The success and scale of the Internet and its protocol IP has spurred emergent distributed technologies such as fog/edge computing and new application models based on distributed containerized microservices. The Internet of Things and Connected Communities are poised to build on these technologies and models and to benefit from the ability to communicate in a peer-to-peer (P2P) fashion. Ubiquitous sensing, actuating and computing implies a scale that breaks the centralized cloud computing model. Challenges stemming from limited IPv4 public addresses, the need for transport layer authentication, confidentiality and integrity become a burden on developing new middleware and applications designed for the network's edge. One approach - not reliant on the slow adoption of IPv6 - is the use of virtualized overlay networks, which abstract the complexities of the underlying heterogeneous networks that span the components of distributed fog applications and middleware. This paper describes the evolution of the design and implementation of IP-over-P2P (IPOP) - from its purist P2P inception, to a pragmatic hybrid model which is influenced by and incorporates standards. The hybrid client-server/P2P approach allows IPOP to leverage existing robust and mature cloud infrastructure, while still providing the characteristics needed at the edge. IPOP is networking cyber infrastructure that presents an overlay virtual private network which self-organizes with dynamic membership of peer nodes into a scalable structure. IPOP is resilient to partitioning, supports redundant paths within its fabric, and provides software defined programming of switching rules to utilize these properties of its topology.

Advanced information-processing services based on cloud computing are in great demand. However, users want to be able to customize cloud services for their own purposes. To provide image-processing services that can be optimized for the purpose of each user, we propose a technique for chaining image-processing functions in a CPU-field programmable gate array (FPGA) coupled server architecture. One of the most important requirements for combining multiple image-processing functions on a network, is low latency in server nodes. However, large delay occurs in the conventional CPU-FPGA architecture due to the overheads of packet reordering for ensuring the correctness of image processing and data transfer between the CPU and FPGA at the application level. This paper presents a CPU-FPGA server architecture with a real-time packet reordering circuit for low-latency image processing. In order to confirm the efficiency of our idea, we evaluated the latency of histogram of oriented gradients (HOG) feature calculation as an offloaded image-processing function. The results show that the latency is about 26 times lower than that of the conventional CPU-FPGA architecture. Moreover, the throughput decreased by less than 3.7% under the worst-case condition where 90 percent of the packets are randomly swapped at a 40-Gbps input rate. Finally, we demonstrated that a real-time video monitoring service can be provided by combining image processing functions using our architecture.

In this article, we propose a data framework for edge computing that allows developers to easily attain efficient data transfer between mobile devices or users. We propose a distributed key-value storage platform for edge computing and its explicit data distribution management method that follows the publish/subscribe relationships specific to applications. In this platform, edge servers organize the distributed key-value storage in a uniform namespace. To enable fast data access to a record in edge computing, the allocation strategy of the record and its cache on the edge servers is important. Our platform offers distributed objects that can dynamically change their home server and allocate cache objects proactively following user-defined rules. A rule is defined in a declarative manner and specifies where to place cache objects depending on the status of the target record and its associated records. The system can reflect record modification to the cached records immediately. We also integrate a push notification system using WebSocket to notify events on a specified table. We introduce a messaging service application between mobile appliances and several other applications to show how cache rules apply to them. We evaluate the performance of our system using some sample applications.

A drastic increase in cyberattacks targeting Internet of Things (IoT) devices using telnet protocols has been observed. IoT malware continues to evolve, and the diversity of OS and environments increases the difficulty of executing malware samples in an observation setting. To address this problem, we sought to develop an alternative means of investigation by using the telnet logs of IoT honeypots and analyzing malware without executing it. In this paper, we present a malware classification method based on malware binaries, command sequences, and meta-features. We employ both unsupervised or supervised learning algorithms and text-mining algorithms for handling unstructured data. Clustering analysis is applied for finding malware family members and revealing their inherent features for better explanation. First, the malware binaries are grouped using similarity analysis. Then, we extract key patterns of interaction behavior using an N-gram model. We also train a multiclass classifier to identify IoT malware categories based on common infection behavior. For misclassified subclasses, second-stage sub-training is performed using a file meta-feature. Our results demonstrate 96.70% accuracy, with high precision and recall. The clustering results reveal variant attack vectors and one denial of service (DoS) attack that used pure Linux commands.

In this paper, we propose an adaptive fusion successive cancellation list decoder (ADF-SCL) for polar codes with single cyclic redundancy check. The proposed ADF-SCL decoder reasonably avoids unnecessary calculations by selecting the successive cancellation (SC) decoder or the adaptive successive cancellation list (AD-SCL) decoder depending on a log-likelihood ratio (LLR) threshold in the decoding process. Simulation results show that compared to the AD-SCL decoder, the proposed decoder can achieve significant reduction of the average complexity in the low signal-to-noise ratio (SNR) region without degradation of the performance. When L_max=32 and E_b/N₀=0.5dB, the average complexity of the proposed decoder is 14.23% lower than that of the AD-SCL decoder.

The major weakness of global navigation satellite system receivers is their vulnerability to intentional and unintentional interference. Frequency domain interference suppression (FDIS) technology is one of the most useful countermeasures. The pseudo-range measurement is unbiased after FDIS filtering given an ideal analog channel. However, with the influence of the analog modules used in RF front-end, the amplitude response and phase response of the channel equivalent filter are non-ideal, which bias the pseudo-range measurement after FDIS filtering and the bias varies along with the frequency of the interference. This paper proposes an unbiased interference suppression method based on signal estimation and spectrum compensation. The core idea is to use the parameters calculated from the tracking loop to estimate and reconstruct the desired signal. The estimated signal is filtered by the equivalent filter of actual channel, then it is used for compensating the spectrum loss caused by the FDIS method in the frequency domain. Simulations show that the proposed algorithm can reduce the pseudo-range measurement bias significantly, even for channels with asymmetrical group delay and multiple interference sources at any location.

Power line communications (PLC) is a communication technology that uses a power-line as a transmission medium. Previous studies have shown that connecting an AC adapter such as a mobile phone charger to the power-line affects signal quality. Therefore, in this paper, the authors analyze the influence of chargers on inter-computer communications using packet capture to evaluate communications quality. The analysis results indicate the occurrence of a short duration in which packets are not detected once in a half period of the power-line supply: named communication forbidden time. For visualizing the communication forbidden time and for evaluating the communications quality of the inter-computer communications using PLC, the authors propose an instantaneous power-line frequency synchronized superimposed chart and its plotting algorithm. Further, in order to analyze accurately, the position of the communication forbidden time can be changed by altering the initial burst signal plotting position. The difference in the chart, which occurs when the plotting start position changes, is also discussed. We show analysis examples using the chart for a test bed data assumed an ideal environment, and show the effectiveness of the chart for analyzing PLC inter-computer communications.

This paper studies the joint optimization of precoding, transmit power and data rate allocation for energy-efficient full-duplex (FD) cloud radio access networks (C-RANs). A new nonconvex problem is formulated, where the ratio of total sum rate to total power consumption is maximized, subject to the maximum transmit powers of remote radio heads and uplink users. An iterative algorithm based on successive convex programming is proposed with guaranteed convergence to the Karush-Kuhn-Tucker solutions of the formulated problem. Numerical examples confirm the effectiveness of the proposed algorithm and show that the FD C-RANs can achieve a large gain over half-duplex C-RANs in terms of energy efficiency at low self-interference power levels.

A low-complexity time-invariant angle-range dependent directional modulation (DM) based on time-modulated frequency diverse array (TM-FDA-DM) is proposed to achieve point-to-point physical layer security communications. The principle of TM-FDA is elaborated and the vector synthesis method is utilized to realize the proposal, TM-FDA-DM, where normalization and orthogonal matrices are designed to modulate the useful baseband symbols and inserted artificial noise, respectively. Since the two designed matrices are time-invariant fixed values, which avoid real-time calculation, the proposed TM-FDA-DM is much easier to implement than time-invariant DMs based on conventional linear FDA or logarithmical FDA, and it also outperforms the time-invariant angle-range dependent DM that utilizes genetic algorithm (GA) to optimize phase shifters on radio frequency (RF) frontend. Additionally, a robust synthesis method for TM-FDA-DM with imperfect angle and range estimations is proposed by optimizing normalization matrix. Simulations demonstrate that the proposed TM-FDA-DM exhibits time-invariant and angle-range dependent characteristics, and the proposed robust TM-FDA-DM can achieve better BER performance than the non-robust method when the maximum range error is larger than 7km and the maximum angle error is larger than 4°.

The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.