42,686 research outputs found
Energy-efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks
This paper proposes an energy-efficient counting rule for distributed
detection by ordering sensor transmissions in wireless sensor networks. In the
counting rule-based detection in an sensor network, the local sensors
transmit binary decisions to the fusion center, where the number of all
local-sensor detections are counted and compared to a threshold. In the
ordering scheme, sensors transmit their unquantized statistics to the fusion
center in a sequential manner; highly informative sensors enjoy higher priority
for transmission. When sufficient evidence is collected at the fusion center
for decision making, the transmissions from the sensors are stopped. The
ordering scheme achieves the same error probability as the optimum
unconstrained energy approach (which requires observations from all the
sensors) with far fewer sensor transmissions. The scheme proposed in this paper
improves the energy efficiency of the counting rule detector by ordering the
sensor transmissions: each sensor transmits at a time inversely proportional to
a function of its observation. The resulting scheme combines the advantages
offered by the counting rule (efficient utilization of the network's
communication bandwidth, since the local decisions are transmitted in binary
form to the fusion center) and ordering sensor transmissions (bandwidth
efficiency, since the fusion center need not wait for all the sensors to
transmit their local decisions), thereby leading to significant energy savings.
As a concrete example, the problem of target detection in large-scale wireless
sensor networks is considered. Under certain conditions the ordering-based
counting rule scheme achieves the same detection performance as that of the
original counting rule detector with fewer than sensor transmissions; in
some cases, the savings in transmission approaches .Comment: 7 pages, 3 figures. Proceedings of FUSION 2018, Cambridge, U
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Sequential Statistical Signal Processing with Applications to Distributed Systems
Detection and estimation, two classical statistical signal processing problems with wellestablished
theories, are traditionally studied under the fixed-sample-size and centralized
setups, e.g., Neyman-Pearson target detection, and Bayesian parameter estimation. Recently,
they appear in more challenging setups with stringent constraints on critical resources,
e.g., time, energy, and bandwidth, in emerging technologies, such as wireless sensor
networks, cognitive radio, smart grid, cyber-physical systems (CPS), internet of things
(IoT), and networked control systems. These emerging systems have applications in a wide
range of areas, such as communications, energy, the military, transportation, health care,
and infrastructure.
Sequential (i.e., online) methods suit much better to the ever-increasing demand on
time-efficiency, and latency constraints than the conventional fixed-sample-size (i.e., offline)
methods. Furthermore, as a result of decreasing device sizes and tendency to connect
more and more devices, there are stringent energy and bandwidth constraints on devices
(i.e., nodes) in a distributed system (i.e., network), requiring decentralized operation with
low transmission rates. Hence, for statistical inference (e.g., detection and/or estimation)
problems in distributed systems, today's challenge is achieving high performance (e.g., time
efficiency) while satisfying resource (e.g., energy and bandwidth) constraints.
In this thesis, we address this challenge by (i) first finding optimum (centralized) sequential
schemes for detection, estimation, and joint detection and estimation if not available in
the literature, (ii) and then developing their asymptotically optimal decentralized versions
through an adaptive non-uniform sampling technique called level-triggered sampling. We
propose and rigorously analyze decentralized detection, estimation, and joint detection and
estimation schemes based on level-triggered sampling, resulting in a systematic theory of
event-based statistical signal processing. We also show both analytically and numerically
that the proposed schemes significantly outperform their counterparts based on conventional
uniform sampling in terms of time efficiency. Moreover, they are compatible with the
existing hardware as they work with discrete-time observations produced by conventional
A/D converters.
We apply the developed schemes to several problems, namely spectrum sensing and
dynamic spectrum access in cognitive radio, state estimation and outage detection in smart
grid, and target detection in multi-input multi-output (MIMO) wireless sensor networks
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
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