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Fair Cellular Throughput Optimization with the Aid of Coordinated Drones
Get PDFThe interest on flexible air-to-ground channels from aerial base stations to enhance users access by seeking good line-of-sight connectivity from the air has increased in the past years. In this paper, we propose a deployable analytical framework for the 3-D placement of a fleet of coordinated drone relay stations to optimize network capacity according to α-fairness metrics. We formulate a mixed-integer non-convex program, which results to be intractable. Therefore, we design a near-optimal heuristic that can solve the problem in real-time applications. We assess the performance of our proposal by simulation, using a realistic urban topology, and study pros and cons of using drone relay stations in both static and dynamic scenarios, when popular events gather masses of users in limited areas.TRUEpu
CrowdSenSim 2.0: A Stateful Simulation Platform for Mobile Crowdsensing in Smart Cities
Get PDFMobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Typically, a large number of participants is required to make a sensing campaign successful. For such a reason, it is often not practical for researchers to build and deploy large testbeds to assess the performance of frameworks and algorithms for data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we present CrowdSenSim 2.0, a significant extension of the popular CrowdSenSim simulation platform. CrowdSenSim 2.0 features a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms. All these improvements boost the performances of the simulator and make the runtime execution and memory utilization significantly lower, also enabling the support for larger simulation scenarios. We demonstrate retro-compatibility with the older platform and evaluate as a case study a stateful data collection algorithm.TRUEpu
Performance Evaluation of Single Base Station ToA-AoA Localization in an LTE Testbed
Get PDFPrecise localization is becoming an integral part of mobile network architectures, not only to provide location-based services but also to optimize the operation of the network itself through suitable context information. Location systems are of particular importance for indoor settings where GPS may be unavailable. While upcoming 5G systems will provide improved location accuracy, for a long time to come many areas will only have LTE coverage, and ubiquitous localization will thus also have to rely on LTE technology. To evaluate the location accuracy that can be achieved with current mobile systems, we implement a localization algorithm in a standard-compliant LTE testbed based on software-defined radios. We assess the localization accuracy in representative indoor scenarios. Despite a bandwidth of only 20MHz, the results show a good median error around 2m, but significantly larger errors may occur in non-line-of-sight cases. Nevertheless, the accuracy is sufficient for a range of potential applications.TRUEpu
Dynamic Vehicle Path-Planning in the Presence of Traffic Events
Get PDFAdvanced Driver Assistance Systems require atremendous amount of sensor information to support the driver’scomfort and safety. In particular, systems that provide (good)route options to a vehicle rely on information, such as traffic jamsand road blockages, which is sensed by other (possibly distant)vehicles and distributed by a central server. This information isclearly dynamic and may be invalid by the time the vehicle arrivesat the affected location. In this work, we develop an innovativeapproach to determine optimal routes (minimizing the costs liketravel-time to their destination) for vehicles whose original routeis adversely impacted by a (severe) road event. The route is inprinciple reassessed just before each upcoming road intersection(decision-point), considering updated information about the roadevent and an estimate of the remaining lifetime of the road event.A set of recursive equations is developed that yields the optimaldecision for each vehicle at each decision-point, accounting foraspects such as the vehicle’s destination, driver characteristics,etc. In practice, the decision may be taken by the vehicle itself (ifall the needed information is transferred to it), or by the remoteserver and be communicated to the vehicle (if all needed privatevehicle information is transferred to the server). A discussion ispresented, along with some ideas, their assessment, and associatedtradeoffs, aiming at reducing communications costs. Simulationsshow that our approach adapts to the considered event andfinds routes of similar quality as a full-knowledge approach withlimited communication overhead.TRUEpu
Atomic Appends: Selling Cars and Coordinating Armies with Multiple Distributed Ledgers
Get PDFThe various applications using Distributed Ledger Technologies (DLT) or blockchains, have led to the introduction of a new `marketplace' where multiple types of digital assets may be exchanged. As each blockchain is designed to support specific types of assets and transactions, and no blockchain will prevail, the need to perform interblockchain transactions is already pressing.
In this work we examine the fundamental problem of interoperable and interconnected blockchains. In particular, we begin by introducing the Multi-Distributed Ledger Objects (MDLO), which is the result of aggregating multiple Distributed Ledger Objects -- DLO (a DLO is a formalization of the blockchain) and that supports append and get operations of records (e.g., transactions) in them from multiple clients concurrently. Next, we define the AtomicAppends problem, which emerges when the exchange of digital assets between multiple clients may involve appending records in more than one DLO. Specifically, AtomicAppend requires that either all records will be appended on the involved DLOs or none. We examine the solvability of this problem assuming rational and risk-averse clients that may fail by crashing, and under different client utility and append models, timing models, and client failure scenarios. We show that for some cases the existence of an intermediary is necessary for the problem solution. We propose the implementation of such intermediary over a specialized blockchain, we term Smart DLO (SDLO), and we show how this can be used to solve the AtomicAppends problem even in an asynchronous, client competitive environment, where all the clients may crash.TRUEpu
DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning
Get PDFNetwork slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator's desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog's tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-the-art mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.TRUEpu
A Comprehensive Study of Low Frequency and High Frequency Channel Correlation
Get PDFTo meet the increasing throughput demand due to the proliferation of data-hungry services and applications, 5G
technologies are adopting innovative solutions, such as using high frequency (HF) bands from 20 to 300GHz. While these bands provide more bandwidth and thus higher data rates, this comes at the price of higher propagation and penetration losses, making reliable communication more challenging in some scenarios. The characteristics of low frequency (LF) and HF bands are complementary, and multiband systems that combine the advantages of both types of bands are highly promising. In such a case, estimating the channel of one band given an prior channel measurement of the other band helps to reduce channel measurement overhead, provided the bands are correlated. In this work, we present a detailed study of the correlation between HF and LF bands. The existence of this correlation makes possible to reduce the overhead of expensive operations such as millimeter-
wave beam steering to one third of its original time with an
average loss rate SNR of less than 3dB.TRUEpu
Towards mobile radio access infrastructures for mobile users
Get PDFThis paper provides a first investigation of twice-mobile networks, i.e., cellular networks where both the end users and (part of) the radio access network infrastructure are mobile. Twice-mobile networks are based on an opportunistic, dense, crowdsourced, random deployment of mobile small cell base stations carried by vehicles, and on millimetre-wave backhaul connections between the mobile small cell base stations and the fixed network elements. Thanks to the fact that vehicles carrying mobile small cell base stations roam coherently with mobile subscribers, twice-mobile networks provide adaptive broadband wireless capacity where and when users need it, thus avoiding the cost and intrinsic inefficiency of dense deployments of fixed small cell base stations. In this paper we investigate the achievable capacity under the twice-mobile network paradigm, using real-world telecom traffic and vehicle positions in two case studies in Milan, Italy. Our results show that, thanks to positive spatial correlations between mobile net- work demands and road traffic, mobile small cell base stations carried by vehicles ensure performance equivalent or better to that of a traditional deployment of fixed small cells, at significantly lower cost.pu