In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase. In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN.

Purpose This paper aims to investigate the impact of the General Data Protection Regulation (GDPR) infringement fine announcements on the market value of mostly European publicly listed companies with a view to reinforcing the importance of data privacy compliance, thereby informing cyber security investment strategies for organisations. Design/methodology/approach Previous studies have shown (varying degrees of) evidence of a negative impact of data breach announcements on the share price of publicly listed companies. Following on from this research, further studies have been carried out in assessing the economic impact of the introduction of legislation in this area to encourage firms to invest in cyber security and protect the privacy of data subjects. Existing research has been predominantly US centric. Findings Using event study techniques, a data set of 25 GDPR fine announcement events was analysed, and statistically significant cumulative abnormal returns of around 1% on average up to three days after the event were identified. In almost all cases, this negative economic impact on market value far outweighed the monetary value of the fine itself, and relatively minor fines could result in major market valuation losses for companies, even those having large market capitalisations. Originality/value This research would be of benefit to business management, practitioners of cyber security, investors and shareholders as well as researchers in cyber security or related fields (pointers to future research are given). Data protection authorities may also find this work of interest.

Fingerprint-based localization methods provide high accuracy location estimation, which use machine learning algorithms to recognize the statistical patterns of collected data. In these methods, the users’ locations can be estimated based on the received signal strength vectors from some transmitters. However, the data collection is a labor-intensive phase, and the collected data should be updated periodically. Many researchers have contributed to reducing this cost. The easiest way to remove the data collection cost is to use fingerprints generated by the model-based approaches, in which the trained machine learning algorithm can be updated based on the environment changes. Probabilistic-based localization algorithms, in addition to the user location, can estimate a region of interest called 2σ confidence interval in which the probability of user presence is 95%. Gaussian process regression (GPR) is a probabilistic method that can be used to achieve this goal. However, conventional GPR (CGPR) cannot accurately estimate the confidence interval when noise-free fingerprints generated by the model-based approaches are used in the training phase. In this paper, we propose a novel GPR-based localization algorithm, named enhanced GPR (EGPR), which improves the accuracy level of confidence interval estimation compared to the existing methods while fixing the level of computational complexity in the online phase. We also theoretically prove that GPR-based algorithms are minimum variance unbiased and efficient estimators. Experiments under line-of-sight and non-line-of-sight conditions demonstrate the superiority of our proposed method over counterparts in terms of accuracy as well as applicability in real-time localization systems.

Large-scale Cyber-Physical Systems (CPSs) are information systems that involve a vast network of sensor nodes and other devices that stream observations in real-time and typically are deployed in uncontrolled, broad geographical terrains. Sensor node failures are inevitable and unpredictable events in large-scale CPSs, which compromise the integrity of the sensors measurements and potentially reduce the quality of CPSs services and raise serious concerns related to CPSs safety, reliability, performance, and security. While many studies were conducted to tackle the challenge of sensor nodes failure detection using domain-specific solutions, this paper proposes a novel sensor nodes failure detection approach and empirically evaluates its validity using a real-world case study. This paper investigates time-series clustering techniques as a feasible solution to identify sensor nodes malfunctions by detecting long-segmental outliers in their observations' time series. Three different time-series clustering techniques have been investigated using real-world observations collected from two various sensor node networks, one of which consists of 275 temperature sensors distributed around London. This study demonstrates that time-series clustering effectively detects sensor node's continuous (halting/repeating) and incipient faults. It also showed that the feature-based time series clustering technique is a more efficient long-segmental outliers detection mechanism compared to shape-based time-series clustering techniques such as DTW and K-Shape, mainly when applied to shorter time-series windows.

Cyber-physical systems (CPSs) are integrated systems engineered to combine computational control algorithms and physical components such as sensors and actuators, effectively using an embedded communication core. Smart cities can be viewed as large-scale, heterogeneous CPSs that utilise technologies like the Internet of Things (IoT), surveillance, social media, and others to make informed decisions and drive the innovations of automation in urban areas. Such systems incorporate multiple layers and complex structure of hardware, software, analytical algorithms, business knowledge and communication networks, and operate under noisy and dynamic conditions. Thus, large-scale CPSs are vulnerable to enormous technical and operational challenges that may compromise the quality of data of their applications and accordingly reduce the quality of their services. This paper presents a systematic literature review to investigate data quality challenges in smart-cities large-scale CPSs and to identify the most common techniques used to address these challenges. This systematic literature review showed that significant work had been conducted to address data quality management challenges in smart cities, large-scale CPS applications. However, still, more is required to provide a practical, comprehensive data quality management solution to detect errors in sensor nodes’ measurements associated with the main data quality dimensions of accuracy, timeliness, completeness, and consistency. No systematic or generic approach was demonstrated for detecting sensor nodes and sensor node networks failures in large-scale CPS applications. Moreover, further research is required to address the challenges of ensuring the quality of the spatial and temporal contextual attributes of sensor nodes’ observations.

Flying platforms, such as unmanned aerial vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial base stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of self interference cancellation (SIC) techniques in full-duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one.

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.

In recent years, because of the growing demand for location based services in indoor environment and development of Wi-Fi, fingerprint-based indoor localization has attracted many researchers’ interest. In Wireless Sensor Networks (WSNs), fingerprint based localization methods estimate the target location by using a pattern matching model for the measurements of the Received Signal Strength (RSS) from the available transmitter sensors, which are collected by a smartphone with internal sensors. Due to the dynamic nature of the environment, the fingerprint database needs to be updated, periodically. Hence, it is better to add new fingerprint data to the primary database in order to update them. However, collecting the labeled data is time consuming and labor intensive. In this paper, we propose a novel algorithm, which uses high level extracted features by an autoencoder to improve the localization performance in the classification process. Furthermore, to update the fingerprint data base, we also add crowd-sourced labeled and unlabeled data in order to improve the localization performance, gradually. Simulation results indicate that the proposed method provides a significant improvement in localization performance, using high level extracted features by the autoencoder, and by increasing the number of unlabeled training data.

Location fingerprinting is a technique employed when Global Positioning System (GPS) positioning breaks down within indoor environments. Since Location Service Providers (LSPs) would implicitly have access to such information, preserving user privacy has become a challenging issue in location estimation systems. This paper proposes a low-complexity k-anonymity approach for preserving the privacy of user location and trajectory, in which real location/trajectory data is hidden within k fake locations/trajectories held by the LSP, without degrading overall localization accuracy. To this end, three novel location privacy preserving methods and a trajectory privacy preserving algorithm are outlined. The fake trajectories are generated so as to exhibit characteristics of the user’s real trajectory. In the proposed method, no initial knowledge of the environment or location of the Access Points (APs) is required in order for the user to generate the fake location/trajectory. Moreover, the LSP is able to preserve privacy of the fingerprinting database from the users. The proposed approaches are evaluated in both simulation and experimental testing, with the proposed methods outperforming other well-known k-anonymity methods. The method further exhibits a lower implementation complexity and higher movement similarity (of up to 88%) between the real and fake trajectories.

In this study, two iterative design algorithms for multiple‐input multiple‐output (MIMO) radars with co‐located antennas with point targets are proposed. In these algorithms, by joint design of the receiver filter and transmit waveform using linear combination of orthogonal waveforms, the authors aim to maximise the signal‐to‐interference plus‐noise ratio (SINR) in the presence of signal dependent interference. In the first proposed algorithm, in each iteration, transmit waveforms and receive filter are designed in closed form to decrease the computational complexity. In the second method, by adding constant envelope criteria, the final waveform would be a linear combination of orthogonal waveforms and because of using a constant envelope, combining coefficients have equal magnitudes and different phases. Therefore, it is more practical for hardware implementation in comparison to the first proposed method. Simulation results show that both proposed methods have better SINR performances compared with other methods proposed in MIMO radar literature. The outperformance of the second proposed method with respect to phased arrays shows that by only using phase shifted combination of orthogonal waveforms, better performance in comparison to phased array radars can be achieved.

Multiple‐input multiple‐output (MIMO) radars may outperform other radar systems such as phased array radars, in terms of higher resolution, better detection probability in the presence of interferences, better parameter identifiability and more flexibility in beampattern design. Waveform covariance matrix design, because of its role in the beampattern synthesis process, is one of the most important problems in MIMO radar systems. In this study, the authors have proposed a closed‐form solution based on Fourier series coefficients to design a covariance matrix. The resulting covariance matrix fulfils the practical constraints, i.e. positive semi‐definiteness and the uniform elemental power constraint. It also provides performance similar to that of iterative methods, while requires lower computation time and provides better mean square error with respect to other existing closed‐form methods. Eigenvalue decomposition is also utilised to convert the possible resulted pseudo‐covariance matrices (pseudo‐CM), which are not guaranteed to be positive semidefinite, into a covariance matrix. Simulation results show the performance of the proposed method.

Localization in wireless sensor networks means determination of sensor nodes coordination, and this can be done using the known positions of some other nodes (called anchors) and given distance measurements. Practically, these distance measurements may be corrupted by noise and this may cause errors in estimating the nodes' locations. In addition, when some connections do not have line-of-sight (LOS) links, localization accuracy degrades significantly. Furthermore, in some cases, there is an uncertainty in anchor position and this degrades the accuracy of the localization. In this paper, we propose a new class of convex relaxations for wireless sensor network localization based on the principle of maximum entropy for both LOS and non-LOS environments. Moreover, unlike maximum likelihood (ML) formulation, this class is independent from noise probability density function. In this paper, we propose a localization method in the presence of anchor position uncertainty which is independent from knowing the covariance of uncertainty in anchor positions, while in the ML-based approaches, knowing the covariance matrix is necessary. Simulation results confirm that the proposed convex relaxations can provide more accuracy in comparison with ML-based semidefinite programming methods.

Device‐to‐device (D2D) communication is a promising 5G technology, which helps to increase spectrum efficiency and sum‐rate, as well as to decrease experienced latency. The main challenges of D2D communication are power consumption of paired devices, channel estimation between device pairs, and interference management between devices that use the same time and frequency resources. The electromagnetic time reversal (EMTR) technique is used in D2D communications to focus the signal power in both time and space domains for power efficiency of end users, to simplify the structure of transceivers, to help channel estimation in a low complexity way, and to nullify the effect of interference. First, in the time domain, the effect of using EMTR technique is investigated and its performance is compared with a similar non‐EMTR system. Then, EMTR technique is used in an OFDM‐based system and its advantages in the context of signal to interference plus noise ratio (SINR) and sum‐rate are presented analytically and through computer simulations. Simulation results show a significant gain in SINR, sum‐rate and received signal power for the proposed EMTR‐based D2D system.

This letter considers joint design of transmit waveform and receive filter in colocated multiple-input multiple-output (MIMO) radars in order to enhance target detection performance in the presence of signal-dependent interference. Here, signal-to-interference-plus-noise ratio (SINR) is maximized under the practical constraints of integrated sidelobe level (ISL) and peak sidelobe level (PSL) at the pulse compression output. We have also shown that the joint transmit signal and receive filter design can be formulated as a convex optimization problem, which can be efficiently solved using optimization toolbox (CVX). Simulation results show that our proposed algorithm is able to suppress interference efficiently under ISL and PSL constraints and get close to the SINR efficiency of unconstrained methods.

In this paper, we introduce a likelihood model for tracking the location of object in multiple view systems. Our proposed model transforms conventional nonlinear Euclidean estimation model to an estimation model based on the manifold tangent subspace. In this paper, we show that by decomposition of input noise into two parts and description of model by exponential map, real observations in the Euclidean geometry can be transformed to the manifold tangent subspace. Moreover, by obtained tangent subspace likelihood function, we propose two iterative and noniterative maximum likelihood estimation approaches which numerical results show their good performance.

Spectrum scarcity and dramatically increasing demand for high data rate and high‐quality video live streaming are of future cellular network design challenges. As a solution to this problem, cache‐enabled cellular network architecture has been recently proposed. Device‐to‐device (D2D) communications can be exploited for distributed video content delivery, and devices can be used for caching of the video files. This can increase the capacity and reduce the end‐to‐end delay in cellular networks. In this study, the authors propose a new scheme for video distribution over cellular networks by exploiting full‐duplex (FD) radios for D2D devices in two scenarios: (i) two nodes exchange their desired video files simultaneously and (ii) each node can concurrently transmit to and receive from two different nodes. In the latter case, an intermediate transceiver can serve one or multiple users’ file request(s) whilst capturing its desired file from another device in the vicinity. Mathematical expressions along with extensive simulations are used to compare their proposed scheme with a half‐duplex scheme to show the achievable gains in terms of sum throughput, active links, and delay. They will also look into the energy cost for achieving the improvements provided by operation in FD mode.

In this study, the authors propose a distributed form of Kalman filter for non‐linear dynamics as a generalised Kalman consensus filter (GKCF) and prove its stability, analytically. More specifically, the authors obtain the sufficient condition for asymptotical convergence using Lyapunov analysis. For this purpose, the authors propose four lemmas and show that Kalman consensus filter (KCF) in a linear system is a special case of the authors’ proposed GKCF.

In this letter, we focus on the signal-to-interference-plus-noise ratio (SINR) enhancement using transmitting waveform covariance matrix optimization in colocated MIMO radars. In our proposed algorithm, transmitted waveform covariance matrix has been optimized to focus the transmit beampattern into the target direction and in the receiver, we try to reject maximum number of interfering objects. In the proposed algorithm, transmitted waveform covariance matrix is able to be synthesized with binary phase shift keying waveforms in closed form. Simulation results show that our proposed method has better SINR performance in comparison with methods in which transmitted covariance matrix is designed independently from the interferences locations.

This study considers the problem of waveform design for colocated multiple‐input multiple‐output (MIMO) radars for multiple targets in the presence of multiple interferences in white Gaussian noise. Here, the authors jointly design the transmit waveform and receive beamforming by a sequential algorithm. The proposed sequential algorithm maximises the minimum signal‐to‐interference‐plus‐noise ratio (SINR) to design both continuous and finite alphabet phase waveforms. In the case of continuous phase, all phases can be chosen in the waveform space, while in finite alphabet case, phases are only chosen from a confine set. Two important practical constraints of ‘constant envelope’ and ‘similarity’ are considered as well. The authors also have converted the waveform design problem into a quasi‐convex optimisation problem which can be effectively solved by using convex optimisation toolbox (CVX). They have evaluated the performance of the matched filter output, beampattern and peak‐to‐average power ratio via numerical simulations and shown that the proposed sequential method achieves better SINR performance compared with existing MIMO radar transmit waveform design methods, for both single and multiple target scenarios.

In this letter, we jointly design the transmit signals and the receive combining filter based on exact information on the locations of target and interferences in co-located multiple-input multiple-output (MIMO) radars. The idea behind the proposed method is to maximize the signal-to-interference-plus-noise ratio (SINR) without designing the waveform covariance matrix and exploit the advantages of MIMO radar. Each transmit element sends linear combinations of the orthogonal waveforms by a weighting matrix. Numerical results show the improvement in the SINR performance offered by using the proposed algorithm compared to the phased array and existing MIMO radar techniques.

In this study, the authors propose an efficient resource allocation algorithm in downlink of an orthogonal frequency division multiple access (OFDMA)‐based multi‐cell underlay cognitive radio network. A more realistic model in which multiple secondary users and primary users coexist in a multi‐cell environment has been considered. The main challenge of this model is inter‐cell as well as intra‐cell interference. The proposed algorithm tackles the challenges by adaptively subchannel and power allocation in cognitive radio network. Firstly, based on initial power allocation and with taking throughput of secondary users and signal‐to‐interference plus noise ratio constraint of the primary users into account, subchannel allocation is done by Hungarian method. Then, an iterative water‐filling is implemented to enhance the initialised power allocation. Simulation results demonstrate the strength of the authors proposed scheme compared with some other suboptimal algorithms. The proposed scheme provides significant improvement over the traditional fixed subchannel allocation scheme in terms of spectral efficiency. In addition, it has been shown that the proposed water‐filling policy loads more power into secondary user's band compared with an equal power allocation policy in order to achieve higher throughput.

Inter‐cell interference (ICI) is a serious problem in multi‐cell orthogonal frequency division multiple access systems and should be mitigated by a smart radio resource management scheme. The authors present here a novel downlink adaptive resource allocation (RA) algorithm based on ICI measurement to achieve both multi‐user diversity gain and the cancellation of ICI. The proposed algorithm is performed in two steps: first, interference management by user grouping into different clusters and second, subchannel allocation in order to maximise the total throughput of the network. It does not need any priori frequency planning and no precise signal‐to‐interference plus noise ratio information is required for subchannel allocation. In both steps of the algorithm, the authors propose a graphic framework and use Hungarian method to solve subproblems. Simulation results indicate that the authors algorithm mitigates the ICI successfully and can improve the total throughput of network by 10–15% compared with existing adaptive RA schemes.

In this survey paper, we investigate some of the challenges to be addressed before a practical real-time secondary market for spectrum can be developed. We differentiate the methods of dynamic spectrum allocation as coordinated usage of resources, interworking solutions, integration solutions and secondary access of spectrum. Secondary spectrum access approaches are generally classified as real-time and non-real-time secondary access. However, the focus of this work is on real-time secondary spectrum utilization which can follow a negotiated or opportunistic access strategy. While different solutions for increasing spectrum utilization and efficiency are under investigation, many aspects of spectrum sharing technologies are still open questions. After an extensive literature survey, the major challenges and open questions of real-time secondary usage of spectrum are addressed here. We also provide some insights on potentially important design considerations and requirements when developing successful spectrum management schemes to realize real-time secondary spectrum usage.

In this paper the effect of aggregate interference produced by a group of ultra wideband (UWB) transmitters using direct sequence technology on a Wi-Max/IEEE-802.16 based system is investigated. Various simulations with different parameters like victim receiver bandwidth, carrier frequency, activity factor and the number of users are performed to measure the produced interference. Finally these measured values are compared to the maximum tolerable interference levels specified for Wi-Max systems to assess if UWB system will create problems for the operation of Wi-Max/IEEE 802.16 based system. It is demonstrated that in a realistic hot spot scenario, the interference produced by the UWB system will fall below the threshold values and this system will not harm the operation of the Wi-Max receiver.

A novel approach is proposed and analysed in this paper, where the micro-cell utilises the same frequency band as the macro-cell to transmit non-real time data. The main idea is that, whenever the macro-cell interference falls below a given threshold, the micro-cell schedules data with a transmission rate dependent on the micro-cell signal strength and the macro-cell interference level. We prove analytically that utilizing the hence created micro-cell transmission scheme non-real time data can be transmitted at micro-cells. We also derive the average duration and the frequency of transmit event for non-real time data transmission at micro-cells, all of which is verified by means of a dynamic CDMA system level simulator

In a macrocell network, there will remain some geographical areas within the cell coverage where mobile stations require further services. These are mainly in terms of downloading data, in particular in temporary or permanent hotspot areas, such as exhibitions, conference centers, etc. The objective of this study is to obtain extra capacity for W-CDMA systems by adding hotspot base stations to an already present continuous macrocell layer. IP-based services are offered to local mobile stations, using packet switched transmission without additional bandwidth; i.e. using the same frequency band in both the hotspot and macrocell. In order to maximize the hotspot base station throughput, and at the same time, to minimize the interlayer interference from hotspot to macrocell, a new link quality control scheme is designed. Downlink system level simulations are also carried out to prove the feasibility of the idea and to show the effect of required E/sub b//(I/sub o/ + N/sub o/) for macrocell users, on the hotspot data throughput.

The interaction between layers in multilayer cellular systems can be in terms of directing the traffic overflow from the lower layer to the overlaying layer (overflow sensitive cell-layer selection strategy), and/or of classifying the users based on their speed (speed sensitive cell-layer selection strategy). In this paper, we study a hybrid speed/overflow sensitive micro/macro cell-layer selection strategy for two-layer CDMA cellular systems. It is shown that the speed threshold has a major impact on the capacity and the handover rate in such systems. Therefore, here we propose a handover rate control scheme where the speed threshold can be dynamically adjusted according to the handover and blocking rate feedbacks. Simulation results show that the proposed scheme can control the handover rate even when the speed distribution of mobile stations changes with time.

A dynamic radio network simulator is implemented for studying WCDMA based hierarchical cell structures. The simulator allows estimation of capacity and quality of service related issues in a two-layer network (microcells and macrocells). The input to the simulator is base station and mobile station information and its output is presented as the blocking and dropping probabilities, handoff rate and capacity of the assumed network. Both uplink and downlink are considered. As an example, the impact of between-layer handover on the capacity is investigated. The whole simulator is based entirely on visual C++ software.

Performance (QoS and capacity) improvement by use of interference cancellation detection is examined for an uplink W-CDMA system with imperfect power control. Link- and system-level simulation results are given along with analytical expressions to investigate the case.