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This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes. [ABSTRACT FROM AUTHOR]

Data collection with high efficiency is a key problem for the completion of crowdsensing tasks in Mobile Crowdsensing Networks. Much work has been done on the opportunistic routing problem, but they usually just focused on the sociality of node, and the attributes of node itself have always been ignored, such as energy and buffer, which are particularly important for the persistent crowdsensing tasks. In this paper, we now modify the spectral clustering algorithm and propose a novel and more practical routing protocol for MCSN, named EBRP, in which the sociality of node and the dynamic changes of the attributes of node itself are all fully considered when the relay nodes are selected. To evaluate the performance of our routing protocol, we have done extensive simulations and much analysis based on three real-life mobility traces. The results show that our EBRP can greatly outperform the existing protocols in terms of delivery ratio, cost, and node survival ratio. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are evolving with increasingly device heterogeneity, while the problem of effectively composing the platform-specific functionalities provided by heterogeneous sensor nodes to achieve specific goals still remains a challenge. Because of the constrained resources and unreliable communication in sensor networks, traditional service composition techniques in web services with adequate resources are insufficient. In this paper, focusing on the limited energy of sensor nodes, we propose an energy-aware service composition framework for developing various applications in heterogeneous wireless sensor networks. With both the energy-aware metrics: energy-aware load-balancing factor and overall energy consumption, we formulate the process of energy-aware sensor service composition into a combinatorial optimization problem; furthermore, an improved discrete particle swarm optimization (IDPSO) algorithm with inertia weights adjustment and extreme perturbation scheme is proposed to solve the combinatorial optimization problem. The experiment results have shown that the performance of the service route given by IDPSO is approximately equal to the best service route searched out by the exhaustive algorithm. Meanwhile, our proposed energy-aware service composition method is able to reduce the energy consumption and prolong the lifetime of the sensor network when providing stable service composition for various applications. [ABSTRACT FROM AUTHOR]

Policy gradient methods are effective means to solve the problems of mobile multimedia data transmission in Content Centric Networks. Current policy gradient algorithms impose high computational cost in processing high-dimensional data. Meanwhile, the issue of privacy disclosure has not been taken into account. However, privacy protection is important in data training. Therefore, we propose a randomized block policy gradient algorithm with differential privacy. In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. To solve the privacy protection problem, we add a differential privacy protection mechanism to the algorithm, and we prove that it preserves the ε -privacy level. We conduct extensive simulations in four environments, which are CartPole, Walker, HalfCheetah, and Hopper. Compared with the methods such as important-sampling momentum-based policy gradient, Hessian-Aided momentum-based policy gradient, REINFORCE, the experimental results of our algorithm show a faster convergence rate than others in the same environment. [ABSTRACT FROM AUTHOR]

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms. [ABSTRACT FROM AUTHOR]

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users' historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user's historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user's resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user's optimal response strategy. Finally, two conversion methods of the user's total payoff are proposed to ensure flexible application of the user's payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility. [ABSTRACT FROM AUTHOR]

In view of the traditional increment localization method, only the heteroscedasticity caused by the error accumulation is considered unilaterally a kind of incremental localization algorithm based on multivariate analysis is proposed. The algorithm combines the feasible weighted least squares (FWLS) in the multianalysis with the canonical correlation regression (CCR) and utilizes the FWLS to solve the heteroscedasticity caused by the error accumulation in the process of incremental localization; in the process of estimation, the CCR is used to solve the topology problems between the original beacon nodes and new beacon nodes. The simulation results show that the method can not only effectively restrain the influence caused by the accumulative errors but also can adapt to the different node topological shapes, so as to improve the positioning accuracy of the nodes. [ABSTRACT FROM AUTHOR]