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Abstract: Abstract ICT hold significant potential to increase resource and energy efficiencies and contribute to a circular economy. Yet unresolved is whether the aggregated net effect of ICT overall mitigates or aggravates environmental burdens. While the savings potentials have been explored, drivers that prevent these and possible counter measures have not been researched thoroughly. The concept digital sufficiency constitutes a basis to understand how ICT can become part of the essential environmental transformation. Digital sufficiency consists of four dimensions, each suggesting a set of strategies and policy proposals: (a) hardware sufficiency, which aims for fewer devices needing to be produced and their absolute energy demand being kept to the lowest level possible to perform the desired tasks; (b) software sufficiency, which covers ensuring that data traffic and hardware utilization during application are kept as low as possible; (c) user sufficiency, which strives for users applying digital devices frugally and using ICT in a way that promotes sustainable lifestyles; and (d) economic sufficiency, which aspires to digitalization supporting a transition to an economy characterized not by economic growth as the primary goal but by sufficient production and consumption within planetary boundaries. The policies for hardware and software sufficiency are relatively easily conceivable and executable. Policies for user and economic sufficiency are politically more difficult to implement and relate strongly to policies for environmental transformation in general. This article argues for comprehensive policies for digital sufficiency, which are indispensible if ICT are to play a beneficial role in overall environmental transformation. PubDate: 2022-05-12
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Abstract: Abstract A simulation model of balanced energy consumption for wireless sensor networks (WSNs) is proposed to solve the uneven energy consumption problem in WSN that leads to the rapid death of some nodes and the occurrence of blind spots. The model consists of five parts: a hierarchical model, an energy consumption model, a cluster filtration model, a data transmission model, and a cluster model. It forms a complete scheme that can effectively solve the problems of unreasonable cluster head selection, uneven clustering, and poor network robustness in WSN networking by combining energy consumption depolarization strategies to fill the gap of set optimization scheme. A clustering method is proposed to equalize node load, and an adaptive two-cluster model is used in accordance with node location to equalize network energy consumption. The simulation results show that the proposed energy consumption model can significantly improve the overall performance of the network. The network lifetime is extended by about 120%, the total data transmission per unit of energy is improved by 51% on average, the redundant data generation is reduced by 44.2%, and the localization tracking success rate is reduced by only 2.14%. PubDate: 2022-05-07
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Abstract: Abstract Machine learning (ML)-based traffic classification is evolving into a well-established research domain. Considering statistical characteristics of the traffic flows, ML-based classification methods have succeeded in even classifying encrypted traffic. However, recent research efforts have emerged, for privacy preservation, where traffic obfuscation is being considered as a way to hide traffic characteristics preventing traffic classification. Traffic mutation is one such obfuscation technique that consists of modifying the flow packet sizes and inter-arrival times. However, at the same time, these techniques can be used by malicious attackers to hide their attack traffic and avoid detection. In this paper, we propose a deep learning (DL) model to detect mutated traffic and recover the original one. The experimental results show the effectiveness of the proposed model in detecting mutated traffic with a detection rate up to 95%, on average, and denoising recovery loss less than 3 × 10− 1. PubDate: 2022-04-23
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Abstract: Abstract Cyber-physical systems (CPS) are multi-layer complex systems that form the basis for the world’s critical infrastructure and, thus, have a significant impact on human lives. In recent years, the increasing demand for connectivity in CPS has brought attention to the issue of cyber security. Aside from traditional information systems threats, CPS faces new challenges due to the heterogeneity of devices and protocols. In this paper, we assess how feature selection may improve different machine learning training approaches for intrusion detection and identify the best features for each intrusion detection system (IDS) setup. In particular, we propose using F1-Score as a criteria for the adapted greedy randomized adaptive search procedure (GRASP) metaheuristic to improve the intrusion detection performance through binary, multi-class, and expert classifiers. Our numerical results reveal that there are different feature subsets that are more suitable for each combination of IDS approach, classifier algorithm, and attack class. The GRASP metaheuristic found features that detect accurately four DoS (denial of service) attack classes and several variations of injection attacks in cyber physical systems. PubDate: 2022-04-20
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Abstract: Abstract In this paper, product of two Gaussian-Q functions is represented as sum of exponentials. It is further used to evaluate error probabilities of modulation techniques in fading distributions. The knowledge of moment generating function (MGF) is sufficient enough to derive closed-form solution to integrals appearing in symbol error probability (SEP). Numerical results demonstrate accuracy improvement over other existing competing approximations. Furthermore, the proposed solutions are fairly simple as MGF of fading models comprises of fundamental mathematical functions. PubDate: 2022-04-01
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Abstract: Abstract This paper investigates the performance of iterative interference alignment (IA) with spatial hole sensing in K-user multi-input multi-output (MIMO) cognitive radio (CR) networks. In the considered network, there are some unused degrees of freedom (DoF) or equivalently spatial holes in the primary network (PN) where the secondary network (SN) users communicate without causing harmful interference to the PN receivers. First, the generalized likelihood ratio test method is utilized to determine the availability of the unused DoFs; then, it is decided whether individual primary streams are present in the PN. With the aid of precoding and suppression matrices generated by an iterative IA approach, the interferences in the PN that are caused by the SN are aligned, and due to the secondary transmission, interference leakage on the kth primary receiver decreases below 10− 6. The effects of the detection threshold values and the number of transmitter and receiver antennas are investigated in terms of detection and false alarm probability. Finally, the amplify-and-forward (AF) relaying scheme in the SN is evaluated and the impact of the relaying architecture on the system performance is analyzed. PubDate: 2022-04-01
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Abstract: Abstract Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient machine learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables federated learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations. PubDate: 2022-04-01
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Abstract: Abstract Smart electronic devices are playing a fundamental role in modern home and industrial applications. The increased reliance on such devices, especially in time critical and secure applications, intensifies the need for time synchronization among multiple devices. This work presents a novel audio-based, cheap, offline synchronization method, whereby multiple slaves synchronize simultaneously to a master within a single room. Synchronization is carried out under the proposed protocol in a way that is independent of the physical location of the target devices, which in turn are not required to have any sort of network connectivity. The proposed method relies on the transmission of a De Bruijn sequence that holds the information required for the slaves to synchronize. The effectiveness of the proposed synchronization protocol is validated through an in-house experimental setup. Synchronization at distances of up to 250 cm between the master and a slave was achieved. PubDate: 2022-04-01
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Abstract: Abstract A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure. PubDate: 2022-04-01
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Abstract: Abstract Green computing is a central theme in many computer science areas, including computer networks. Dynamic solutions that can properly adjust network resources can prevent infrastructure over-provision and mitigate power consumption during low-demand periods. In this work, we propose DTM (Dynamic mechanism for Traffic Management), an energy-aware dynamic mechanism for traffic management, built upon the SDN paradigm. DTM continuously monitors the use of network links to concentrate traffic and disconnect idle equipment without degrading the offered quality of service. Our simulations show that the mechanism can save up to 46% of energy, on average, in the links’ capacities of homogeneous and heterogeneous scenarios. In scenarios with average to high traffic demands, the mean energy savings are 36.72% and 17.86%, respectively. Compared to a well-known existing mechanism, our approach is up to 7% better for medium-demand scenarios, and approximately 4% better for high-demand scenarios. PubDate: 2022-04-01
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Abstract: Abstract In this paper, we analyze the performance of an in-band full-duplex (IBFD) decode-and-forward (DF) two-way relay (TWR) system whose two terminal nodes exchange information via a relay node over the same frequency and time slot. Unlike the previous works on full-duplex two-way relay systems, we investigate the system performance under the impacts of both hardware impairments and imperfect self-interference cancellation (SIC) at all full-duplex nodes. Specifically, we derive the exact expression of outage probability based on the signal to interference plus noise and distortion ratio (SINDR), thereby determine the throughput and the symbol error probability (SEP) of the considered system. The numerical results show a strong impact of transceiver impairments on the system performance, making it saturate at even a low level of residual self-interference. In order to tackle with the impact of hardware impairments, we derive an optimal power allocation factor for the relay node to minimize the outage performance. Finally, the numerical results are validated by Monte Carlo simulations. PubDate: 2022-04-01
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Abstract: Abstract This article studies the secure communication of a non-orthogonal multiple access (NOMA) system with the help of an unmanned aerial vehicle (UAV). The UAV flies from an initial location to a final location and assists the NOMA communication between terrestrial nodes, including a source, a near user, and a far user, in the presence of an eavesdropper. Artificial noise (AN) is inserted into the transmitted signal of the UAV to enable secure communication for the users. To maximize the system secrecy sum-rate (SSSR), an algorithm is proposed to optimize key system parameters, such as the UAV’s trajectory, the power–allocation factors, and the transmit powers of the source and the UAV. Since the objective function of the SSSR maximization problem is non-convex, the successive convex optimization (SCO) and block coordinate descent (BCD) methods are applied to find efficient approximate solutions. The effectiveness of the proposed algorithms is confirmed by simulation results. PubDate: 2022-04-01
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Abstract: Abstract In this paper, a noise reduction orthogonal multi-user correlated delay shift keying (NR-OMU-CDSK) noncoherent communication system based on frequency domain processing is proposed. In NR-OMU-CDSK, chaotic signal generated in frequency domain is converted to time domain through inverse Fourier transform, and then the real and imaginary components of the time domain signal are simultaneously modulated to two phase-orthogonal branches. The transmission rate and security performance of each branch are further improved by switches and Walsh code. In the receiver, moving average filter is used to reduce the variance of interference term in the decision variable, and information bits are obtained through relevant demodulation. The bit error rate (BER) performance of NR-OMU-CDSK is evaluated in AWGN channel and multipath Rayleigh fading channel. The research results show that the theoretical BER is basically consistent with the simulation results. The transmission rate of NR-OMU-CDSK is improved by 4N×100% (N is the number of users), and the signal-to-noise ratio is improved by nearly 4dB for the same BER performance, when NR-OMU-CDSK is compared with conventional CDSK in AWGN channel. Moreover, compared to other multi-user systems, this system has also obvious advantages in various system performance and avoids RF delay line problems. PubDate: 2022-04-01
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Abstract: Abstract As machine learning models are increasingly integrated into critical cybersecurity tools, their security issues become a priority. Particularly after the rise of adversarial examples, original data to which a small and well-computed perturbation is added to influence the prediction of the model. Applied to cybersecurity tools, like network intrusion detection systems, they could allow attackers to evade detection mechanisms that rely on machine learning. However, if the perturbation does not consider the constraints of network traffic, the adversarial examples may be inconsistent, thus making the attack invalid. These inconsistencies are a major obstacle to the implementation of end-to-end network attacks. In this article, we study the practicality of adversarial attacks for the purpose of evading network intrusion detection models. We evaluate the impact of state-of-the-art attacks on three different datasets. Through a fine-grained analysis of the generated adversarial examples, we introduce and discuss four key criteria that are necessary for the validity of network traffic, namely value ranges, binary values, multiple category membership, and semantic relations. PubDate: 2022-03-28
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Abstract: Abstract In this article, a novel technique based on the combined utilization of slots in patch and ground plane is proposed to reduce the side lobe level (SLL) in the E-plane of a square patch antenna operating at TM03 mode. The concept of beam reshaping through surface current reorientation is explained in detail by an analytical model and the same is validated through simulated as well as measured results. The combination of a complementary split-ring resonator (CSRR)–shaped slot, two plus-shaped slots on the patch and a plus-shaped defected ground structure (DGS), reduces the SLL by 14.7 dB and increases the half-power beamwidth (HPBW) and first null beamwidth (FNBW) by 21° and 42°, respectively, in comparison to the conventional square patch antenna. Starting from the basic expression of the E-plane radiation pattern of a square patch, an analytical model is invoked to modify this basic expression as per the structural modification to get the final expression for the proposed antenna structure. The physical insight for the stepwise modifications of the antenna structure is properly explained and justified. Analytical and simulated predictions are experimentally verified using a prototype. The simulated and measured results show a good agreement. PubDate: 2022-03-15
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Abstract: Abstract The number of tunnels configured and state kept in IP/MPLS backbones depends on the number of flows and traffic engineering requirements. Segment routing automates tunnel configuration and reduces state in the network, based on the concept of segments: subpaths of the graph. A flow can be defined using only one segment if the route matches the shortest path computed by the IGP, while this number grows with the need for different subpaths. As a consequence, there is a trade-off between traffic engineering and the number of segments used, which translates to header overhead and state in routers. The challenge then is to have flows sharing as many segments as possible. We advance the state of the art with a two-step bi-objective optimization model to reduce the number of configured segments, considering two traffic engineering requirements, load balancing and latency. Our results show that, as we increase the number of flows in the network, the number of configured segments also increases, and then stabilizes regardless of the number of additional flows. Hence, using a real telecommunication network, we show that we can meet traffic engineering requirements with less than 22% of the total number of states as compared to the usual case of IP/MPLS backbones. PubDate: 2022-02-18 DOI: 10.1007/s12243-022-00907-w
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Abstract: Abstract Machine learning mechanisms for network intrusion detection systems lack accurate evaluation, comparison, and deployment due to the scarcity of well-constructed datasets. In this paper, we propose a statistical analysis of the features contained in four highly used security datasets. We conclude that the analyzed datasets should not be used as a benchmark for creating novel anomaly-based mechanisms for intrusion detection systems. The analyzed datasets introduce a biased classification since features are over-correlated, and most of the features are capable of making a complete distinction between normal and attack flows. Our proposed methodology analyzes the correlation among features instead of checking for redundant values or data imbalance. The results align with the performance of three machine learning techniques. We show that biased classification occurs due to a significant difference between attack and normal data. The syntactically generated features are statistically different between normal and attack classes, which implies overfitting in the machine learning approaches. PubDate: 2022-02-12 DOI: 10.1007/s12243-021-00904-5
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Abstract: Abstract This paper aims to find a minimal set of nodes to optimize coverage, connectivity, and energy-efficiency for 2D and 3D Wireless Sensor Networks (WSN). This issue is denoted as a trinomial problem in our study. We propose using the paving rectangle technique, which provides a minimal number of squares based on Fibonacci’s tiles. Applying this strategy to the area coverage, connectivity, and lifetime can reduce the non-deterministic polynomial time problem (NP-Hard problem). We propose a theoretical framework to model the problem, to show the effectiveness of the method applied to the area coverage, connectivity, and lifetime on heterogeneous WSNs. The simulation results highlight the benefits of using this technique. PubDate: 2022-02-03 DOI: 10.1007/s12243-021-00890-8
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Abstract: Abstract In this paper, we propose a lattice-based undeniable signature where security is based on the hardness of the ISIS problem. The security requirements for an undeniable signature scheme are clearly described, and the proposed scheme is proved to enjoy completeness, soundness, unforgeability, and invisibility properties. PubDate: 2022-01-10 DOI: 10.1007/s12243-021-00843-1
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