5,007 research outputs found
Technologies and solutions for location-based services in smart cities: past, present, and future
Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas
Jointly Optimizing Placement and Inference for Beacon-based Localization
The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and
Systems (IROS
Key Generation in Wireless Sensor Networks Based on Frequency-selective Channels - Design, Implementation, and Analysis
Key management in wireless sensor networks faces several new challenges. The
scale, resource limitations, and new threats such as node capture necessitate
the use of an on-line key generation by the nodes themselves. However, the cost
of such schemes is high since their secrecy is based on computational
complexity. Recently, several research contributions justified that the
wireless channel itself can be used to generate information-theoretic secure
keys. By exchanging sampling messages during movement, a bit string can be
derived that is only known to the involved entities. Yet, movement is not the
only possibility to generate randomness. The channel response is also strongly
dependent on the frequency of the transmitted signal. In our work, we introduce
a protocol for key generation based on the frequency-selectivity of channel
fading. The practical advantage of this approach is that we do not require node
movement. Thus, the frequent case of a sensor network with static motes is
supported. Furthermore, the error correction property of the protocol mitigates
the effects of measurement errors and other temporal effects, giving rise to an
agreement rate of over 97%. We show the applicability of our protocol by
implementing it on MICAz motes, and evaluate its robustness and secrecy through
experiments and analysis.Comment: Submitted to IEEE Transactions on Dependable and Secure Computin
Traffic agents for improving QoS in mixed infrastructure and ad hoc modes wireless LAN
As an important complement to infrastructured wireless networks, mobile ad hoc networks (MANET) are more flexible in providing wireless access services, but more difficult in meeting different quality of service (QoS) requirements for mobile customers. Both infrastructure and ad hoc network structures are supported in wireless local area networks (WLAN), which can offer high data-rate wireless multimedia services to the mobile stations (MSs) in a limited geographical area. For those out-of-coverage MSs, how to effectively connect them to the access point (AP) and provide QoS support is a challenging issue. By mixing the infrastructure and the ad hoc modes in WLAN, we propose in this paper a new coverage improvement scheme that can identify suitable idle MSs in good service zones as traffic agents (TAs) to relay traffic from those out-of-coverage MSs to the AP. The service coverage area of WLAN is then expanded. The QoS requirements (e.g., bandwidth) of those MSs are considered in the selection process of corresponding TAs. Mathematical analysis, verified by computer simulations, shows that the proposed TA scheme can effectively reduce blocking probability when traffic load is light
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
- …