717 research outputs found
Socio-economic aware data forwarding in mobile sensing networks and systems
The vision for smart sustainable cities is one whereby urban sensing is core to optimising city
operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned
to become pervasive form of data collection and analysis for smart cities but deployment of
millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and
ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile
Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future
smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the
data using cellular communication or short range opportunistic communication. The largest
challenge here is the efficient transmission of potentially huge volumes of sensor data over
sometimes meagre or faulty communications networks in a cost-effective way.
This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN
with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing
Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed
algorithms are developed for efficient network performance including data routing and forwarding,
sensing rate control and and pricing. This thesis also considered realistic urban sensing
issues such as economic incentivisation and demonstrates how social network and mobility
awareness improves data transmission. Through simulations and real testbed experiments, it
is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
From MANET to people-centric networking: Milestones and open research challenges
In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications
- …