An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy

MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.0% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 56.8% on average, and the average evasive rate is noticeably improved by up to 56.2%

De-anonymyzing scale-free social networks by using spectrum partitioning method

Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anonymization into node matching problem in graph, and the de-anonymization method can reduce the number of nodes to be matched at each time. In addition, we use spectrum partitioning method to divide the social graph into disjoint subgraphs, and it can effectively be applied to large-scale social networks and executed in parallel by using multiple processors. Through the analysis of the influence of power-law distribution on de-anonymization, we synthetically consider the structural and personal information of users which made the feature information of the user more practical

Harnessing Context for Budget-Limited Crowdsensing with Massive Uncertain Workers

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 standard 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 for 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 total sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers are massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments

Crocs: Cross-Technology Clock Synchronization for WiFi and ZigBee

Clock synchronization is a key function in embedded wireless systems and networks. This issue is equally important and more challenging in IoT systems nowadays, which often include heterogeneous wireless devices that follow different wireless standards. Conventional solutions to this problem employ gateway-based indirect synchronization, which suffers low accuracy. This paper for the first time studies the problem of cross-technology clock synchronization. Our proposal called Crocs synchronizes WiFi and ZigBee devices by direct cross-technology communication. Crocs decouples the synchronization signal from the transmission of a timestamp. By incorporating a barker-code based beacon for time alignment and cross-technology transmission of timestamps, Crocs achieves robust and accurate synchronization among WiFi and ZigBee devices, with the synchronization error lower than 1 millisecond. We further make attempts to implement different cross-technology communication methods in Crocs and provide insight findings with regard to the achievable accuracy and expected overhead

Real-space observation of short-period cubic lattice of skyrmions in MnGe

Emergent phenomena and functions arising from topological electron-spin textures in real space or momentum space are attracting growing interest for new concept of states of matter as well as for possible applications to spintronics. One such example is a magnetic skyrmion, a topologically stable nanoscale spin vortex structure characterized by a topological index. Real-space regular arrays of skyrmions are described by combination of multi-directional spin helixes. Nanoscale configurations and characteristics of the two-dimensional skyrmion hexagonal-lattice have been revealed extensively by real-space observations. Other three-dimensional forms of skyrmion lattices, such as a cubic-lattice of skyrmions, are also anticipated to exist, yet their direct observations remain elusive. Here we report real-space observations of spin configurations of the skyrmion cubic-lattice in MnGe with a very short period (~3 nm) and hence endowed with the largest skyrmion number density. The skyrmion lattices parallel to the {100} atomic lattices are directly observed using Lorentz transmission electron microscopes (Lorentz TEMs). It enables the first simultaneous observation of magnetic skyrmions and underlying atomic-lattice fringes. These results indicate the emergence of skyrmion-antiskyrmion lattice in MnGe, which is a source of emergent electromagnetic responses and will open a possibility of controlling few-nanometer scale skyrmion lattices through atomic lattice modulations