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Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain. [ABSTRACT FROM AUTHOR]

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by conventional Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected cumulative sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments. [ABSTRACT FROM AUTHOR]

Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for crowdsourcing applications that require extensive validation of the tasks quality, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and even counterintuitive findings: 1) a high-quality worker may pretend to be a low-quality one; 2) the rise of task quality from high-quality workers may not result in the increased utility of the requestor; 3) the utility of the requestor may not get improved with the increasing number of workers. These findings can boost forward looking and strategic planning solutions for crowdsourcing. [ABSTRACT FROM AUTHOR]

In this paper, we present the first algorithm to precisely implement the abstract MAC (absMAC) layer under the physical SINR model in dynamic networks. The absMac layer, first presented by (Kuhn et al., 2009), provides reliable local broadcast communications, with timing guarantees stated in terms of a collection of abstract delay functions, based on which high-level algorithms can be designed, independent of specific channel behaviors. The implementation of absMAC requires the design of a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is to minimize the bounds of the abstract delay functions. Halldórsson et al. (2015) showed that under the standard SINR model (synchronous communications without physical carrier sensing or location information), there exist no efficient exact implementations. In this work, we demonstrate that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that precisely implements the absMAC layer under the SINR model in dynamic networks. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm leads to many new faster algorithms for solving high-level problems under the SINR model in dynamic networks. We demonstrate this by exemplifying problems of Consensus, Multi-Message Broadcast, and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle many communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality. [ABSTRACT FROM AUTHOR]

A cost-efficient deal that can achieve high sensing quality with a low reward is the permanent goal of the requestor in mobile crowdsensing, which heavily depends on the quantity and quality of the workers. However, the spatial diversity and temporal dynamics lead to heterogeneous worker supplies, making it hard for the requestor to utilize a homogeneous pricing strategy to realize a cost-efficient deal from a systematic point of view. Therefore, a cost-efficient deal calls for a cost-efficient pricing strategy, boosting the whole sensing quality with less operation (computation) cost. However, the state-of-the-art studies ignore the dual cost-efficient demands of large-scale sensing tasks. Hence, we propose a combinatorial pinning zero-determinant (ZD) strategy, which empowers the requestor to utilize a single strategy within its feasible range to minimize the total expected utilities of the workers throughout all sensing regions for each time interval, without being affected by the strategies of the workers. Through turning the worker-customized strategy to an interval-customized one, the proposed combinatorial pinning ZD strategy reduces the number of pricing strategies required by the requestor from $O(n^3)$ O (n 3) to $O(n)$ O (n) . Besides, it extends the application scenarios of the classical ZD strategy from two-player simultaneous-move games to multiple-heterogeneous-player sequential-move ones, where a leader can determine the linear relationship of the players’ expected utilities. Such an extension enriches the theoretical hierarchy of ZD strategies, broadening their application scope. Extensive simulations based on real-world data verify the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of mutual privacy protection in social participatory sensing in which individuals contribute their private information to build a (virtual) community. Particularly, we propose a mutual privacy preserving $k$-means clustering scheme that neither discloses an individual's private information nor leaks the community's characteristic data (clusters). Our scheme contains two privacy-preserving algorithms called at each iteration of the $k$-means clustering. The first one is employed by each participant to find the nearest cluster while the cluster centers are kept secret to the participants; and the second one computes the cluster centers without leaking any cluster center information to the participants while preventing each participant from figuring out other members in the same cluster. An extensive performance analysis is carried out to show that our approach is effective for $k$ -means clustering, can resist collusion attacks, and can provide mutual privacy protection even when the data analyst colludes with all except one participant. [ABSTRACT FROM PUBLISHER]

Lane-level navigation has received a lot of attention in recent years. It has played a great role in assisting route planning, as well as navigating automated vehicles. Aside from sticking to the planned route, abnormal traffic situations which result in blocking lanes could impact lane switching decisions. Unfortunately, there is currently no navigation system that can sense and track a vehicle's lane position and to advise the driver of lane switching decisions. Google Maps stores a priori the number of lanes and their directions at each highway exit and provides this information to drivers when navigating. However, even with this information, some drivers may not be able to make an informed decision regarding when and where to make a correct lane switch. This motivated us to develop a mechanism for the detection and tracking of real-time lane changes. In this paper, we propose a GPS-aiding system that can sense and track a vehicle's lane position. The system leverages smart phones’ computing capability, rear cameras, and inertial motion sensors. With little extra computational overhead, the system applies computer vision techniques to achieve lane-level positioning. We also design a machine learning-based algorithm to detect and track lane switching. We conduct a series of experiments, analyze our system in real-world environments, and achieve very promising results. We believe our system can be a great asset to current smart phone navigation systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network (CRN) when there exist multiple primary channels. Our work focuses on a scenario in which each secondary user (SU) has the freedom to decide whether to participate in cooperative spectrum sensing; if not, the SU becomes a free rider. Such a mechanism can conserve the energy for spectrum sensing at a risk of sacrificing the spectrum sensing performance. To overcome this problem, we address the following two questions: “Which action (contributing to spectrum sensing or not) should be taken?” and “which channel should be sensed?” We model our framework as an evolutionary game in which each SU makes its decision based on its utility history and takes an action more frequently if it brings a relatively higher utility. We also develop a coalition formation algorithm based on the channel status, where each SU always chooses the coalition that brings the most information regarding the status of the corresponding channel. The simulation results demonstrate that the proposed scheme can guarantee the detection probability at a low false alarm rate. The results also indicate that our algorithm can satisfy different requirements by carefully tuning the system parameters. [ABSTRACT FROM PUBLISHER]

The benefits of cognitive radio networking (CRN) have been well recognized with the emerging wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interferences to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the PUs. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem is how to select a relay efficiently. Due to the potentially large number of SUs, it is infeasible for a PU to first scan all the SUs and then pick the best. Basically, the PU transmitter intends to observe the SUs sequentially. After observing an SU, the PU needs to make a decision regarding whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory and derive the optimal stopping rule. We also discuss the optimal observation order of the SUs and analyze the collision probability. To evaluate the performance of our proposed scheme, we compare our optimal stopping policy with the random selection policy through simulation study, and the results demonstrate the superiority of our policy. Extensive simulation study is conducted to investigate the impact of different parameters on the system performance, and the results indicate that our algorithm can satisfy different system requirements by carefully tuning the corresponding system parameters. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Dynamic spectrum access is a new spectrum sharing paradigm that allows secondary users to access the abundant spectrum holes or white spaces in the licensed spectrum bands. DSA is a promising technology to alleviate the spectrum scarcity problem and increase spectrum utilization. While DSA has attracted many research efforts recently, in this article, we discuss the challenges of DSA and aim to shed light on its future. We first give an introduction to the stateof- the-art in spectrum sensing and spectrum sharing. Then, we examine the challenges that prevent DSA from major commercial deployment. We believe that, to address these challenges, a new DSA model is critical, where the licensed users cooperate in DSA and hence much more flexible spectrum sharing is possible. Furthermore, the future DSA model should consider the political, social, economic, and technological factors all together, to pave the way for the commercial success of DSA. To support this future DSA model, the future cognitive radio is expected to have additional components and capabilities, to enforce policy, provide incentive and coexistence mechanisms, etc. We call the future cognitive radio with the expanded capabilities a network radio, and discuss its architecture as well as the design issues for future DSA. [ABSTRACT FROM PUBLISHER]