Recently, deploying static directional chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Extensive simulations and field experiments are conducted to evaluate the performance of our solution. The results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 132.09% on average.

Hybrid recommender systems leverage diverse information sources and techniques to enhance performance. Nevertheless, integrating users’ multifaceted preferences remains challenging due to the uneven data. Meanwhile, information insufficiency introduces uncertainty in recommendations while existing strategies (i.e., recommend or not-recommend) lack the flexibility to address it. Additionally, these works mainly overlook that ratings not only reflect preferences but imply users’ attitudes toward the strategies, leading to the same recommendation rule despite users distinctly. To solve these issues, a Hybrid Three-Way Recommender (HTWR) system is proposed to formulate personalized three-way rules. Specifically, users’ historical and predictive preferences are captured via tags and ratings while integrated based on the user’s data distribution. Then, the theory of three-way decision is introduced to address such uncertainty by offering the option of defer-recommend. Finally, the users variability is formally given and incorporated into the loss function to obtain personalized rules. Experiments on three public datasets validate the superiority and flexibility of the proposed HTWR.

Noise-tolerant feature selections are valuable for data learning; they can resort to efficient fuzzy granulations and uncertainty measures, and a fundamental model concerns weighted kernel fuzzy rough sets (WKFRSs) which consider data distributions and uncertainty. In terms of current WKFRSs, fuzzy granulations adopt k-nearest neighbors for weighted optimization, while uncertainty measures consider single algebraic and informational views; corresponding feature selection algorithms have made achievements of noisy processing, but still exist advancement space from granulation deepening and measurement reinforcement. In this paper embracing WKFRSs, two-type weight-fuzzy granulations are defined by using self-adapting radius neighborhoods, three-view uncertainty measures are comprehensively constructed from uncertainty mechanisms, so 2 × (1 + 1 + 2) = 8 heuristic algorithms of feature selections are systematically established for better noise-aware learning. At first, two improved factors of local density and boundary influence are proposed by general neighborhood characterization and statistical radius determination, and thus two sample weights emerge to adjust Gaussian-kernel fuzzy relations to induce two weight-fuzzy granulations. Then, the fuzzy precision and fuzzycomplementary mutual information are respectively proposed from algebraic and informational views, and the two are combined into two fused measures via arithmetic and geometric means. Furthermore, the above two-type granulations and threeview measures two-dimensionally generate 2×(1+1+2) = 8 new heuristic selection algorithms via feature significances. Finally by data experiments, constructional fuzzy granulations, uncertainty measures, feature selections are validated to have anti-noise characteristics and corresponding robustness, while new selection algorithms acquire better performances of classification learning than multiple contrast algorithms.

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.

Benefitting from the maturation of Wireless Power Transfer technology, Wireless Rechargeable Sensor Networks have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this article, we study a fundamental issue of W ireless ch A rger placement w I th obs T acles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1-1/e-ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

This paper presents an interactive Soapbox platform, which utilizes networked public displays to encourage local citizens to participate in civic engagement. Soapbox is comprised of three subsystems: Soapbox for speaker (SFS), Soapbox for audience (SFA) and Shoutbox. SFS allows people to deliver a speech to the public. SFA enables audience members to watch a speech and give feedback to it. Shoutbox is an input tool that runs on personal devices and is used to author comments and share them with the speaker and other members of the audience. The Soapbox system was evaluated in both the lab and the field. The evaluation demonstrated that users are positive about trying the platform, and can quickly grasp how to use it. Further, participants thought that Soapbox is very appropriate for its purpose, and can effectively attract more citizens to participate in civic engagement.

We introduce a novel interaction method which integrates humans into Internet of Things and advances from the existing client-server paradigm. We take advantage of the shared physical space to facilitate decentralized and seamless human-to-thing, human-to-human and thing-to-thing interactions. We build these interactions upon two core technologies: mobile agents and NFC. Mobile agents realize autonomous execution of user-specific interaction tasks among things while facilitating cooperation and interoperability. Humans initiate and control interactions through physical actions that trigger communications, e.g. mobile agent migration, between NFC devices even over disparate systems. Human social relationships are utilized to disseminate tasks further in the IoT system. We discuss the benefits of this method in comparison with the common smartphone-based control of smart spaces. Real-world evaluation shows that this interaction method is feasible for resource-constrained embedded IoT devices.

Under the framework of set-valued information system, based on application of quantifiers and OWA operator, the definition on upper and lower approximations is generalized to the common form associated with the parameter lambdaepsi [0, 1]. The advantage of such generalization is that it could be flexibly used by restriction of additional linguistic quantifier, and is favorable to the further discussion on knowledge reduction and rules extraction in such model.

In incomplete information systems, based on similarity relation, a method of attributes reduction is discussed in this paper. Relative important degree of attributes is defined. Important degree of attributes is obtained by using the OWA operator to aggregate relative important degree of attributes. Due to finding attributes reduction in accordance with the reorder of attributes which identified by important degree of attributes, the advantage of our method is to reduce the search space of attribute reduction and avoid blindness. Finally, the specific example shows our method is effective.