10,447 research outputs found
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
An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks
Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs
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
On Modeling Geometric Joint Sink Mobility with Delay-Tolerant Cluster-less Wireless Sensor Networks
Moving Sink (MS) in Wireless Sensor Networks (WSNs) has appeared as a
blessing because it collects data directly from the nodes where the concept of
relay nodes is becomes obsolete. There are, however, a few challenges to be
taken care of, like data delay tolerance and trajectory of MS which is NP-hard.
In our proposed scheme, we divide the square field in small squares. Middle
point of the partitioned area is the sojourn location of the sink, and nodes
around MS are in its transmission range, which send directly the sensed data in
a delay-tolerant fashion. Two sinks are moving simultaneously; one inside and
having four sojourn locations and other in outer trajectory having twelve
sojourn locations. Introduction of the joint mobility enhances network life and
ultimately throughput. As the MS comes under the NP-hard problem, we convert it
into a geometric problem and define it as, Geometric Sink Movement (GSM). A set
of linear programming equations has also been given in support of GSM which
prolongs network life time
EMEEDP: Enhanced Multi-hop Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network
In WSN (Wireless Sensor Network) every sensor node sensed the data and
transmit it to the CH (Cluster head) or BS (Base Station). Sensors are randomly
deployed in unreachable areas, where battery replacement or battery charge is
not possible. For this reason, Energy conservation is the important design goal
while developing a routing and distributed protocol to increase the lifetime of
WSN. In this paper, an enhanced energy efficient distributed protocol for
heterogeneous WSN have been reported. EMEEDP is proposed for heterogeneous WSN
to increase the lifetime of the network. An efficient algorithm is proposed in
the form of flowchart and based on various clustering equation proved that the
proposed work accomplishes longer lifetime with improved QOS parameters
parallel to MEEP. A WSN implemented and tested using Raspberry Pi devices as a
base station, temperature sensors as a node and xively.com as a cloud. Users
use data for decision purpose or business purposes from xively.com using
internet.Comment: 6 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1409.1412 by other author
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