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Task Offloading with Power Control for Mobile Edge Computing Using Reinforcement Learning-Based Markov Decision Process
This paper proposes an efficient computation task offloading mechanism for mobile edge computing (MEC) systems. The studied MEC system consists of multiple user equipment (UEs) and multiple radio interfaces. In order to maximize the number of UEs benefitting from the MEC, the task offloading and power control strategy for a UE is optimized in a joint manner. However, the problem of finding the optimal solution is NP-hard. We then reformulate the problem as a Markov decision process (MDP) and develop a reinforcement learning- (RL-) based algorithm to solve the MDP. Simulation results show that the proposed RL-based algorithm achieves a near-optimal performance compared to the exhaustive search algorithm, and it also outperforms the received signal strength- (RSS-) based method no matter from the standpoint of the system (as it leads to a larger number of beneficial UEs) or an individual (as it generates a lower computation overhead for a UE).
A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification
With the rapid growth of mobile Apps, it is necessary to classify the mobile Apps into predefined categories. However, there are two problems that make this task challenging. First, the name of a mobile App is usually short and ambiguous to reflect its real semantic meaning. Second, it is usually difficult to collect adequate labeled samples to train a good classifier when a customized taxonomy of mobile Apps is required. For the first problem, we leverage Web knowledge to enrich the textual information of mobile Apps. For the second problem, the mostly utilized approach is the semisupervised learning, which exploits unlabeled samples in a cotraining scheme. However, how to enhance the diversity between base learners to maximize the power of the cotraining scheme is still an open problem. Aiming at this problem, we exploit totally different machine learning paradigms (i.e., shallow learning and deep learning) to ensure a greater degree of diversity. To this end, this paper proposes Co-DSL, a collaborative deep and shallow semisupervised learning framework, for mobile App classification using only a few labeled samples and a large number of unlabeled samples. The experiment results demonstrate the effectiveness of Co-DSL, which could achieve over 85% classification accuracy by using only two labeled samples from each mobile App category.
Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound
In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.
An Approach Based on Customized Robust Cloaked Region for Geographic Location Information Privacy Protection
Location-based services (LBS) have gained huge popularity because of the easy availability of modern mobile devices and the fast development of geographical information science (GIS). However, the lack of protection for private user positions might give rise to privacy concerns. This kind of problem is especially serious in mobile application environment because many mobile applications tend to use LBS. In this paper, we propose a new privacy preserving approach using customized robust cloaked region (RCR), depending on a peer-to-peer structure and the premise that users do not trust each other when sharing their geographical locations. Two algorithms are used to generate the RCR with high user density. The area of the RCR is controlled by the user’s demanded degree of protection. To enhance the resistance to regional background knowledge attack, we incorporate a location semantic value into each unit of the user map. According to extensive simulations, our method can effectively obfuscate a user’s geographical location into a highly indistinguishable region because of the disturbance of nearby users and different equally possible locations.
Stabilizing Transmission Capacity in Millimeter Wave Links by Q-Learning-Based Scheme
Due to uncontrollable factors (e.g., radio channel quality, wireless terminal mobility, and unpredictable obstacle emergence), a millimeter wave (mmWave) link may encounter some problems like unstable transmission capacity and low energy efficiency. In this paper, we propose a new transmission capacity stabilization scheme based on the Q-learning mechanism with the aid of edge computing facilities in an integrated mmWave/sub-6 GHz system. With aid of the proposed scheme, an integrated mmWave/sub-6 GHz user equipment (UE) can adjust its transmission power and angle, even choose a relaying UE to stabilize its transmission capacity. Differing from traditional schemes, the proposed scheme is run in edge computing facilities, where any UE only needs to provide its personalized information (e.g., base station discovery, neighboring UEs, working status (i.e., busy and idle), position coordinates, and residual energy level), and then it will receive intelligent and adaptive guidance from edge computing facilities. This facilitates each UE to maintain its transmission capacity stability by adjusting its radio parameters. The simulation results show that any UE with aid of the proposed scheme can achieve more stable transmission capacity and higher energy efficiency.
Analysis and Design of Systematic Rateless Codes in FH/BFSK System with Interference
The asymptotic analysis of systematic rateless codes in frequency hopping (FH) systems with interference is first provided using discretized density evolution (DDE) and compared with the traditional fixed-rate scheme. A simplified analysis with Gaussian assumption of initial message is proposed in the worst case of interference, which has much lower complexity and provides a very close result to DDE. Based on this simplified analysis, the linear programming is employed to design rateless codes and the simulation results on partial-band interference channels show that the optimized codes have more powerful antijamming performance than the codes originally designed for conventional systems.