Wireless Communications and Mobile Computing

Digital divide means unequal access to the people for information and communication technology (ICT) facilities. The developed countries are comparatively less digitally divided as compared to developing countries. This study focuses on District Chitral considering its geographical conditions and high mountainous topography which plays a significant role in its isolation. Aside from the digital divide, the situation in Chitral is even more severe in terms of the absence of basic ICT infrastructure and electricity in the schools. To address this issue, especially in female secondary and higher secondary schools, we designed a project to bridge the digital divide via Wireless Local Area Network on Raspberry Pi3 for balancing the ICT facilities in the targeted area. The Wi-Fi-Based Content Distributors (Wi-Fi-BCDs) were provided to bridge the digital divide in rural area schools of Chitral. The Wi-Fi-BCD is a solar-based system that is used to deliver quality educational contents directly to classroom, library, or other learning environments without electricity connection and Internet wire as these facilities are available by default in it. The close-ended questionnaire was adopted to collect data from the students, teachers, and headmistresses of girl secondary and higher secondary schools in Chitral. The procedure of validity, reliability, regression, correlation, and exploratory factor analysis was used to analyze the obtained data. The technology acceptance model (TAM) was modified and adopted to examine the effects of Wi-Fi-BCD for bridging the digital divide. The relationship of the modified TAM model was examined through regression and correlation to verify the model fitness according to the data obtained. The result analysis of this study shows that the relationship of the modified TAM model with its variables is positively significant, while the analysis of path relationship between model variables and outcomes from the questionnaire shows that it motivates learners to use Wi-Fi-BCD.

Wireless sensor network is a key technology in Internet of Things. However, due to the large number of sensor nodes and limited security capability, aging nodes and malicious nodes increase. In order to detect the untrusted nodes in the network quickly and effectively and ensure the reliable operation of the network, this paper proposes a dynamic network security mechanism. Firstly, the direct trust value of the node is established based on its behavior in the regional information interaction. Then, the comprehensive trust value is calculated according to the trust recommendation value and energy evaluation value of other high-trust nodes. Finally, node reliability and management nodes are updated periodically. Malicious nodes are detected and isolated according to the credibility to ensure the dynamic, safe, and reliable operation of the network. Simulation results and analysis show that the node trust value calculated by this mechanism can reflect its credibility truly and accurately. In terms of reliable network operation, the mechanism can effectively detect malicious nodes, with higher detection rate, avoid the risk of malicious nodes as management nodes, reduce the energy consumption of nodes, and also play a defensive role in DOS attacks in wireless sensor networks.

Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two-stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real-time performance.

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

In order to improve the efficiency of customization and reduce the cost of customization under Big data environment, this paper uses cost-sharing contract, pricing mechanism, Hotelling model, and game theory tools and research methods, for C2B Electronic Commerce (e-commerce) mode of Supply Chain Pricing Strategy for in-depth discussion. This paper first gives the architecture of the customization service system based on big data. The paper studies the game equilibrium of supply chain members under four scenarios: centralized decision-making, decentralized decision-making, C2B-dominated decision-making, and traditional enterprise-dominated decision-making in a supply chain composed of a supplier and C2B e-commerce enterprises with horizontal price competition, and examines the cross-price. Important parameters such as impact coefficient, impact coefficient of effort degree of personalized customization, and so on have an impact on variables such as effort degree of personalized customization, retail price, and profit of supply chain members of C2B e-commerce enterprises. Research shows that with the increase of cross-price impact coefficient, C2B e-commerce will enhance its personalized customization efforts in different situations in order to pursue higher profits.

Recently, the research on the vehicular ad hoc network (VANET) has been paid more attention by researchers with the quick development of the autonomous driving technology. In the VANET, vehicles can communicate with everything through the route established by routing algorithms. However, the topology of the VANET changes fast because the vehicles move fast. Also, as the number of vehicles increases, the probability of data collision and the transmission latency will also increase when communicating. Therefore, the VANET needs a stable, low-latency, and efficient route for vehicles to communicate with each other. However, the existing routing algorithms are either unable to aggregate data or are not suitable for the large-size VANET. In this paper, we consider the vehicle attribute information comprehensively and cluster the vehicles on the road by using the cluster algorithm we propose. We dynamically select the cluster heads at each moment according to their attribute information. We consider all kinds of nodes in the network and the vehicle nodes will communicate with each other through the cluster heads under the two-level communicating algorithm we propose. Compared with the existing cluster routing algorithm, the algorithm we propose is much more suitable for the large-size VANET because the cluster heads do not need a gateway to help them communicate. In the simulation part, we set some real street scenes in Simulation of Urban Mobility (SUMO) and the vehicles can move by the traffic rules like in the real world, which is more suitable for the VANET. After analysing the communication performance in Network Simulator version 2 (NS2), we can get a conclusion that the algorithm proposed is superior to the traditional routing algorithm. The route established by the algorithm we propose is much more stable and efficient. And the latency is also lower than the former.