Channel-Hopping Sequence and Rendezvous MAC for Cognitive Radio Networks.

In cognitive radio networks (CRNs), two secondary users (SUs) need to meet on a channel among multiple channels within a finite time to establish a link, which is called rendezvous. For blind rendezvous, researchers have devised ample well-grounded channel hopping (CH) sequences that guarantee smaller time-to-rendezvous. However, the best part of these works lacks the impact of network factors, particularly channel availability and collision during rendezvous. In this study, a new CH scheme is investigated by jointly considering the medium access control (MAC) protocol for single-hop multi-user CRNs. The analysis of our new variable hopping sequence (V-HS) guarantees rendezvous for the asymmetric channel model within a finite time. Although this mathematical concept guarantees rendezvous between two SUs, opportunities can be overthrown because of the unsuccessful exchange of control packets on that channel. A successful rendezvous also requires the exchange of messages reliably while two users visit the same channel. We propose a MAC protocol, namely ReMAC, that can work with V-HS and CH schemes. This design allows multiple rendezvous opportunities when a certain user visits the channel and modifies the conventional back-off strategy to maintain the channel list. Both simulation and analytical results exhibited improved performance over the previous approaches.

Frequency-Based Representation of Massive Alerts and Combination of Indicators by Heterogeneous Intrusion Detection Systems for Anomaly Detection.

Although the application of a wide range of sensors has been generalized through the development of technology, the processing of massive alerts generated through data analysis and monitoring remains a challenge. This problem is also found in cyber security because the intrusion detection system (IDS) produces a tremendous number of alerts. Massive alerts not only significantly increase resources for analysis, but also make it difficult to analyze the overall situation of the system. In order to handle massive alerts, we propose using an indicator as a frequency-based representation. The proposed indicator is generated from categorical parameters of alerts that occur within a unit time utilizing frequency and is used for situational awareness with machine learning to detect whether there is a threat or not. The advantage of using indicators is that they can determine the situation for a period without analyzing individual alerts, which helps security experts to recognize the situation in the system and focus on targets that require in-depth analysis. In addition, the conversion from the categorical parameters which is highly related to analysis to numeric parameter allows for applying machine learning. For performance evaluation, we collect data from an HAI testbed similar to real critical infrastructure and conduct experiments using indicators and XGBoost, a classification machine learning algorithm against five famous vulnerability attacks. Consequently, we show that the proposed method can detect attacks with more than 90 percent accuracy, and the performance is enhanced using heterogeneous intrusion detection systems.

Random Access Using Deep Reinforcement Learning in Dense Mobile Networks.

5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users' random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.

Learning from Routing Information for Detecting Routing Misbehavior in Ad Hoc Networks.

Owing to ad hoc wireless networks' properties, the implementation of complex security systems with higher computing resources seems troublesome in most situations. Therefore, the usage of anomaly or intrusion detection systems has attracted considerable attention. The detection systems are implemented either as host-based, run by each node; or as cluster/network-based, run by cluster head. These two implementations exhibit benefits and drawbacks, such as when cluster-based is used alone, it faces maintaining protection when nodes delay to elect or replace a cluster head. Despite different heuristic approaches that have been proposed, there is still room for improvement. This work proposes a detection system that can run either as host- or as cluster-based to detect routing misbehavior attacks. The detection runs on a dataset built using the proposed routing-information-sharing algorithms. The detection system learns from shared routing information and uses supervised learning, when previous network status or an exploratory network is available, to train the model, or it uses unsupervised learning. The testbed is extended to evaluate the effects of mobility and network size. The simulation results show promising performance even against limiting factors.

Effects of phosphate addition on biofilm bacterial communities and water quality in annular reactors equipped with stainless steel and ductile cast iron pipes.

The impact of orthophosphate addition on biofilm formation and water quality was studied in corrosion-resistant stainless steel (STS) pipe and corrosion-susceptible ductile cast iron (DCI) pipe using cultivation and culture-independent approaches. Sample coupons of DCI pipe and STS pipe were installed in annular reactors, which were operated for 9 months under hydraulic conditions similar to a domestic plumbing system. Addition of 5 mg/L of phosphate to the plumbing systems, under low residual chlorine conditions, promoted a more significant growth of biofilm and led to a greater rate reduction of disinfection by-products in DCI pipe than in STS pipe. While the level of THMs (trihalomethanes) increased under conditions of low biofilm concentration, the levels of HAAs (halo acetic acids) and CH (chloral hydrate) decreased in all cases in proportion to the amount of biofilm. It was also observed that chloroform, the main species of THM, was not readily decomposed biologically and decomposition was not proportional to the biofilm concentration; however, it was easily biodegraded after the addition of phosphate. Analysis of the 16S rDNA sequences of 102 biofilm isolates revealed that Proteobacteria (50%) was the most frequently detected phylum, followed by Firmicutes (10%) and Actinobacteria (2%), with 37% of the bacteria unclassified. Bradyrhizobium was the dominant genus on corroded DCI pipe, while Sphingomonas was predominant on non-corroded STS pipe. Methylobacterium and Afipia were detected only in the reactor without added phosphate. PCR-DGGE analysis showed that the diversity of species in biofilm tended to increase when phosphate was added regardless of the pipe material, indicating that phosphate addition upset the biological stability in the plumbing systems.

Effects of diverse water pipe materials on bacterial communities and water quality in the annular reactor.

To investigate the effects of pipe materials on biofilm accumulation and water quality, an annular reactor with the sample coupons of four pipe materials (steel, copper, stainless steel, and polyvinyl chloride) was operated under hydraulic conditions similar to a real plumbing system for 15 months. The bacterial concentrations were substantially increased in the steel and copper reactors with progression of corrosion, whereas those in stainless steel (STS) and polyvinyl chloride (PVC) reactors were affected mainly by water temperature. The heterotrophic plate count (HPC) of biofilms was about 100 times higher on steel pipe than other pipes throughout the experiment, with the STS pipe showing the lowest bacterial number at the end of the operation. Analysis of the 16S rDNA sequences of 176 cultivated isolates revealed that 66.5% was Proteobacteria and the others included unclassified bacteria, Actinobacteria, and Bacilli. Regardless of the pipe materials, Sphingomonas was the predominant species in all biofilms. PCR-DGGE analysis showed that steel pipe exhibited the highest bacterial diversity among the metallic pipes, and the DGGE profile of biofilm on PVC showed three additional bands not detected from the profiles of the metallic materials. Environmental scanning electron microscopy showed that corrosion level and biofilm accumulation were the least in the STS coupon. These results suggest that the STS pipe is the best material for plumbing systems in terms of the microbiological aspects of water quality.

The effects of UV disinfection on drinking water quality in distribution systems.

UV treatment is a cost-effective disinfection process for drinking water, but concerned to have negative effects on water quality in distribution system by changed DOM structure. In the study, the authors evaluated the effects of UV disinfection on the water quality in the distribution system by investigating structure of DOM, concentration of AOC, chlorine demand and DBP formation before and after UV disinfection process. Although UV treatment did not affect concentration of AOC and characteristics of DOM (e.g., DOC, UV(254,) SUVA(254), the ratio of hydrophilic/hydrophobic fractions, and distribution of molecular weight) significantly, the increase of low molecular fraction was observed after UV treatment, in dry season. Chlorine demand and THMFP are also increased with chlorination of UV treated water. This implies that UV irradiation can cleave DOM, but molecular weights of broken DOM are not low enough to be used directly by microorganisms in distribution system. Nonetheless, modification of DOM structure can affect water quality of distribution system as it can increase chlorine demands and DBPs formation by post-chlorination.