Two major factors affecting mobile network performance are mobility and traffic patterns. Simulations and analytical-based performance evaluations rely on models to approximate factors affecting the network. Hence, the understanding of mobility and traffic is imperative to the effective evaluation and efficient design of future mobile networks. Current models target either mobility or traffic, but do not capture their interplay. Many trace-based mobility models have largely used pre-smartphone datasets (e.g., AP-logs), or much coarser granularity (e.g., cell-towers) traces. This raises questions regarding the relevance of existing models, and motivates our study to revisit this area. In this study, we conduct a multidimensional analysis, to quantitatively characterize mobility and traffic spatio-temporal patterns, for laptops and smartphones, leading to a detailed integrated mobility-traffic analysis. Our study is data-driven, as we collect and mine capacious datasets (with 30TB, 300k devices) that capture all of these dimensions. The investigation is performed using our systematic (FLAMeS) framework. Overall, dozens of mobility and traffic features have been analyzed. The insights and lessons learnt serve as guidelines and a first step towards future integrated mobility-traffic models. In addition, our work acts as a stepping-stone towards a richer, more-realistic suite of mobile test scenarios and benchmarks.1 Throughout, we use flutes for smartphones, and cellos for laptops.
Despite a rapid increase of public interest for electric mobility, several factors still impede Battery Electric Vehicles’ (BEVs) acceptance. These factors include their limited range and inconvenient charging. For mitigating these limitations to users, certain BEV-specific services are required. Therefore, such services provide a reliable range prediction and routing, including charging-stop planning. The basis of these services is a precise and reliable Energy Demand (ED) prediction. For that matter, aggregated fleet-vehicle data combined with map-specific data (e.g., road slope) form an energetic map, which can serve for precise ED predictions. However, data coverage is paramount for these predictions, more specifically regarding gapless energetic maps. This work aims to eliminate the energetic map’s gaps using two Machine Learning (ML) approaches: regression and classification. The proposed ML solution builds upon the synergy between map-information and crowdsourced driving profiles of 4.6 million kilometres of training and test traces. For evaluation, two test-scenarios capture the models’ performance for the analysed problem in two perspectives. First, we evaluate our ML models, followed by the problem-specific energetic evaluation perspective for better interpretability. From the latter, the results indicate energetic map data imputation performs promisingly better when using the regression instead of the classification model.
or tablets. One major problem is how to scale communication over the limited wireless spectrum. Wi-Fi and Bluetooth often interfere with each other in densely deployed IoT networks. We can utilize emerging communication mechanisms such as Visible Light Communication (VLC) and ultrasound to bypass wireless interference. Combined with a smart IoT device management platform [3], we can orchestrate different IoT and edge devices to fully leverage wireless technologies. For instance, when detecting jamming condition of Wi-Fi channels, switch to VLC for data transmission. Thereby, we are able to enhance network performance and save energy by avoiding redundant transmissions. A unique property of VLC and ultrasound is that the communication range is naturally restricted by territorial obstacles, thus providing the basis for distance-bounding services. A distance-bounding service ensures an upper distance limit between sender and receiver. For example, seamless car entry systems verify if the car's key is within a certain distance, otherwise the doors cannot be opened and the engine cannot be started. In contrast, mid-range radio-based communications like Bluetooth or Wi-Fi cause additional overhead to measure the round trip time between sender and receiver and estimate the distance between them. Due to the limited communication distance, visible light and ultrasound can help enhancing privacy and security of IoT communications where their data exchange can be easily restricted through obstacles like doors, walls, and windows. Radio waves penetrate such spatial barriers and are hence exposed to eavesdropping and interception attacks. From a deployability perspective, ultrasound is easy to deploy and flexible owing to wide support by off-the-shelf smartphones. VLC has also seen significant advances such as the open-source platform OpenVLC [4]. In this work, we exploit emerging communication technologies, VLC, and ultrasound, to utilize the advantages of different electromagnetic spectrum for enhancing indoor IoT communication. In Section II, we analyze user mobility in terms of required transmission distance and compare different wireless communication technologies regarding their suitability for indoor IoT communication. In addition, we highlight use cases for VLC and ultrasound communication in Section III. Besides that, Section IV provides details of our VLC and ultrasound communication modules and we Abstract-The number of deployed Internet of Things (IoT) devices is steadily increasing to manage and interact with community assets of smart cities, such as transportation systems and power plants. This may lead to degraded network performance due to the growing amount of network traffic a nd connections generated by various IoT devices. To tackle these issues, one promising direction is to leverage the physical proximity of communicating devices and inter-device communication to achieve low latency, bandwidth efficiency, a nd r esilient services. In this work, we aim at enhancing the performance of indoor IoT communicat...
Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus -a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by ∼4× and the energy consumption by ∼77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.
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