Butler Matrix Frequency Diverse Retrodirective Array Beamforming: An Energy-Efficient Technique for mmWave NetworksRead the full article
Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas in this fast moving field.
Wireless Communications and Mobile Computing maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
Latest ArticlesMore articles
Master-Slave Topologies with Phase-Locked Loops
Since phase-locked loops (PLLs) were conceived by Bellescize in 1932, their presence has become almost mandatory in any telecommunication device or network, being the essential element to recover frequency and phase information. As the technology developed, PLL appeared in several applications, such as, dense communication networks, smart grids, electronic instrumentation, computational clusters, and integrated circuits. In all of these practical cases, isolated or networked PLLs are responsible for recovering the correct time basis and synchronizing the processes. According to the application needs, different clock distribution strategies were developed, with the master-slave being the simplest and most used choice. Considering that the master clock is obtained from a stable periodic oscillator, two topologies are studied: one-way, not considering clock feedback; and two-way master-slave, with the slave nodes providing clock feedback to the master. Here, these two cases are studied by using simulation strategies, presenting results about the clock signal recovery process in the presence of disturbances, indicating that master-slave clock distribution networks can be useful for networks with few nodes and a stable master oscillator with the one-way topology presenting better results than the two-way arrangement.
Calculating Trust Using Multiple Heterogeneous Social Networks
In today’s Internet, a web user becomes members of multiple social networks due to different types of services provided by each of these networks. This creates an opportunity to make trust decisions that go beyond individual social networks, since these networks provide single perspective of trust. To make trust inference over multiple social networks, these networks need to be consolidated. It is nontrivial as these networks are of heterogeneous nature due to different naming conventions used in these networks. Furthermore, trust metrics extracted from these networks are also varied in nature due to different trust evaluation algorithms used in each of these networks. Heterogeneity of these social networks can be overcome by using semantic technologies as it allows us to represent knowledge using ontologies. Trust data can be consolidated by using such data fusion techniques which not only provide but also preserve trust data integrity from each of the individual social network profiles. The proposed semantic framework is evaluated using two sets of experiments. Through simulations in this work, we analysed various techniques for data fusion. For identifying suitable technique that preserves the integrity of trust consolidated from each of the individual networks, analysis revealed that Weighted Ordered Weighted Averaging parameter best aggregated trust data, and, unlike other techniques, it preserved the integrity of trust from each individual network for varying participant overlap and tie overlap (). Similarly, for experimental analysis, we used findings of the simulation study about the best trust aggregation technique and applied the proposed framework on real-life trust data between participants, which we extracted from pairs of professional social networks. Analysis partially proved our hypothesis about generating better trust values from consolidated multiple heterogeneous networks. We witnessed an improvement in overall results for all the participants who were part of multiple social networks (), while disproving the claim for those existing in nonoverlapping regions of the social networks.
Formal Verification of Hardware Components in Critical Systems
Hardware components, such as memory and arithmetic units, are integral part of every computer-controlled system, for example, Unmanned Aerial Vehicles (UAVs). The fundamental requirement of these hardware components is that they must behave as desired; otherwise, the whole system built upon them may fail. To determine whether or not a component is behaving adequately, the desired behaviour of the component is often specified in the Boolean algebra. Boolean algebra is one of the most widely used mathematical tools to analyse hardware components represented at gate level using Boolean functions. To ensure reliable computer-controlled system design, simulation and testing methods are commonly used to detect faults; however, such methods do not ensure absence of faults. In critical systems’ design, such as UAVs, the simulation-based techniques are often augmented with mathematical tools and techniques to prove stronger properties, for example, absence of faults, in the early stages of the system design. In this paper, we define a lightweight mathematical framework in computer-based theorem prover Coq for describing and reasoning about Boolean algebra and hardware components (logic circuits) modelled as Boolean functions. To demonstrate the usefulness of the framework, we (1) define and prove the correctness of principle of duality mechanically using a computer tool and all basic theorems of Boolean algebra, (2) formally define the algebraic manipulation (step-by-step procedure of proving functional equivalence of functions) used in Boolean function simplification, and (3) verify functional correctness and reliability properties of two hardware components. The major advantage of using mechanical theorem provers is that the correctness of all definitions and proofs can be checked mechanically using the type checker and proof checker facilities of the proof assistant Coq.
Text Data Security and Privacy in the Internet of Things: Threats, Challenges, and Future Directions
In our daily life, Internet-of-Things (IoT) is everywhere and used in many more beneficial functionalities. It is used in our homes, hospitals, fire prevention, and reporting and controlling the environmental changes. Data security is the crucial requirement for IoT since the number of recent technologies in different domains is increasing day by day. Various attempts have been made to cater the user’s demands for more security and privacy. However, a huge risk of security and privacy issues can arise among all those benefits. Digital document security and copyright protection are also important issues in IoT because they are distributed, reproduced, and disclosed with extensive use of communication technologies. The content of books, research papers, newspapers, legal documents, and web pages are based on plain text, and the ownership verification and authentication of such documents are essential. In the current domain of the Internet of Things, limited techniques are available for ownership verification and copyright protection. In the said perspective, this study includes the discussion about the approaches of text watermarking, IoT security challenges, IoT device limitations, and future research directions in the area of text watermarking.
Real Network Traffic Collection and Deep Learning for Mobile App Identification
The proliferation of mobile devices over recent years has led to a dramatic increase in mobile traffic. Demand for enabling accurate mobile app identification is coming as it is an essential step to improve a multitude of network services: accounting, security monitoring, traffic forecasting, and quality-of-service. However, traditional traffic classification techniques do not work well for mobile traffic. Besides, multiple machine learning solutions developed in this field are severely restricted by their handcrafted features as well as unreliable datasets. In this paper, we propose a framework for real network traffic collection and labeling in a scalable way. A dedicated Android traffic capture tool is developed to build datasets with perfect ground truth. Using our established dataset, we make an empirical exploration on deep learning methods for the task of mobile app identification, which can automate the feature engineering process in an end-to-end fashion. We introduce three of the most representative deep learning models and design and evaluate our dedicated classifiers, namely, a SDAE, a 1D CNN, and a bidirectional LSTM network, respectively. In comparison with two other baseline solutions, our CNN and RNN models with raw traffic inputs are capable of achieving state-of-the-art results regardless of TLS encryption. Specifically, the 1D CNN classifier obtains the best performance with an accuracy of 91.8% and macroaverage F-measure of 90.1%. To further understand the trained model, sample-specific interpretations are performed, showing how it can automatically learn important and advanced features from the uppermost bytes of an app’s raw flows.
Performance Improvement on Nonorthogonal Multiple Access without CSIT
In this paper, a downlink virtual-channel-optimization nonorthogonal multiple access (VNOMA) without channel state information at the transmitter (CSIT) is proposed. The novel idea is to construct multiple complex virtual channels by jointly adjusting the amplitudes and phases to maximize the minimum Euclidean distance (MED) among the superposed constellation points. The optimal solution is derived in the absence of CSIT. Considering practical communications with finite input constellations in which symbols are uniformly distributed, we resort to the sum constellation constrained capacity (CCC) to evaluate the performance. For MED criterion, the maximum likelihood (ML) decoder is expected at the receiver. To decrease the computational cost, we propose a reduced-complexity bitwise ML (RBML) decoder. Experimental results are presented to validate the superior of our proposed scheme.