Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields

Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction

Effective long-term predictions have been increasingly demanded in urban-wise data mining systems. Many practical applications, such as accident prevention and resource pre-allocation, require an extended period for preparation. However, challenges come as long-term prediction is highly error-sensitive, which becomes more critical when predicting urban-wise phenomena with complicated and dynamic spatial-temporal correlation. Specifically, since the amount of valuable correlation is limited, enormous irrelevant features introduce noises that trigger increased prediction errors. Besides, after each time step, the errors can traverse through the correlations and reach the spatial-temporal positions in every future prediction, leading to significant error propagation. To address these issues, we propose a Dynamic Switch-Attention Network (DSAN) with a novel Multi-Space Attention (MSA) mechanism that measures the correlations between inputs and outputs explicitly. To filter out irrelevant noises and alleviate the error propagation, DSAN dynamically extracts valuable information by applying self-attention over the noisy input and bridges each output directly to the purified inputs via implementing a switch-attention mechanism. Through extensive experiments on two spatial-temporal prediction tasks, we demonstrate the superior advantage of DSAN in both short-term and long-term predictions.Comment: 11 pages, an ACM SIGKDD 2020 pape

Deep Learning and Bayesian Calibration Approach to Hourly Passenger Occupancy Prediction in Beijing Metro: A Study Exploiting Cellular Data and Metro Conditions

In In burgeoning urban landscapes, the proliferation of the populace necessitates swift and accurate urban transit solutions to cater to the citizens' commuting requirements. A pivotal aspect of fostering optimized traffic management and ensuring resilient responses to unanticipated passenger surges is precisely forecasting hourly occupancy levels within urban subway systems. This study embarks on delineating a two-tiered model designed to address this imperative adeptly: 1. Preliminary Phase - Employing a Feed Forward Neural Network (FFNN): In the initial phase, a Feed Forward Neural Network (FFNN) is employed to gauge the occupancy levels across various subway stations. The FFNN, a class of artificial neural networks, is well-suited for this task because it can learn from the data and make predictions or decisions without being explicitly programmed to perform the task. Through a series of interconnected nodes, known as neurons, arranged in layers, the FFNN processes the input data, adjusts its weights based on the error of its predictions, and optimizes the network for accurate forecasting. For the random process of occupation levels in time and space, this phase encapsulates the so-called process filtration, wherein the underlying patterns and dynamics of subway occupancy are captured and represented in a structured format, ready for subsequent analysis. The estimates garnered from this phase are pivotal and form the foundation for the subsequent modelling stage. 2. Subsequent Phase - Implementing a Bayesian Proportional-Odds Model with Hourly Random Effects: With the estimates from the FFNN at disposal, the study transitions to the subsequent phase wherein a Bayesian Proportional-Odds Model is utilized. This model is particularly adept for scenarios where the response variable is ordinal, as in the case of occupancy levels (Low, Medium, High). The Bayesian framework, underpinned by the principles of probability, facilitates the incorporation of prior probabilities on model parameters and updates this knowledge with observed data to make informed predictions. The unique feature of this model is the incorporation of a random effect for hours, which acknowledges the inherent variability across different hours of the day. This is paramount in urban transit systems where passenger influx varies significantly with the hour. The synergy of these two models facilitates calibrated estimations of occupancy levels, both conditionally (relative to the sample) and unconditionally (on a detached test set). This dual-phase methodology furnishes analysts with a robust and reliable insight into the quality of predictions propounded by this model. This, in turn, avails a data-driven foundation for making informed decisions in real-time traffic management, emergency response planning, and overall operational optimization of urban subway systems. The model expounded in this study is presently under scrutiny for potential deployment by the Beijing Metro Group Ltd. This initiative reflects a practical stride towards embracing sophisticated analytical models to ameliorate urban transit management, thereby contributing to the broader objective of fostering sustainable and efficient urban living environments amidst the surging urban populace

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area

Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving