23,035 research outputs found
Parallel energy-efficient coverage optimization using WSN with Image Compression
Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrices are calculated by grid exclusion algorithm and Dijkstra’s algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combing the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection
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Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs
Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
AMCTD: Adaptive Mobility of Courier nodes in Threshold-optimized DBR Protocol for Underwater Wireless Sensor Networks
In dense underwater sensor networks (UWSN), the major confronts are high
error probability, incessant variation in topology of sensor nodes, and much
energy consumption for data transmission. However, there are some remarkable
applications of UWSN such as management of seabed and oil reservoirs,
exploration of deep sea situation and prevention of aqueous disasters. In order
to accomplish these applications, ignorance of the limitations of acoustic
communications such as high delay and low bandwidth is not feasible. In this
paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized
Depth-based routing (AMCTD), exploring the proficient amendments in depth
threshold and implementing the optimal weight function to achieve longer
network lifetime. We segregate our scheme in 3 major phases of weight updating,
depth threshold variation and adaptive mobility of courier nodes. During data
forwarding, we provide the framework for alterations in threshold to cope with
the sparse condition of network. We ultimately perform detailed simulations to
scrutinize the performance of our proposed scheme and its comparison with other
two notable routing protocols in term of network lifetime and other essential
parameters. The simulations results verify that our scheme performs better than
the other techniques and near to optimal in the field of UWSN.Comment: 8th International Conference on Broadband and Wireless Computing,
Communication and Applications (BWCCA'13), Compiegne, Franc
Analyzing Energy-efficiency and Route-selection of Multi-level Hierarchal Routing Protocols in WSNs
The advent and development in the field of Wireless Sensor Networks (WSNs) in
recent years has seen the growth of extremely small and low-cost sensors that
possess sensing, signal processing and wireless communication capabilities.
These sensors can be expended at a much lower cost and are capable of detecting
conditions such as temperature, sound, security or any other system. A good
protocol design should be able to scale well both in energy heterogeneous and
homogeneous environment, meet the demands of different application scenarios
and guarantee reliability. On this basis, we have compared six different
protocols of different scenarios which are presenting their own schemes of
energy minimizing, clustering and route selection in order to have more
effective communication. This research is motivated to have an insight that
which of the under consideration protocols suit well in which application and
can be a guide-line for the design of a more robust and efficient protocol.
MATLAB simulations are performed to analyze and compare the performance of
LEACH, multi-level hierarchal LEACH and multihop LEACH.Comment: NGWMN with 7th IEEE Inter- national Conference on Broadband and
Wireless Computing, Communication and Applications (BWCCA 2012), Victoria,
Canada, 201
Optimization of depth-based routing for underwater wireless sensor networks through intelligent assignment of initial energy
Underwater Wireless Sensor Networks (UWSNs) are extensively used to explore the diverse marine environment. Energy efficiency is one of the main concerns regarding performance of UWSNs. In a cooperative wireless sensor network, nodes with no energy are known as coverage holes. These coverage holes are created due to non-uniform energy utilization by the sensor nodes in the network. These coverage holes degrade the performance and reduce the lifetime of UWSNs. In this paper, we present an Intelligent Depth Based Routing (IDBR) scheme which addresses this issue and contributes towards maximization of network lifetime. In our proposed scheme, we allocate initial energy to the sensor nodes according to their usage requirements. This idea is helpful to balance energy consumption amongst the nodes and keep the network functional for a longer time as evidenced by the results provided
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
Combined Coverage Area Reporting and Geographical Routing in Wireless Sensor-Actuator Networks for Cooperating with Unmanned Aerial Vehicles
In wireless sensor network (WSN) applications with multiple gateways, it is key to route location dependent subscriptions efficiently at two levels in the system. At the gateway level, data sinks must not waste the energy of the WSN by injecting subscriptions that are not relevant for the nodes in their coverage area and at WSN level, energy-efficient delivery of subscriptions to target areas is required. In this paper, we propose a mechanism in which (1) the WSN provides an accurate and up-to-date coverage area description to gateways and (2) the wireless sensor network re-uses the collected coverage area information to enable efficient geographical routing of location dependent subscriptions and other messages. The latter has a focus on routing of messages injected from sink nodes to nodes in the region of interest. Our proposed mechanisms are evaluated in simulation
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