Researchers from both academia and industry have started exploring the potential of sixth generation cellular networks, envisioning novel concepts and futuristic capabilities. An empirical analysis of real-world fifth generation (5G) deployments serves a compass to direct the next stage of evolution and provides insights on the additional improvements required for the future services and applications. However, acquiring real-world measurement data at a city or county scale poses substantial challenges in terms of time and cost. To address this issue, this paper presents a practical and cost-effective data collection testbed and a methodology that harnesses the existing services provided by municipal council authorities, including curbside waste collections to generate large-scale realtime network coverage maps. Rich datasets of measurement data collected from multiple fourth generation (4G) and 5G cells over seven months in Nottingham, United Kingdom (UK) for all four major UK network operators, namely EE, Vodafone, O2, and Three Mobile, are provided. These large datasets can be utilized for analyzing network deployment options, coverage, and future service provisioning as well as designing and training artificial intelligence and machine learning algorithms to further optimize the mobile networks. In addition, the paper reviews the latest empirical 5G network analysis tools and techniques, which were not seen in previous generations.

Climate change poses complex and interconnected risks that demand structured, data-driven approaches to support effective adaptation and sustainable development. This study introduces a new integrated framework for assessing climate vulnerability in Indonesia by combining the Framework for the Development of Environment Statistics and the Driving Forces-Pressure-State-Impact-Response (DPSIR) model. The framework leverages diverse data from 2014 to 2023, integrating socio-economic statistics and satellite-derived environmental indicators mapped to the DPSIR structure for analysis. To analyze causal relationships among vulnerability components, Partial Least Squares Structural Equation Modeling was applied, revealing significant pathways along the DPSIR sequence. Notably, socio-economic drivers exerted strong direct effects on environmental pressures, while environmental degradation indirectly influenced institutional responses through its link to climate-related risks such as flooding and drought. A composite Integrated Climate Vulnerability Index was developed using the Entropy Weight Method to quantify spatio-temporal vulnerability patterns across Indonesian provinces. Results show a national decline in vulnerability between 2014 and 2020, followed by a modest rebound in the years following the COVID19 pandemic. However, regional disparities persisted; eastern provinces, including Papua and East Nusa Tenggara, consistently exhibited high vulnerability. The evaluation demonstrates that integrating structured frameworks with statistical modeling supports evidence-based, region-specific climate adaptation and sustainable development planning.

The 5G era has seen the largest number of studies around Radio Environment Maps (REM) than in the previous three generations combined. Visualization of network coverage on interactive maps provides contextual information and numerous benefits to operators, regulators and to the public. In this context, spatial interpolation and extrapolation techniques are used to add synthetic data points between measurements to fill gaps in the data, where techniques such as machine learning, Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) are used to enhance the quality of REMs. In this paper we present a state-of-the-art software package, which integrates a series of interpolation methods, augmented with polygon intersection queries functionality to control data used for estimation of coverage on the roads. The proposed web-based application is powered by a set of modular Python packages, making it future-proof and real-world ready, enabling efficient and precise network management.

With the rise of requirements for high-speed and low-latency connectivity, innovative approaches such as network slicing, Quality of Service (QoS) Provisioning, and reinforcement learning-based resource allocation, including the use of resource blocks (RBs) including radio resources, have to keep pace with these evolving requirements. By utilising network intelligence through machine learning and deep reinforcement learning, there is a potential to enhance QoS provisioning, expand the current network capacity, reduce congestion and latency, improve energy efficiency, and thus support new business models and revenue sources. The progress so far suggests that while considering RBs, network-slicing datasets, intertwined with QoS provisioning, have not been explored comprehensively, and packet drop probability or rate has not been taken into account in resource allocation. This paper proposes a network slicing method that uses seven machine learning algorithms and demonstrates its efficiency and accuracy compared to benchmarks in the literature with respect to QoS. Moreover, a priority algorithm is developed to ensure that packets with a high chance of being dropped (affecting QoS) are queued first. A resource allocation algorithm considering QoS provisioning and RBs is utilised to improve network performance based on a mathematical derivation of packet drop rate. Furthermore, a virtualisation of the processing between Cloud and Edge depends on the network slice. By intelligently distributing tasks between Cloud and Edge resources using deep reinforcement learning and genetic algorithms, an offloading script ensures uninterrupted service availability even when all network resources (i.e., RBs) are in use, thus maintaining the desired QoS.

Despite 5 revolutionary generations and 18 releases, automated measurement tools for cellular networks offer limited programmability and their integrated APIs remain difficult to reproduce. We demonstrate the challenges and solutions related to building web and mobile-based applications for continuous cellular networks data collection and analysis. This is shown in the form of an integrated suite of reliable, low-cost cloud-based data processing, querying and analysis software functions that domain experts and laypeople users can utilize to assess cellular networks’ quality of service at street level.

WebRTC is a Google-developed project that allows users to communicate directly. It is an open-source tool supported by all major browsers. Since it does not require additional installation steps and provides ultra-low latency streaming, smart city and social network applications such as WhatsApp, Facebook Messenger, and Snapchat use it as the underlying technology on the client-side both on desktop browsers and mobile apps. While the open-source tool is deemed to be secure and despite years of research and security testing, there are still vulnerabilities in the real-time communication application programming interface (API). We show in this paper how eavesdropping can be enabled by exploiting weaknesses and loopholes found in official WebRTC specifications. We demonstrate through real-world implementation how an eavesdropper can intercept WebRTC video calls by installing a malicious code onto the WebRTC webserver. Furthermore, we identify and discuss several, easy to perform, ways to detect wiretapping. Our evaluation shows that several indicators within webrtc-internals API traces can be used to detect anomalous activities, without the need for network monitoring tools.

Massive data handling requirement in education Industry 4.0 has attracted interests in the research of microservice architectures due to their scalability, resilience, and elasticity characteristics. This development has been challenged by extensive data exchange required by a set of independent microservices to build a complete application, which could result in increasing risks and exposure to the security and privacy breaches of the data. It is imperative to see that educational data are highly sensitive, critical for ascertaining educational attainment and facilitating credentials for qualification verifications. This article puts forward a new proposal of devising a security and privacy-preserving design mechanism of data transactions in educational microservices leveraging the blockchain technology. The design comprises three phases, namely the blockchain framework, data sending–receiving, and confidentiality-integrity-availability over a secured platform with each phase having detailed mechanisms for algorithm implementation. The proposal is shown to exhibit favorable performance in terms of time cost of publishing, throughput, and latency, and shown to have high survey acceptance in terms of confidentiality, integrity, and availability with approximately 10% improvement from prior blockchain adoption.

While recent works on investigating renewable energy sources for powering the highway offer promising solutions for sustainable environments, they are often impeded by unequal distribution of sources across the region due to variations in solar exposure and road intensity that electromagnetically and mechanically generate the energy. By exploiting viable gathering of massive renewable energy data using the Internet of Things (IoT), this paper proposes a framework for improved highway-energy management based on the unmanned aerial vehicle-assisted wireless energy re-distribution of the harvested renewable energy. Combining both massive low-rate sensing with high-speed 6G-envisioned transmission for data aggregation, the IoT architecture is of multi-scale, consisting of: i) global data exchange and analytics for energy mapping, re-distribution planning and forecasting, and ii) local data sensing and processing at individual highway lampposts for micro-energy management. The feasibility of the networked energy system is analyzed via analytical cost-reliability analyses. The cost analysis demonstrates the cost-effectiveness through the lowest Requirement of Energy and Cost of Energy for the setup and maintenance. The reliability analysis reveals the energy plus (E+) feature of the system in certain conditions with enhanced reliability in adverse weathers that impact energy generation. With multi-scale data connectivity to intelligently manage standalone renewable energy, this work puts forward a viable idea of 6G use cases with massively networked energy sensors with a vision of achieving super-connected and intelligence-equipped highways.

Modern societies are becoming increasingly reliance on inter-connected digital systems. Despite numerous benefits, it is important to overcome existing security problems in a highly inter-connected system, like Sensor-Cloud systems. Sensor-Cloud is the product of the integration of wireless sensor networks and cloud computing. However, when a physical sensor node receives multiple service commands simultaneously, there will be some service collisions, namely, coupling security problem. This coupling security problem may lead to the failure of sustainable services and the system security threat. In order to solve the problem, sustainable resource management and maximum resource utilization are important. In this paper, we extend the Kuhn-Munkres algorithm based on fog computing to achieve sustainability. To begin with, we design a buffer queue in fog computing layer which will return the result to the cloud layer directly to increase the resource utilization. Then, we extend the Kuhn-Munkres algorithm to get the initial assignments of resources. The last step is to determine whether the initial assigned resources can be further scheduled, which means that we further improve the resource utilization to realize sustainable resource management. The results demonstrate that our method outperforms the traditional scheduling methods, which decreases both of the rounds and computational costs of scheduling by 24.04-57.78 percent and 9.88-31.51 percent, respectively. The experimental evaluations proved that the performance of the proposed fog-based scheme can effectively solve coupling security problem for sustainable Sensor-Cloud systems.

Recent years have seen continuous exposure of online social network (OSN) users to the security vulnerability due to socio-technical cyber hazards committed by OSN associates. Unfortunately, the current OSN platforms has lack of functionalities for automatic users protection in initiating online friendship and online users interaction within their circle. Moreover, the users cannot analyze their associates' time-varying and changing behavior, which is a strong indicator of malicious activities. To address this issue, this paper proposes a design of automatic two-phase pre- and post-filtering approach with the objective of mitigating the cyber threats in OSN platforms. In the pre-filtering phase, we propose a method where each user can access a reliable technique to select a friend after getting his/her details authenticity. In the post-filtering phase, a control mechanism is established to recognize malicious posts/actions of the associates in order to limit the spread of the corresponding cyber threats. Our empirical analysis shows some significant comparison data towards hybrid (combined pre- and post-filtering) method, where the users' consents for our proposed method exceed significantly over the other competing techniques. In fact, the proportional mean value of the hybrid method is increased almost twice (2.03) of the existing methods.

Recent studies have proposed that traditional security technology -- involving pattern-matching algorithms that check predefined pattern sets of intrusion signatures -- should be replaced with sophisticated adaptive approaches that combine machine learning and behavioural analytics. However, machine learning is performance driven, and the high computational cost is incompatible with the limited computing power, memory capacity and energy resources of portable IoT-enabled devices. The convoluted nature of deep-structured machine learning means that such models also lack transparency and interpretability. The knowledge obtained by interpretable learners is critical in security software design. We therefore propose two novel models featuring a common Deep Extraction and Mutual Information Selection (DEMISe) element which extracts features using a deep-structured stacked autoencoder, prior to feature selection based on the amount of mutual information (MI) shared between each feature and the class label. An entropy-based tree wrapper is used to optimise the feature subsets identified by the DEMISe element, yielding the DEMISe with Tree Evaluation and Regression Detection (DETEReD) model. This affords 'white box' insight, and achieves a time to build of 603 seconds, a 99.07% detection rate, and 98.04% model accuracy. When tested against AWID, the best-referenced intrusion detection dataset, the new models achieved a test error comparable to or better than state-of-the-art machine-learning models, with a lower computational cost and higher levels of transparency and interpretability.

In this paper, we consider diffusion-based molecular communication with and without drift between two static nano-machines. We employ type-based information encoding, releasing a single molecule per information bit. At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules. In such systems, transposition errors fundamentally undermine reliability and capacity. Thus, in this paper, we study the impact of transpositions on the system performance. Toward this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift. Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP. Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.

This paper considers channel estimation for multiple-input multiple-output (MIMO) channels and revisits two competing concepts of including training data into the transmit signal, namely, orthogonal pilot (OP) that periodically transmits alternating pilot-data symbols, and superimposed pilot (SP) that overlays pilot-data symbols over time. We investigate rates achievable by both schemes when the channel undergoes time-selective bandlimited fading and analyze their behaviors with respect to the MIMO dimension and fading speed. By incorporating the multiple-antenna factors, we demonstrate that the widely known trend in which the OP is superior to the SP in the regimes of high signal-to-noise ratio (SNR) and slow fading, and vice versa, does not hold in general. As the number of transmit antennas (nt) increases, the range of operable fading speeds for the OP is significantly narrowed due to limited time resources for channel estimation and insufficient fading samples, which results in the SP being competitive in wider speed and SNR ranges. For a sufficiently small nt, we demonstrate that as the fading variation becomes slower, the estimation quality for the SP can be superior to that for the OP. In this case, the SP outperforms the OP in the slow-fading regime due to full utilization of time for data transmission.

We study transmission over multiple-input multiple-output block-fading channels with imperfect channel state information (CSI) at both the transmitter and receiver. In particular, based on mismatched decoding theory for a fixed channel realization, we investigate the largest achievable rates with independent and identically distributed inputs and the nearest neighbor decoder. We then study the corresponding information outage probability in the high signal-to-noise ratio (SNR) regime and analyze the interplay between estimation error variances at the transmitter and receiver to determine the optimal outage exponent, defined as the high-SNR slope of the outage probability plotted in a logarithmic-logarithmic scale against the SNR. We demonstrate that despite operating with imperfect CSI, power adaptation can offer substantial gains in terms of outage exponent.

We study transmission over multiple-antenna block-fading channels with imperfect channel state information at both the transmitter and receiver. Specifically, we investigate achievable rates based on the generalized mutual information. We then analyze the corresponding outage probability in the high signal-to-noise ratio regime.

This paper studies communication outages in multiple-input multiple-output (MIMO) block-fading channels with imperfect channel state information at the receiver (CSIR). Using mismatched decoding error exponents, we prove the achievability of the generalized outage probability, the probability that the generalized mutual information (GMI) is less than the data rate, and show that this probability is the fundamental limit for independent and identically distributed (i.i.d.) codebooks. Then, using nearest neighbor decoding, we study the generalized outage probability in the high signal-to-noise ratio (SNR) regime for random codes with Gaussian and discrete signal constellations. In particular, we study the SNR exponent, which is defined as the high-SNR slope of the error probability curve on a logarithmic-logarithmic scale. We show that the maximum achievable SNR exponent of the imperfect CSIR case is given by the SNR exponent of the perfect CSIR case times the minimum of one and the channel estimation error diversity. Random codes with Gaussian constellations achieve the optimal SNR exponent with finite block length as long as the block length is larger than a threshold. On the other hand, random codes with discrete constellations achieve the optimal SNR exponent with block length growing with the logarithm of the SNR. The results hold for many fading distributions, including Rayleigh, Rician, Nakagami-$m$, Nakagami-$q$and Weibull as well as for optical wireless scintillation distributions such as lognormal-Rice and gamma-gamma.

We study block-fading channels where both transmitter and receiver do not know the actual channel state information (CSI) but they have access to a noisy version. We study the interplay between estimation error variances at the transmitter and at the receiver to give the optimal outage exponents. We also demonstrate that achieving a reliable channel estimate at the receiver is more important than obtaining a reliable channel state information at the transmitter in terms of outage exponent.

We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-log-which is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinity-of non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels.

This paper presents the outage analysis of multiple-input multiple-output (MIMO) block-fading channels with nearest neighbour decoding and mismatched channel state information at the receiver (CSIR). Based on mismatched decoding arguments, we demonstrate the achievability of the generalised outage probability, the probability that the generalised mutual information (GMI) is less than the target data rate, and show that this probability is the fundamental limit for independent and identically distributed (i.i.d.) codebooks. We then analyse the behaviour of the generalised outage probability at high signal-to-noise ratio (SNR) regime. For both Gaussian and discrete signal codebooks, we provide a simple characterisation of the mismatched CSIR SNR exponents and derive the necessary condition on the block length to achieve those SNR exponents.

We investigate the effects of imperfect channel knowledge and feedback in incremental-redundancy automatic-repeat request (INR-ARQ) coding systems over multiple-input multiple-output (MIMO) block-fading channels. We propose an ARQ decoder based on nearest neighbour decoding and evaluate the corresponding achievable rates. We then derive the optimal code diversity assuming that the feedback channel is modelled as a binary symmetric channel. Our main results show that the feedback link reliability must improve with the forward (transmission) signal-to-noise ratio (SNR) for the code to exploit the diversity offered by ARQ scheme. We also identify the conditions for achieving full diversity and for ARQ not helping in improving the system's diversity.

In this paper, an adaptive window length estimation for channel estimation in direct-sequence (DS) code-division multiple-access (CDMA) receivers is presented. The estimated optimum window length is derived based on autocovariance analysis of the estimated channel coefficients. As the propagation channel changes over time, an adaptive algorithm for updating the window length is proposed through Doppler and signal-to-noise ratio (SNR) estimations. The proposed system improves the performance by adaptively selecting and continuously updating the window lengths over a wide-range of velocities (5 to 400 kmph).