Systems for Internet of Things (IoT) have generated new requirements in all aspects of their development and deployment, including expanded Quality of Service (QoS) needs, enhanced resiliency of computing and connectivity, and the scalability to support massive numbers of end devices in a variety of applications. The research reported here concerns the development of a reliable and secure IoT/cyber physical system (CPS), providing network support for smart and connected communities, to be realized by means of distributed, secure, resilient Edge Cloud (EC) computing. This distributed EC system will be a network of geographically distributed EC nodes, brokering between end-devices and Backend Cloud (BC) servers. This paper focuses on three main aspects of the CPS: a) resource management in mobile cloud computing; b) information management in dynamic distributed databases; and c) biological-inspired intrusion detection system.

On the Internet, end hosts and network nodes interdependently work to smoothly transfer traffic. Observed traffic dynamics are the result of those interactions among those entities. To manage Internet traffic to provide satisfactory quality services, such dynamics need to be well understood to predict traffic patterns. In particular, some nodes have a function that sends back-pressure signals to backward nodes to reduce their sending rate and mitigate congestion. Transmission Control Protocol (TCP) congestion control in end-hosts also mitigates traffic deviation to eliminate temporary congestion by reducing the TCP sending rate. How these congestion controls mitigate congestion has been extensively investigated. However, these controls only throttle their sending rate but do not reduce traffic volume. Such congestion control fails if congestion is persistent, e.g., for hours, because unsent traffic demand will infinitely accumulate. However, on the actual Internet, even with persistent congestion, such accumulation does not seem to occur. During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model. We also show that by using ULCL, we can predict the degree of QoS degradation and traffic reduction as an equilibrium of the feedback loop. We applied our model to traffic and packet-loss ratio time series data gathered in an actual network and demonstrate that it effectively estimates actual traffic and packet-loss ratio.

The current orthogonal cooperative system (OCS) achieves diversity through the use of relays and the consumption of an additional time slot (TS). To guarantee the orthogonality of the received signal and avoid the mutual interference at the destination, the source has to be mute in the second TS. Consequently, the spectral efficiency (SE) is halved. In this paper, linear constellation precoded orthogonal frequency division multiplexing with index modulation (LCP-OFDM-IM) based OCS is proposed, where the source activates the complementary subcarriers to convey the symbols over two TSs. Hence the source can consecutively transmit information to the destination without the mutual interference. Compared with the current OFDM based OCS, the LCP-OFDM-IM based OCS can achieve a higher SE, since the subcarrier activation patterns (SAPs) can be exploited to convey additional information. Furthermore, the optimal precoder, in the sense of maximizing the minimum Euclidean distance of the symbols conveyed on each subcarrier over two TSs, is provided. Simulation results show the superiority of the LCP-OFDM-IM based OCS over the current OFDM based OCS.

Information floating (IF) permits mobile nodes to transmit information to other nodes by direct wireless communication only in transmittable areas (TAs), thus avoiding unneeded and inefficient information distribution to irrelevant areas, which is a problem with the so-called epidemic communication used in delay tolerant networks. In this paper, we propose applying IF to sensor networking to find and share available routes in disaster situations. In this proposal, IF gathers and shares information without any assistance from gateways, which is normally required for conventional wireless sensor networks. A performance evaluation based on computer simulation results is presented. Furthermore, we demonstrate that the proposed method is effective by highlighting its advantageous properties and directly comparing it with a method based on epidemic communication. Our findings suggest that the proposed method is a promising step toward more effective countermeasures against restricted access in disaster zones.

In resource-constrained network function virtualization (NFV) environments, it is expected that data throughput for service chains is maintained by using virtual network functions (VNFs) effectively. In this paper, we formulate an optimization problem for maximizing the total data throughput in resource-constrained NFV environments. Moreover, based on our formulated optimization problem, we propose a heuristic service chain construction algorithm for maximizing the total data throughput. This algorithm also determines the placement of VNFs, the amount of resources for each VNF, and the transmission route for each service chain. It is expected that the heuristic algorithm can construct service chains more quickly than the meta-heuristic algorithm. We evaluate the performance of the proposed methods with simulations, and we investigate the effectiveness of our proposed heuristic algorithm through a performance comparison. Numerical examples show that our proposed methods can construct service chains so as to maximize the total data throughput regardless of the number of service chains, the amount of traffic, and network topologies.

Virtual network embedding has been intensively studied for a decade. The time complexity of most conventional methods has been reduced to the cube of the number of links. Since customers are likely to request a dense virtual network that connects every node pair directly (|E|=O(|V|²)) based on a traffic matrix, the time complexity is actually O(|E|³=|V|⁶). If we were allowed to reduce this dense network to a sparse one before embedding, the time complexity could be decreased to O(|V|³); the time saving would be of the order of a million times for |V|=100. The network reduction, however, combines several virtual links into a broader link, which makes the embedding cost (solution quality) much worse. This paper analytically and empirically investigates the trade-off between the embedding time and cost for the virtual network reduction. We define two simple reduction operations and analyze them with several interesting theorems. The analysis indicates that an exponential drop in embedding time can be achieved with a linear increase in embedding cost. A rigorous numerical evaluation justifies the desirability of the trade-off.

This paper proposes a traveling maintenance method based on the resource pool concept, as a new network maintenance model. For failure recovery, the proposed method utilizes permissible time that is ensured by shared resource pools. In the proposed method, even if a failure occurs in a communication facility, maintenance staff wait for occurrence of successive failures in other communication facilities during the permissible time instead of immediately tackling the failure. Then, the maintenance staff successively visit the communication facilities that have faulty devices and collectively repair them. Therefore, the proposed method can reduce the amount of time that the maintenance staff take for fault recovery. Furthermore, this paper provides a system design that optimizes the proposed traveling maintenance according to system requirements determined by the design philosophy of telecommunication networks. Through simulation experiments, we show the effectiveness of the proposed method.

In this paper, we propose a method to reduce the labeling cost while acquiring training data for a malicious domain name detection system using supervised machine learning. In the conventional systems, to train a classifier with high classification accuracy, large quantities of benign and malicious domain names need to be prepared as training data. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by using approximately 1% of the training data used by the conventional systems. Another disadvantage of the conventional system is that if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved. The combination of the two methods proposed here allows us to develop a new system for malicious domain name detection with high classification accuracy and generalization ability by labeling a small amount of training data.

Distributed Denial of Service attacks against the application layer (L7 DDoS) are among the most difficult attacks to defend against because they mimic normal user behavior. Some mitigation techniques against L7 DDoS, e.g., IP blacklisting and load balancing using a content delivery network, have been proposed; unfortunately, these are symptomatic treatments rather than fundamental solutions. In this paper, we propose a novel technique to disincentivize attackers from launching a DDoS attack by increasing attack costs. Assuming financially motivated attackers seeking to gain profit via DDoS attacks, their primary goal is to maximize revenue. On the basis of this assumption, we also propose a mitigation solution that requires mining cryptocurrencies to access servers. To perform a DDoS attack, attackers must mine cryptocurrency as a proof-of-work (PoW), and the victims then obtain a solution to the PoW. Thus, relative to attackers, the attack cost increases, and, in terms of victims, the economic damage is compensated by the value of the mined coins. On the basis of this model, we evaluate attacker strategies in a game theory manner and demonstrate that the proposed solution provides only negative economic benefits to attackers. Moreover, we implement a prototype to evaluate performance, and we show that this prototype demonstrates practical performance.

This paper presents a methodology with which to construct an equivalent simulation model of closed-loop BCI testing for a vehicle component. The proposed model comprehensively takes the transfer impedance of the test configuration into account. The methodology used in this paper relies on circuit modeling and EM modeling as well. The BCI test probes are modeled as the equivalent circuits, and the frequency-dependent losses characteristics in the probe's ferrite are derived using a PSO algorithm. The measurement environments involving the harness cable, load simulator, DUT, and ground plane are designed through three-dimensional EM simulation. The developed circuit model and EM model are completely integrated in a commercial EM simulation tool, EMC Studio of EMCoS Ltd. The simulated results are validated through comparison with measurements. The simulated and measurement results are consistent in the range of 1MHz up to 400MHz.

We investigate the potential of photonic crystal fiber (PCF) to realize high quality and high-power transmission. We utilize the PCF with a quasi-uniform air-hole structure, and numerically clarify that the quasi-uniform PCF can realize the effective area (A_eff) of about 500µm² with bending loss comparable with that of a conventional single-mode fiber for telecom use by considering the quasi single-mode transmission. We then apply the quasi-uniform PCF to kW-class high-power beam delivery for the single-mode laser processing. The cross-sectional design of the PCF with the high-power delivery potential of more than 300kW·m is numerically and experimentally revealed. A 10kW single-mode beam at 1070nm is successfully delivered over a 30m-long optical fiber cable containing a fabricated PCF with single-mode class beam quality of M² =1.7 for the first time.

In the coming smart-home era, more and more household electrical appliances are generating more and more sensor data and transmitting them over the home networks, which are often connected to Internet through Point-to-Point Protocol over Ethernet (PPPoE) for desirable authentication and accounting. However, according to our knowledge, high-speed commercial home PPPoE router is still absent for a home network environment. In this paper, we first introduce and evaluate our programmable platform FLARE-DPDK for ease of programming network functions. Then we introduce our effort to build a compact 10Gbps software FLARE PPPoE router on a commercial mini-PC. In our implementation, the control plane is implemented with Linux PPPoE software for authentication-like signaling control. The data plane is implemented over FLARE-DPDK platform, where we get packets from physical network interfaces directly bypassing Linux kernel and distribute packets to multiple CPU cores for data processing in parallel. We verify our software PPPoE router in both lab and production network environment. The experimental results show that our FLARE software PPPoE router can achieve much higher throughput than a commercial PPPoE router tested in a production environment.

The increase in network traffic in recent years has led to increased power consumption. Accordingly, many studies have tried to reduce the energy consumption of network devices. Various types of data have become available in large quantities via large high-speed computer networks. Time-constrained file transfer is receiving much attention as an advanced service. In this model, a request must be completed within a user-specified deadline or rejected if the requested deadline cannot be met. Some bandwidth assignment and routing methods to accept more requests have been proposed. However, these existing methods do not consider energy consumption. Herein, we propose a joint bandwidth assignment and routing method that reduces energy consumption for time-constrained large file transfer. The bandwidth assignment method reduces the power consumption of mediate node, typically router, by waiting for requests and transferring several requests at the same time. The routing method reduces the power consumption by selecting the path with the least predicted energy consumption. Finally, we evaluate the proposed method through simulation experiments.

Communication networks are now an essential infrastructure of society. Many services are constructed across multiple network domains. Therefore, the reliability of multi-domain networks should be evaluated to assess the sustainability of our society, but there is no known method for evaluating it. One reason is the high computation complexity; i.e., network reliability evaluation is known to be #P-complete, which has prevented the reliability evaluation of multi-domain networks. The other reason is intra-domain privacy; i.e., network providers never disclose the internal data required for reliability evaluation. This paper proposes a novel method that computes the lower and upper bounds of reliability in a distributed manner without requiring privacy disclosure. Our method is solidly based on graph theory, and is supported by a simple protocol that secures intra-domain privacy. Experiments on real datasets show that our method can successfully compute the reliability for 14-domain networks in one second. The reliability is bounded with reasonable errors; e.g., bound gaps are less than 0.1% for reliable networks.

An analytic expression of the Capon spectrum is derived for two uncorrelated incident signals. On the basis of this theoretical formulation, we discuss the effect of a factor arising from the inner product of mode vectors with respect to the incident angles, which compromises the resolution. We show numerical examples to demonstrate the effect that the inner product of mode vectors has on the shape of the Capon spectrum.

In this study, product of two independent and non-identically distributed (i.n.i.d.) random variables (RVs) for κ-µ fading distribution and α-µ fading distribution is considered. The statistics of the product of RVs has been broadly applied in a large number of communications fields, such as cascaded fading channels, multiple input multiple output (MIMO) systems, radar communications and cognitive radios (CR). Exact close-form expressions of probability density function (PDF) and cumulative distribution function (CDF) with exact series formulas for the product of two i.n.i.d. fading distributions κ-µ and α-µ are deduced more accurately to represent the provided product expressions and generalized composite multipath shadowing models. Furthermore, ergodic channel capacity (ECC) is obtained to measure maximum fading channel capacity. At last, interestingly unlike κ-µ, η-µ, α-µ in [9], [17], [18], these analytical results are validated with Monte Carlo simulations and it shows that for provided κ-µ/α-µ model, non-linear parameter has more important influence than multipath component in PDF and CDF, and when the ratio between the total power of the dominant components and the total power of the scattered waves is same, higher α can significantly improve channel capacity over composite fading channels.

The conventional universal filtered-DFT-spread-OFDM (UF-DFTs-OFDM) can drastically improve the out-of-band emission (OOBE) caused by the discontinuity between symbols in the conventional cyclic prefix-based DFTs-OFDM (CP-DFTs-OFDM). However, the UF-DFTs-OFDM degrades the communication quality in a long-delay multipath fading environment due to the frequency-domain ripple derived from the long transition time of the low pass filter (LPF) corresponding to the guard interval (GI). In this paper, we propose an enhanced UF-DFTs-OFDM (eUF-DFTs-OFDM) that achieves significantly low OOBE and high communication quality even in a long-delay multipath fading environment. The eUF-DFTs-OFDM applies an LPF with quite short length in combination with the zero padding (ZP) or the CP process. Then, the characteristics of the OOBE, peak-to-average power ratio (PAPR), and block error rate (BLER) are evaluated by computer simulation with the LTE uplink parameters. The result confirms that the eUF-DFTs-OFDM can improve the OOBE by 22.5dB at the channel-edge compared to the CP-DFTs-OFDM, and also improve the E_S/N₀ to achieve BLER =10^-3 by about 2.5dB for QPSK and 16QAM compared to the UF-DFTs-OFDM. For 64QAM, the proposed eUF-DFTs-ODFDM can eliminate the error floor of the UF-DFTs-OFDM. These results indicate that the proposed eUF-DFTs-OFDM can significantly reduce the OOBE while maintaining the same level of communication quality as the CP-DFTs-OFDM even in long-delay multipath environment.

Long delay multipath is a major cause of the poor reception of digital terrestrial broadcasting signals. The direct solution to this problem in orthogonal frequency division multiplexing (OFDM) system is to make the guard interval (GI) longer than the maximum channel delay. However, given the wide variety in broadcasting channel characteristics, the worst case GI may be twice the value needed which decreases the spectral efficiency and service quality. Therefore, the solution must be implemented on the receiver side. For the next generation broadcasting system, this paper proposes a space division multiplexing (SDM) multiple-input multiple-output (MIMO)-OFDM receiver for a multipath environment whose maximum delay time exceeds the GI length. The proposed system employs the high frequency resolution spatial filters that have the same configuration as the conventional one but operate at four times higher frequency resolution. Computer simulation and laboratory test results are presented to show the effectiveness of the proposed system.

In this paper, hierarchical interference coordination is proposed that suppresses both intra- and inter-cluster interference (ICI) in clustered wireless networks. Assuming transmitters and receivers are equipped with multiple antennas and complete channel state information is shared among all transmitters within the same cluster, interference alignment (IA) is performed that uses nulls to suppress intra-cluster interference. For ICI mitigation, we propose a null-steering precoder designed on the nullspace of a principal eigenvector of the correlated ICI channels, which eliminates a significant amount of ICI power given the exchange of cluster geometry between neighboring clusters. However, as ICI is negligible for the system in which the distance between clusters are large enough, the proposed scheme may not improve the system performance compared with the pure IA scheme that exploits all spatial degrees of freedom (DoF) to increase multiplexing gain without ICI mitigation. For the efficient interference management between intra- and inter-cluster, we analyze the decision criterion that provides an adaptive transmission mode selection between pure IA and proposed ICI reduction in given network environments. Moreover, a low computational complexity based transmission mode switching algorithm is proposed for irregularly distributed networks.