy consumption compared with prior schemes.

Unknown RFID tags appear when the unread tagged objects are moved in or tagged objects are misplaced. This paper studies the practically important problem of unknown tag detection while taking both time-efficiency and energy-efficiency of battery-powered active tags into consideration. We first propose a Sampling Bloom Filter which generalizes the standard Bloom Filter. Using the new filtering technique, we propose the Sampling Bloom Filter-based Unknown tag Detection Protocol (SBF-UDP), whose detection accuracy is tunable by the end users. We present the theoretical analysis to minimize the time and energy costs. SBF-UDP can be tuned to either the time-saving mode or the energy-saving mode, according to the specific requirements. Extensive simulations are conducted to evaluate the performance of the proposed protocol. The experimental results show that SBF-UDP considerably outperforms the previous related protocols in terms of both time-efficiency and energy-efficiency. For example, when 3 or more unknown tags appear in the RFID system with 30000 known tags, the proposed SBF-UDP is able to successfully report the existence of unknown tags with a confidence more than 99%. While our protocol runs 9 times faster than the fastest existing scheme and reducing the energy consumption by more than 80%.

Cognitive radio networking allows the unlicensed secondary users to opportunistically access the licensed spectrum as long as the performance of the licensed primary users does not degrade. This dynamic spectrum access strategy is enabled by cognitive radio coupled with spectrum sensing technologies. Due to the imperfection of wireless transmissions, collaborative spectrum sensing (CSS) has been proposed to significantly improve the probability of detecting the transmissions of primary users. Nevertheless, current CSS techniques are sensitive to malicious secondary users, leading to a high false alarm rate and low detection accuracy on the presence of the primary users. In this paper, we present several robust collaborative spectrum sensing schemes that can calculate a trust value for each secondary user to reflect its suspicious level and mitigate its harmful effect on cooperative sensing. Our approach explores the spatial and temporal correlations among the reported information of the secondary users to determine the trust values. Extensive simulation study has been performed and our results demonstrate that the proposed schemes can guarantee the accuracy of the cooperative sensing system with a low false alarm rate when a considerable number of secondary users report false information.

SUMMARY The anonymity and dynamic character of a Peer‐to‐Peer (P2P) network makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT (Distributed Hash Table) trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the DHT to distribute local reputation to trade‐off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in the P2P network, i.e. providing service and issuing reputation scores. To adapt to the dynamic P2P networks, we take dynamic node mechanism into account. Our scheme can assure convergence effectiveness and robustness, when nodes enter or leave the system. We conduct extensive simulations to evaluate the performance of DHTrust. The results show that our system makes significant improvement in convergence speed and aggregation accuracy. Moreover, it is robust to malicious peers. Copyright © 2011 John Wiley & Sons, Ltd.

The anonymity and dynamic characters of Peer-to-Peer (P2P) system makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the Distributed Hash Table (DHT) to distribute local reputation to trade off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in P2P network, i.e., providing service and issuing reputation scores. Through the simulation experiments, we find our system makes significant performance gains in convergence speed and aggregation accuracy, and the most important, is robust to malicious peers.