15 research outputs found
Stability enhancement method and experiment of orchard vehicle control
Study on orchard working vehicle rollover and tipping prediction is important to maintain vehicle stability control in complicated operation conditions of orchard. Existing rollover and tipping prediction models for vehicles can not directly apply to orchard working vehicle, which structure and loading are changing under operation. So it is necessary to move ahead study on orchard vehicle bodywork posture prediction and rollover and tipping prediction by theoretical analysis, mathematical modelling, real vehicle test and other methods. In this paper, firstly, we establish orchard working vehicle dynamic model, analyses variation of key parameters during vehicle instability state, and look for characteristic parameters of vehicle instability. Secondly, active safety control algorithm which based on posture detection of vehicle body is researched. Finally, control model is verified and optimized by scaled test
Energy Efficiency in Two-Tiered Wireless Sensor Networks
We study a two-tiered wireless sensor network (WSN) consisting of access
points (APs) and base stations (BSs). The sensing data, which is
distributed on the sensing field according to a density function , is first
transmitted to the APs and then forwarded to the BSs. Our goal is to find an
optimal deployment of APs and BSs to minimize the average weighted total, or
Lagrangian, of sensor and AP powers. For , we show that the optimal
deployment of APs is simply a linear transformation of the optimal -level
quantizer for density , and the sole BS should be located at the geometric
centroid of the sensing field. Also, for a one-dimensional network and uniform
, we determine the optimal deployment of APs and BSs for any and .
Moreover, to numerically optimize node deployment for general scenarios, we
propose one- and two-tiered Lloyd algorithms and analyze their convergence
properties. Simulation results show that, when compared to random deployment,
our algorithms can save up to 79\% of the power on average.Comment: 11 pages, 7 figure
Movement-efficient Sensor Deployment in Wireless Sensor Networks
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Two key issues in MWSNs - energy consumption, which
is dominated by sensor movement, and sensing coverage - have attracted plenty
of attention, but the interaction of these issues is not well studied. To take
both sensing coverage and movement energy consumption into consideration, we
model the sensor deployment problem as a constrained source coding problem. %,
which can be applied to different coverage tasks, such as area coverage, target
coverage, and barrier coverage. Our goal is to find an optimal sensor
deployment to maximize the sensing coverage with specific energy constraints.
We derive necessary conditions to the optimal sensor deployment with (i) total
energy constraint and (ii) network lifetime constraint. Using these necessary
conditions, we design Lloyd-like algorithms to provide a trade-off between
sensing coverage and energy consumption. Simulation results show that our
algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure
Optimal Deployments of UAVs With Directional Antennas for a Power-Efficient Coverage
To provide a reliable wireless uplink for users in a given ground area, one
can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another
application, one can use UAVs to collect data from sensors on the ground. For a
power-efficient and scalable deployment of such flying BSs, directional
antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We
consider a large-scale wireless path-loss model with a realistic
angle-dependent radiation pattern for the directional antennas. Based on such a
model, we determine the optimal 3-D deployment of N UAVs to minimize the
average transmit-power consumption of the users in a given target area. The
users are assumed to have identical transmitters with ideal omnidirectional
antennas and the UAVs have identical directional antennas with given half-power
beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For
uniformly distributed ground users, we show that the UAVs have to share a
common flight height in an optimal power-efficient deployment. We also derive
in closed-form the asymptotic optimal common flight height of UAVs in terms
of the area size, data-rate, bandwidth, HPBW, and path-loss exponent
Quantizers with Parameterized Distortion Measures
In many quantization problems, the distortion function is given by the
Euclidean metric to measure the distance of a source sample to any given
reproduction point of the quantizer. We will in this work regard distortion
functions, which are additively and multiplicatively weighted for each
reproduction point resulting in a heterogeneous quantization problem, as used
for example in deployment problems of sensor networks. Whereas, normally in
such problems, the average distortion is minimized for given weights
(parameters), we will optimize the quantization problem over all weights, i.e.,
we tune or control the distortion functions in our favor.
For a uniform source distribution in one-dimension, we derive the unique
minimizer, given as the uniform scalar quantizer with an optimal common weight.
By numerical simulations, we demonstrate that this result extends to
two-dimensions where asymptotically the parameter optimized quantizer is the
hexagonal lattice with common weights. As an application, we will determine the
optimal deployment of unmanned aerial vehicles (UAVs) to provide a wireless
communication to ground terminals under a minimal communication power cost.
Here, the optimal weights relate to the optimal flight heights of the UAVs.Comment: submitted to DCC 201
Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy
consumption, and connectivity, have attracted plenty of attention, but the
interaction of these factors is not well studied. To take all the three factors
into consideration, we model the sensor deployment problem as a constrained
source coding problem. %, which can be applied to different coverage tasks,
such as area coverage, target coverage, and barrier coverage. Our goal is to
find an optimal sensor deployment (or relocation) to optimize the sensing
quality with a limited communication range and a specific network lifetime
constraint. We derive necessary conditions for the optimal sensor deployment in
both homogeneous and heterogeneous MWSNs. According to our derivation, some
sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these
necessary conditions, we design both centralized and distributed algorithms to
provide a flexible and explicit trade-off between sensing uncertainty and
network lifetime. The proposed algorithms are successfully extended to more
applications, such as area coverage and target coverage, via properly selected
density functions. Simulation results show that our algorithms outperform the
existing relocation algorithms
T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture - A Case Study
Efficient sensor deployment is one of primary requirements of precision agriculture use case of Wireless Sensor Networks (WSNs) to provide qualitative and optimal coverage and connectivity. The application-based performance variations of the geometrical-model-based sensor deployment patterns restricts the generalization of a specific deployment pattern for all applications. Further, single or double metrics based evaluation of the deployment patterns focusing on theoretical or simulation aspects can be attributed to the difference in performance of real applications and the reported performance in literature. In this context, this paper proposes a Testbed based Multi-metric Quality Measurement (T-MQM) of sensor deployment for precision agriculture use case of WSNs. Specifically, seven metrics are derived for qualitative measurement of sensor deployment patterns for precision agriculture. The seven metrics are quantified for four sensor deployment patterns to measure the quality of coverage and connectivity. Analytical and simulation based evaluations of the measurements are validated through testbed experiment based evaluations which are carried out in ‘INDRIYA’ WSNs testbed. Towards realistic research impact, the investigative evaluation of the geometrical-model-based deployment patterns presented in this article could be useful for practitioners and researchers in developing performance guaranteed applications for precision agriculture and novel coverage and connectivity models for deployment patterns
Energy-Efficient Node Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks
We study a heterogeneous wireless sensor network (WSN) where N heterogeneous
access points (APs) gather data from densely deployed sensors and transmit
their sensed information to M heterogeneous fusion centers (FCs) via multi-hop
wireless communication. This heterogeneous node deployment problem is modeled
as an optimization problem with total wireless communication power consumption
of the network as its objective function. We consider both static WSNs, where
nodes retain their deployed position, and mobile WSNs where nodes can move from
their initial deployment to their optimal locations. Based on the derived
necessary conditions for the optimal node deployment in static WSNs, we propose
an iterative algorithm to deploy nodes. In addition, we study the necessary
conditions of the optimal movement-efficient node deployment in mobile WSNs
with constrained movement energy, and present iterative algorithms to find such
deployments, accordingly. Simulation results show that our proposed node
deployment algorithms outperform the existing methods in the literature, and
achieves a lower total wireless communication power in both static and mobile
WSNs, on average