9,236 research outputs found
WiFi Hot Spot Service Business for the Automotive and Oil Industries: A Competitive Analysis
While you refuel for gas, why not refuel for information or upload vehicle data, using a cheap wireless technology as WiFi? This paper analyzes in extensive detail the user segmentation by vehicle usage, service offering, and full business models from WiFi hot spot services delivered to and from vehicles (private, professional, public) around gas stations. Are also analyzed the parties which play a role in such services: authorization, provisioning and delivery, with all the dependencies modelled by attributed digraphs. Account is made of WiFi base station technical capabilities and costs. Five year financial models (CAPEX, OPEX), and data pertain to two possible service suppliers: multi-service oil companies, and mobile service operators (or MVNOs). Model optimization on the return-on-investment (R.O.I.) is carried out for different deployment scenarios, geographical coverage assumptions, as well as tariff structures. Comparison is also being made with public GPRS and 3G data services, as precursors to HSPA/LTE, and the effect of WiFi roaming is analyzed. Regulatory implications, including those dealing with public safety, are addressed. Analysis shows that due to manpower costs and marketing costs, suitable R.O.I. will not be achieved unless externalities are accounted for and innovative tariff structures are introduced. Open issues and further research are outlined. Further work is currently carried out with automotive electronics sector, wireless systems providers, wireless terminals platform suppliers, and vehicle manufacturers. Future relevance of this work is also discussed for the emerging electrical reloading grids for electrical vehicles.WiFi, Fuel Stations, Business Models, Oil Company, Mobile Operator, WiFi Services, Regulations, Professional Vehicles
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Merging mobile edge computing (MEC) functionality with the dense deployment
of base stations (BSs) provides enormous benefits such as a real proximity, low
latency access to computing resources. However, the envisioned integration
creates many new challenges, among which mobility management (MM) is a critical
one. Simply applying existing radio access oriented MM schemes leads to poor
performance mainly due to the co-provisioning of radio access and computing
services of the MEC-enabled BSs. In this paper, we develop a novel user-centric
energy-aware mobility management (EMM) scheme, in order to optimize the delay
due to both radio access and computation, under the long-term energy
consumption constraint of the user. Based on Lyapunov optimization and
multi-armed bandit theories, EMM works in an online fashion without future
system state information, and effectively handles the imperfect system state
information. Theoretical analysis explicitly takes radio handover and
computation migration cost into consideration and proves a bounded deviation on
both the delay performance and energy consumption compared to the oracle
solution with exact and complete future system information. The proposed
algorithm also effectively handles the scenario in which candidate BSs randomly
switch on/off during the offloading process of a task. Simulations show that
the proposed algorithms can achieve close-to-optimal delay performance while
satisfying the user energy consumption constraint.Comment: 14 pages, 6 figures, an extended version of the paper submitted to
IEEE JSA
Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm
[EN]The use of electric bikes (e-bikes) has grown in popularity, especially in large cities
where overcrowding and traffic congestion are common. This paper proposes an intelligent engine
management system for e-bikes which uses the information collected from sensors to optimize battery
energy and time. The intelligent engine management system consists of a built-in network of sensors
in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused
and on the basis of this information the system can provide the user with optimal and personalized
assistance. The user is given recommendations related to battery consumption, sensors, and other
parameters associated with the route travelled, such as duration, speed, or variation in altitude. To
provide a user with these recommendations, artificial neural networks are used to estimate speed and
consumption for each of the segments of a route. These estimates are incorporated into evolutionary
algorithms in order to make the optimizations. A comparative analysis of the results obtained has
been conducted for when routes were travelled with and without the optimization system. From
the experiments, it is evident that the use of an engine management system results in significant
energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to
user behavior and the characteristics of the route
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
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