Low Traffic Periods Research Articles

On Vehicular Data Aggregation in Federated Learning

Vehicular federated learning systems will be beneficial to predicting traffic events in future intelligent cities. However, they might leak private information upon model updates. Hence, an honest but curious server could infer private information, such as the route of a vehicle. In this study, we elaborate on the nature of such privacy leakage caused by gradient sharing. With a simulated scenario, we focus on determining who is in danger of privacy threats and how successful a route inference attack can be. Results indicate that vanilla federated learning exposes intra-city and commuter traffic to successful location inference attacks. We also found that an adversarial aggregator server successfully infers the moving time of vehicles traveling during low-traffic periods.

Trading Off Delay and Energy Saving Through Advanced Sleep Modes in 5G RANs

While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.

Modeling User Transfer During Dynamic Carrier Shutdown in Green 5G Networks

The energy consumption of the fifth generation (5G) of cellular technology is concerning for the mobile industry and the entire society. To minimize the environmental footprint and economic costs of 5G, it is necessary to adapt the transmission capabilities of networks to end-users’ quality of service requirements. In this paper, we focus on the carrier shutdown approach that enables a base station (BS) to autonomously switch off during low traffic periods, by transferring its load to neighbouring active BSs. More specifically, we propose a data-driven framework, constructed through real network measurements, which statistically characterizes the user equipment (UE) transfer across neighbouring BSs, when carrier shutdown operates. The implementation of this framework allows the 5G system to determine a poor load distribution due to energy saving mechanisms, prevent drastic reductions in UE performance, and ultimately estimate energy savings when activating carrier shutdown.

Double Auction Offloading for Energy and Cost Efficient Wireless Networks

Network infrastructure sharing and mobile traffic offloading are promising technologies for Heterogeneous Networks (HetNets) to provide energy and cost effective services. In order to decrease the energy requirements and the capital and operational expenditures, Mobile Network Operators (MNOs) and third parties cooperate dynamically with changing roles leading to a novel market model, where innovative challenges are introduced. In this paper, a novel resource sharing and offloading algorithm is introduced based on a double auction mechanism where MNOs and third parties buy and sell capacity and roam their traffic among each other. For low traffic periods, Base Stations (BSs) and Small Cells (SCs) can even be switched off in order to gain even more in energy and cost. Due to the complexity of the scenario, we adopt the multi-objective optimization theory to capture the conflicting interests of the participating entities and we design an iterative double auction algorithm that ensures the efficient operation of the market. Additionally, the selection of the appropriate time periods to apply the proposed algorithm is of great importance. Thus, we propose a machine learning technique for traffic load prediction and for the selection of the most effective time periods to offload traffic and switch off the Base Stations. Analytical and experimental results are presented to assess the performance of the algorithm.

Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection.

Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.

Local Traffic-aware Green Algorithm based on Sleep-scheduling in autonomous networks

Over recent years, green communication has been investigated to reduce the economic costs and environmental pollution caused by network infrastructures. There exist many approaches to conserve energy in autonomous networks while according to over-provisioning of networking resources for peak traffic periods, sleep-scheduling based approach is one of effective methods between them. In this regards, in this paper, a new Local Traffic-aware Green Algorithm based on Sleep-scheduling (LTGAS) is proposed in autonomous wired networks in order to reduce energy consumption during low traffic periods. This proposal is a dynamic solution that considers online and local traffic knowledge to switch off some underutilized network nodes and links during low traffic periods and re-switch on some sleep devices in congestion situations. In addition, LTGAS is executed in fully distributed mode without any centralized controller and uses only information provided by link-state protocols like OSPF. Moreover, this solution does not have to gather global traffic knowledge of network during a sleep-scheduling decision, resulting in low complexity and low network overhead. The results obtained by the real network scenario show that LTGAS switches off up to 38% of network nodes and up to 96% of the maximum number of switchable links during low traffic periods. In addition, LTGAS has a low effect on network performance metrics, including end-to-end delay, packet delivery ratio and average link utilization, compared with similar proposals while switches off more number of network devices rather than other approaches.

Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs

Safety application systems in Vehicular Ad-hoc Networks (VANETs) require the dissemination of contextual information about the scale of neighbouring vehicles; therefore, ensuring security and privacy is of utmost importance. Vulnerabilities in the messages and the system’s infrastructure introduce the potential for attacks that lessen safety and weaken passengers’ privacy. The purpose of short-lived anonymous identities, called “pseudo-identities”, is to divide the trip into unlinkable short passages. Researchers have proposed changing pseudo-identities more frequently inside a pre-defined area, called a cryptographic mix-zone (CMIX) to ensure enhanced protection. According to ETSI ITS technical report recommendations, the researchers must consider the low-density scenarios to achieve unlinkability in CMIX. Recently, Christian et al. proposed a Chaff-based CMIX scheme that sends fake messages under the consideration of low-density conditions to enhance vehicles’ privacy and confuse attackers. To accomplish full unlinkability, in this paper, we first show the following security and privacy vulnerabilities in the Christian et al. scheme: Linkability attacks outside the CMIX may occur due to deterministic data sharing during the authentication phase (e.g., duplicate certificates for each communication). Adversaries may inject fake certificates, which breaks Cuckoo Filters’ (CFs) updates authenticity, and the injection may be deniable. CMIX symmetric key leakage outside the coverage may occur. We propose a VPKI-based protocol to mitigate these issues. First, we use a modified version of Wang et al.’s scheme to provide mutual authentication without revealing the real identity. To this end, the messages of a vehicle are signed with a different pseudo-identity “certificate”. Furthermore, the density is increased via the sending of fake messages in low traffic periods to provide unlinkability outside the mix-zone. Second, unlike Christian et al.’s scheme, we use the Adaptive Cuckoo Filter (ACF) instead of CF to overcome the false positives’ effect on the whole filter. Moreover, to prevent any alteration of the ACFs, only RUSs distribute the updates, and they sign the new fingerprints. Third, the mutual authentication prevents any leakage from the mix zones’ symmetric keys by generating a fresh one for each communication through a Diffie–Hellman key exchange.

Feasibility study on applying continuous descent operations in congested airspace with speed control functionality: Fixed flight-path angle descent

Continuous descent operations (CDO) are an efficient descent operation for passenger/cargo aircraft and implemented at various airports worldwide. However, it is difficult for air traffic controller (ATCo) to predict the CDO trajectory and control the CDO aircraft, therefore limiting the applicable window only during the low traffic periods. To maximize potential benefits by introducing the CDO, it is required to implement the CDO into congested airspace and time window. This study introduces an augmentation to CDO application in the congested airspace, called fixed flight-path angle (Fixed-FPA) descent. The fixed-FPA descent was originally proposed and investigated only for small passenger jet aircraft; thereafter the fixed-FPA descent was introduced to large passenger jet aircraft. This proposed procedure consists of several altitude restrictions along the arrival route. By following the altitude restrictions, the aircraft flies with a specified flight-path angle. Additionally, specifying the flight-path angle facilitates the prediction of the CDO trajectory by ATCo. Moreover, by setting a shallower flight-path angle compared with the conventional CDO, the fixed-FPA descent is capable of speed control. The preceding studies conducted only the preliminary feasibility study of the fixed-FPA descent on large jet aircraft. In this study, a series of full-flight simulator experiments and Monte Carlo simulations were conducted to assess the operational feasibility and demonstrate the capabilities of fixed-FPA descent. Experimental results demonstrated that the fixed-FPA descent meets the requirements for integrating the CDO into congested airspace, such that the vertical path of the CDO can be predicted by the ATCo, and the speed of the CDO aircraft is controllable. Additionally, the simulation results revealed that the fixed-FPA descent has better fuel efficiency than the conventional when the descending aircraft is required to delay its arrival time.

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

The combination of user-centric network densification and distributed massive multiple-input multiple-output (MIMO) operation has recently brought along a new paradigm in the wireless communications arena, referred to as cell-free massive MIMO networking. In these networks, a large number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of mobile stations (MSs) in the same time/frequency resource. Similar to what has been traditionally done with conventional cellular networks, cell-free massive MIMO networks will be dimensioned to provide the required quality of service (QoS) to MSs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to an inefficient use of both spectral and energy resources. Aiming at the implementation of green cell-free massive MIMO networks, this paper proposes and analyzes the performance of different AP switch ON/OFF (ASO) strategies designed to dynamically turn ON/OFF some of the APs based on the number and/or location of the active MSs in the network. The proposed framework considers line-of-sight (LOS) and non-line-of-sight (NLOS) links between APs and MSs, the use of different antenna array architectures at the access points (APs), suitably characterized by array-dependent spatial correlation matrices, and specific power consumption models for APs, MSs and fronthaul links between the APs and the CPU. Numerical results show that the use of properly designed ASO strategies in cell-free massive MIMO networks clearly improve the achievable energy efficiency. Moreover, they also reveal the existing trade-offs among the achievable energy efficiency, the available network-state information, and the hardware configuration (i.e., number of APs, number of transmit antennas per AP, and number of MSs).

Energy-Efficient Access-Point Sleep-Mode Techniques for Cell-Free mmWave Massive MIMO Networks With Non-Uniform Spatial Traffic Density

Cell-free massive multiple-input multiple-output (MIMO) is a novel beyond 5G (B5G) and 6G paradigm that, through the use of a common central processing unit (CPU), coordinates a large number of distributed access points (APs) to coherently serve mobile stations (MSs) on the same time/frequency resource. By exploiting the characteristics of new less-congested millimeter wave (mmWave) frequency bands, these networks can improve the overall system spectral and energy efficiencies by using low-complexity hybrid precoders/decoders. For this purpose, the system must be correctly dimensioned to provide the required quality of service (QoS) to MSs under different traffic load conditions. However, only heavy traffic load conditions are usually taken into account when analysing these networks and, thus, many APs might be underutilized during low traffic load periods, leading to an inefficient use of resources and waste of energy. Aiming at the implementation of energy-efficient AP switch on/off strategies, several approaches have been proposed in the literature that only consider rather unrealistic uniform spatial traffic distribution in the whole coverage area. Unlike prior works, this paper proposes energy efficient AP sleep-mode techniques for cell-free mmWave massive MIMO networks that are able to capture the inhomogeneous nature of spatial traffic distribution in realistic wireless networks. The proposed framework considers, analyzes and compares different AP switch ON-OFF (ASO) strategies that, based on the use of goodness-of-fit (GoF) tests, are specifically designed to dynamically turn on/off APs to adapt to both the number and the statistical distribution of MSs in the network. Numerical results show that the use of properly designed GoF-based ASO strategies under a non-uniform spatial traffic distribution can serve to considerably improve the achievable energy efficiency.

Coded Caching for Heterogeneous Systems: An Optimization Perspective

In cache-aided networks, the server populates the cache memories at the users during low-traffic periods in order to reduce the delivery load during peak-traffic hours. In turn, there exists a fundamental tradeoff between the delivery load on the server and the cache sizes at the users. In this paper, we study this tradeoff in a multicast network, where the server is connected to users with unequal cache sizes and the number of users is less than or equal to the number of library files. We propose centralized uncoded placement and linear delivery schemes which are optimized by solving a linear program. Additionally, we derive a lower bound on the delivery memory tradeoff with uncoded placement that accounts for the heterogeneity in cache sizes. We explicitly characterize this tradeoff for the case of three end-users, as well as an arbitrary number of end-users when the total memory size at the users is small, and when it is large. Next, we consider a system where the server is connected to the users via rate-limited links of different capacities and the server assigns the users’ cache sizes subject to a total cache budget. We characterize the optimal cache sizes that minimize the delivery completion time with uncoded placement and linear delivery. In particular, the optimal memory allocation balances between assigning larger cache sizes to users with low capacity links and uniform memory allocation.

Online Learning for Energy Saving and Interference Coordination in HetNets

In heterogeneous cellular networks (HetNets), switching OFF small cells under low user traffic periods has been proved to be an effective energy saving strategy. However, this strategy has strong interactions with interference coordination (IC) mechanisms, making it convenient to address both tasks simultaneously. The motivation of this paper is to develop a self-optimization algorithm capable of jointly controlling energy saving and IC mechanisms using an online learning approach. Our proposal is based on a contextual bandit formulation that, among other challenges, implies discovering the most energy-efficient control actions while satisfying a predefined level of Quality of Service (QoS) for the users. We propose a two-level framework comprising a global controller, in charge of a group of macro cells, and multiple local controllers, one per macro cell. The global controller implements a novel algorithm, referred to as the Bayesian Response Estimation and Threshold Search (BRETS), that is capable of learning, for each control action, its feasibility boundaries in terms of QoS and its energy consumption as a function of the aggregated user traffic. The algorithm comes with a bound on its expected convergence time. The local controllers translate the control actions learned by the global controller into local decisions. Our numerical results show that BRETS is only 1% less efficient than an ideal oracle policy, clearly outperforming other benchmark algorithms.

Concrete Modular Pavements – Types, Issues And Challenges

According to the European Asphalt Pavement Association, more than 90 per cent of the European road network is paved with asphalt. Constantly increasing traffic volume and climate change accelerate deterioration of current pavements. As a result, there arises a need to rehabilitate them prematurely. Repair and rehabilitation work lead to traffic congestion, which is one of the most significant concerns in highly trafficked roads and urban streets. Concrete modular pavements consisting of precast concrete slabs are a reasonable solution to deal with the road works since their construction, as well as repair, is time-saving. Repair works typically are implemented during a low traffic period (usually at night). A primary purpose of concrete modular pavements is heavily trafficked roads and other transport areas. This paper focuses on concrete modular pavements, their types, issues and challenges related to their design, slab fabrication and pavement construction. The conducted analysis revealed 15 different types of concrete modular pavements that differ from the techniques of slab joints and load transfer between the adjacent slabs. More than 20 issues and challenges related to the design of modular elements, slab fabrication and pavement construction were identified. Finally, the existing practice of concrete modular pavements was summarised and the gaps of scientific knowledge, as well as a need for comprehensive research, were defined.

Coded Caching and Content Delivery With Heterogeneous Distortion Requirements

Cache-aided coded content delivery is studied for devices with diverse quality-of-service requirements, specified by a different average distortion target. The network consists of a server holding a database of independent contents, and users equipped with local caches of different capacities. User caches are filled by the server during a low traffic period without the knowledge of particular user demands. As opposed to the current literature, which assumes that the users request files in their entirety, it is assumed that the users in the system have distinct distortion requirements; and therefore, each user requests a single file from the database to be served at a different distortion level. Our goal in this paper is to characterize the minimum delivery rate the server needs to transmit over an error-free shared link to satisfy all possible demand combinations at the requested distortion levels, considering both centralized and decentralized cache placement. For centralized cache placement, the optimal delivery rate is characterized for the two-file two-user scenario for any pair of target distortion requirements, when the underlying source distribution is successively refinable. For the two-user scenario with more than two successively refinable files, the optimal scheme is characterized when the cache capacities of the users are the same and the number of files is a multiple of 3. For the general source distribution, not necessarily successively refinable, and with arbitrary number of users and files, a layered caching and delivery scheme is proposed, assuming that scalable source coding is employed at the server. This allows dividing the problem into two subproblems: the lossless caching of each layer with heterogeneous cache sizes, and cache allocation among layers. A delivery rate minimization problem is formulated and solved numerically for each layer; while two different schemes are proposed to allocate user caches among layers, namely, proportional cache allocation and ordered cache allocation . A decentralized lossy coded caching scheme is also proposed, and its delivery rate performance is studied. Simulation results validate the effectiveness of the proposed schemes in both settings.

Cross-layer model design in wireless ad hoc networks for the Internet of Things.

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods.

A Dynamic traffic-aware energy-efficient algorithm based on sleep-scheduling for autonomous systems

Over recent years, energy conservation of Internet infrastructure has attracted a great deal of attention to reduce the economical cost and environmental pollution produced by networking sectors. Due to over-provisioning of networking resources for peak traffic periods, an important proportion of these resources may remain under-utilized in low traffic periods. In this paper, we propose an online green algorithm which switches off some extra nodes and links in order to conserve energy while guaranteeing the network performance. To achieve this, nodes with less occurrence in the shortest path tree and their connected links are selected to be switched-off if their utilization is not more than the average network utilization. Due to the dynamic nature of network traffic, our solution is equipped with a dynamic engine which re-switches on some nodes and links in higher traffic loads. The proposed approach does not require any centralized controller for switching off devices and uses a link-state protocol like OSPF to share required information with minimal overhead. Simulation results obtained using a real network topology show that our proposal switches off up to 28% of network nodes and up to 74% of the maximum number of switchable links with low effect in network performance.

Traffic Noise Influence on Soundscape Quality At Campus Landscape Area

Campuses located in urban areas are prone to issues such as high traffic and noise pollution which can affect both the indoor and outdoor learning environments. Environmental experiences involving the perception of positive and negative sounds relate to the study of soundscape. This paper presents a soundscape assessment using two physical approaches of site observation and sound measurement. Two landscape areas in University of Malaya (UM) campus were selected: the parcourse area (Site I) and the water feature area (Site II). The objectives are to investigate the level of sound quality and to identify the connection between the soundscape and the landscape elements. During site observation, the human sound of Site I, water sound of Site II and vehicular sounds characterize the selected sites. Both sites sound pressure level were at 50 dBA and above during low traffic conditions, and a critical level of over 60 dBA during high traffic conditions. This current sound level exceeded the permissible level for institutions of 50 dBA by the Department of Environment. Sounds generated by the water feature and from human activities do influence the soundscape during low traffic period but insignificant during high traffic period. It is important to take into consideration on the factor of location, surrounding context, landscape elements and vegetation when creating a landscape area for the community in the campus. It is hoped that the findings will provide fundamental data for future improvement and development of UM campus‟ landscape areas.

Joint Demand-Side Management in Smart Grid for Green Collaborative Mobile Operators Under Dynamic Pricing and Fairness Setup

In this paper, the interactions between multiple mobile operators, owning heterogeneous cellular networks, and energy retailers existing in the smart grid are investigated. Energy procurement decisions of the cellular networks sharing common energy sources are jointly optimized while considering both dynamic energy pricing and varying pollution levels of energy sources. The objective is to enable collaboration among mobile operators in their demand-side management (DSM) to achieve economical and environmental goals. This is performed by solving a unified optimization problem aiming at maximizing a metric based on mobile operators’ profits while limiting the amount of carbon dioxide (CO2) emitted by all networks. In this paper, three utility metrics, namely, Sum, Max-min, and Proportional Fair utilities, are considered to reflect the degree of fairness among mobile operators in the optimized DSM. Closed-form expressions of the optimal procured energy from each retailer used to power the marcocell and active small cell base stations (BSs) are derived for pricing profiles and utility metrics. Furthermore, the BS sleeping strategy is applied, in a centralized or a decentralized manner, in order to reduce the network energy consumption during low traffic periods. Finally, the impact of renewable energy generation uncertainty on the energy procurement decision is examined. The behavior of the different actors in the energy procurement decision is investigated through simulations. Results show that significant CO2 emissions reduction can be achieved thanks to the optimized DSM and the BS sleeping strategy.

Fundamental Limits of Coded Caching: Improved Delivery Rate-Cache Capacity Tradeoff

A centralized coded caching system, consisting of a server delivering $N$ popular files, each of size $F$ bits, to $K$ users through an error-free shared link, is considered. It is assumed that each user is equipped with a local cache memory with capacity $MF$ bits, and contents can be proactively cached into these caches over a low traffic period, however, without the knowledge of the user demands. During the peak traffic period, each user requests a single file from the server. The goal is to minimize the number of bits delivered by the server over the shared link, known as the delivery rate , over all user demand combinations. A novel coded caching scheme for the cache capacity of $M= (N-1)/K$ is proposed. It is shown that the proposed scheme achieves a smaller delivery rate than the existing coded caching schemes in the literature, when $K > N \ge 3$ . Furthermore, we argue that the delivery rate of the proposed scheme is within a constant multiplicative factor of 2 of the optimal delivery rate for cache capacities $1/K \le M \le (N-1)/K$ , when $K > N \ge 3$ .

Migration Energy Aware Reconfigurations of Virtual Network Function Instances in NFV Architectures

Network function virtualization (NFV) is a new network architecture framework that implements network functions in software running on a pool of shared commodity servers. NFV can provide the infrastructure flexibility and agility needed to successfully compete in today’s evolving communications landscape. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFIs) that are software modules executed on virtual machines. This paper deals with the migration problem of the VNFIs needed in the low traffic periods to turn OFF servers and consequently to save energy consumption. Though the consolidation allows for energy saving, it has also negative effects as the quality of service degradation or the energy consumption needed for moving the memories associated to the VNFI to be migrated. We focus on cold migration in which virtual machines are redundant and suspended before performing migration. We propose a migration policy that determines when and where to migrate VNFI in response to changes to SFC request intensity. The objective is to minimize the total energy consumption given by the sum of the consolidation and migration energies. We formulate the energy aware VNFI migration problem and after proving that it is NP-hard, we propose a heuristic based on the Viterbi algorithm able to determine the migration policy with low computational complexity. The results obtained by the proposed heuristic show how the introduced policy allows for a reduction of the migration energy and consequently lower total energy consumption with respect to the traditional policies. The energy saving can be on the order of 40% with respect to a policy in which migration is not performed.