Wireless sensor network WSN is one of the interesting issues in the information technology domain. It is used in various fields such as medicine, agriculture, meteorology, etc.

Deployment is the greatest challenge in WSN that affects other features like coverage, connectivity, energy efficient and lifetime. Therefore, this study reviewed the deployment mechanisms which have been used in WSN systematically. The deployment mechanisms can be classified into two main categories: deterministic and nondeterministic.

Also, this study represents a comparison of the important techniques of the selected articles in each category to offer a guideline for further studies and new challenges. We also have noted some open issues for future research. This is a preview of subscription content, log in to check access.

Rent this article via DeepDyve. Anandamurugan, S. Antipredator adaptation shuffled frog leap algorithm to improve network life time in wireless sensor network. Wireless Personal Communications, 901— Google Scholar.

Coverage-Guaranteed Sensor Node Deployment Strategies for Wireless Sensor Networks

Navimipour, N. Control the topology and increase the tolerance of heterogeneous wireless sensor networks. The new genetic based method with optimum number of super node in heterogeneous wireless sensor network for fault tolerant system.

In International conference on intelligent networking and collaborative systems, Julie, E. Performance analysis of energy efficient virtual back bone path based cluster routing protocol for WSN. Rawat, P. Wireless sensor networks: A survey on recent developments and potential synergies.

The Journal of supercomputing, 68 11— Juliana, R.The Roxar Downhole Wireless PT Sensor System provides a means of detecting any variations in pressure and temperature behind the casing in subsea wells and, in particular in the B annulus.

The deterioration of cement seals or loss of casing integrity due to increased pressure behind the well casing can allow injection or reservoir gas to migrate vertically along the outside of the casing, leading to a number of unwanted and potentially hazardous conditions, such as uncontrolled gas escaping at the surface or, in the worst case scenario, a shallow gas blow out due to failed barriers in the casing.

The installation took place in two phases with the PT transponder installed as part of the casing in September followed by the wireless reader being installed as part of the completion in February The entire system has been rigorously tested and is rated to operate at temperatures up to degC. The Installation for Statoil has a minimum expected lifetime of 20 years.

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The field is situated in water depths of approximately meters and involves three production wells and three water injectors. Sign In Register Contact Us.To browse Academia. Skip to main content. Log In Sign Up. Download Free PDF. Antoine Bagula.

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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper demonstrates how an irrigation management system IMS can practically be implemented by deploying a wireless sensor network WSN. Deployment of IMS in rural areas of developing countries like Malawi is a challenge as grid power is scarce. For the system to be self-sustained in terms of power, the study used solar photovoltaic and rechargeable batteries to power all electrical devices.

The system incorporated a remote monitoring mechanism through a General Packet Radio Service modem to report soil temperature, soil moisture, WSN link performance, and photovoltaic power levels.

successful deployment of a wireless sensor

Irrigation valves were activated to water the field. Preliminary results in this study have revealed a number of engineering weaknesses of deploying such a system. Nevertheless, the paper has highlighted areas of improvement to develop a robust, fully automated, solar-powered, and low-cost IMS to suit the socioeconomic conditions of small scale farmers in developing countries. Introduction potential to PA, such that, if well designed, can be a solution to a low-cost IMS suitable for developing countries.

In precision agriculture PAvarious parameters including The increase in WSN deployment in industrial, agri- soil type and temperature vary dramatically from one region cultural, and environmental monitoring applications is as a to the other; consequently, any irrigation system must be result of being a low power and low data rate hence energy flexible to adapt to such variations.

Off-the-shelf irrigation efficient technology. It also offers mobility and flexibility controllers are usually expensive and not effective in man- in connectivity which promote network expansion when aging scarce water resources [1, 2]. On the other hand, needed.

However, WSNs are still under a developmental stage; ulation tools in which random and grid topologies were as such, they are at times unreliable, fragile, and power compared. They evaluated the performance of the networks hungry and can easily lose communication especially when by monitoring delay, throughput, and load.

This approach, deployed in a harsh environment like an agricultural field however, lacks practical aspects where some simulation [2]. Unlike laboratory-based simulations and experimental assumptions are invalid. Zhou and others [4] presented a installations, practical deployments have to handle such WSN deployment for an irrigation system using ZigBee challenges to be fully beneficial. WSNs have an immense protocol. Cellular network Despite having a detailed design for the powering side, they Physical parameters did not monitor battery levels for the sensor nodes.

The ultimate purpose of the WiPAM system was to auto- 2. WSN Protocol and Topology. The WSN deployed in this mate irrigation process. Such personal area networks. The physical layer of ZigBee operates fluctuations were then used by the irrigation controller to ini- in the unlicensed industrial, scientific, and medical radio tiate irrigation events.

In order for the controller to precisely bands of MHz, MHz, and 2.

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This study adopted the 2. However, in order to unlicensed in Malawi. The low power consumption character- 2 minutes when the irrigation was in progress.

In order to attain a low power con- intervals, 2 sending and storing sampled data in a coordi- sumption characteristic, the ZigBee protocol operates at low nator node, 3 sending the data from the coordinator to a data rates kbps at 2. Nonetheless, this imposes gateway node for forwarding to a remote monitoring station its limitation where high data transmission applications are RMS through a cellular network, 4 going to sleep, and 5 required.

Such applications may use other IEEE standards, waking up and repeating the previous steps. Depending on for instance, Bluetooth The performance of wireless sensor networks depends largely on the deployment of sensor nodes as well as their lifetime mainly determined by the energy consumption. Most current attention, however, has been paid to energy-efficient deployment.

With the goal of facilitating further evolution of wireless sensor networks, recently proposed deployment schemes for wireless sensor networks are surveyed.

The focus is on coverage and connectivity, which are regarded as the most important respects of network performance and energy-efficiency. Depending on the application and different actions in the network, coverage issues are classified into static and dynamic ones, while connectivity issues into pure connectivity and routing algorithm based connectivity.

An overview of each of these areas is presented, and the performance of existing methodologies is discussed. In order to spark new interests and developments in this field, some crucial open issues are pointed out. Unable to display preview. Download preview PDF. Skip to main content. This service is more advanced with JavaScript available.

Advertisement Hide. Deployment Issues in Wireless Sensor Networks. Conference paper.

successful deployment of a wireless sensor

This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Akyildiz, I. Ganesan, D.

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Perkins, M. Sibley, G.As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network.

A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area.

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Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment.

Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks. Wireless sensor networks are used for target tracking, environment monitoring, surveillance and for getting humidity, temperature, light, pressure data, etc. Deciding the positions of the sensors is the main subject of sensor network deployment, and in turn it depends on the desired coverage of the area of interest. In dynamic deployment problem, initially sensors are located in the area with random positions and the sensors change their positions by using the knowledge of others positions, if they are mobile.

By these movements, it is tired to increase the coverage rate of the sensors. On the other hand, if the sensors are stationary, they do not have ability to change their positions. In initial deployment, because of the randomness, generally an effective coverage cannot be obtained. To tackle this problem, various dynamic deployment algorithms have been studied by researchers [ 2 — 5 ].

To improve the coverage of the network, one of the approaches used in these researches is the virtual force VF algorithm [ 6 ], which works well for WSNs which consist only of mobile sensors [ 6 — 8 ].

In [ 9 ], a blackboard mechanism based on ant colony theory was proposed for dynamic deployment of mobile sensor networks.

successful deployment of a wireless sensor

Kukunuru et al. These approaches do not consider the stationary sensors which are not able to change their initial positions. However, to save energy and to reduce cost, stationary sensors are widely used in real life network applications. Wang et al. Li and Lei proposed a method of improved particle swarm optimization to solve the deployment problem of WSNs consist of stationary and mobile sensor nodes [ 13 ].

Deployment Issues in Wireless Sensor Networks

Soleimanzadeh et al. In PSO-LA algorithm, PSO and learning automata are hybridized where speed of particles is corrected by using the existing knowledge and the feedback from the actual implementation of the algorithm.

To improve the performance of the PSO-LA, Improved PSO-LA algorithm is introduced, regulating movement of a node without an impact from the movement of other mobile nodes and based on the result gained from its previous movement.

In the third one, Improved PSO-LA with logical movement, sensors virtually move new positions by calculating their target locations with the same procedure of the Improved PSO-LA, but the real movement of the nodes only happens at the last round after final destinations are determined.

In this study, a new approach for dynamic deployment problem for WSNs is proposed. We considered WSNs which consist of mobile and stationary sensors together.

This approach is based on Artificial Bee Colony ABC algorithm which is developed by modeling foraging behavior of honey bee swarms [ 1516 ].

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It is known that the ABC algorithm works well for numerical optimization problems [ 17 — 19 ]. The ABC algorithm was first tested on dynamic deployment a using binary detection model of wireless sensor networks consisting of all mobile nodes in [ 20 ]. Considering the good performance of the algorithm, use of the ABC algorithm will be a proper approach for the sensors in the network to obtain a good coverage in two dimensional space with stationary and mobile nodes.

The performance of proposed approach is evaluated in comparison with another swarm based technique, Particle Swarm Optimization PSO.

We have organized rest of the paper as follows: Section 2 explains dynamic deployment problem of WSNs and sensor detection models, the proposed approach is presented in Section 3 and followed by the simulation results and comparison of PSO algorithm and proposed approach for this problem in Section 4.Recent advancement in wireless sensor networks is affordable and efficient using micro-electro-mechanical systems MEMS.

With its advancement different kinds of microsensors in WSN are available. The high speed and low power electronic devices are valid because of micro sensors Huang et al. Typically, sensor nodes are small and tiny devices composed of three basic components namely, a sensing subsystem for transceiving the data, processing subsystem for acquired data processing and st1orage resources, and also possible actuators subsystems for transmitting the data Omkar et al.

Also, the organization of Sensor nodes in networks is particularly dense Huping Xu et al. Due to the smaller size of sensor nodes, the source of energy required by the device to function is provided by a tiny battery with a minimum energy budget. Since the WSNs are made up of tiny energy-hungry sensor nodes, their limitation is in its usage of energy constraints posed by the sensor nodes Liu et al. A major issue in WSNs is power scarcity, driven during environmental monitoring, data processing, and wireless communications.

In general, a successful sensor node deployment strategy should consider both connectivity and target coverage of the network Agarwal et al. The main objective of WSN is the target coverage Alkhazaali, et al. Coverage means each node in the network should be involved in the quality of surveillance Luo et al. The sensor node's prime function represents every location in the Region Of Interest ROIit is sensed for any incidence of the event of interest Wang et al.

Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

The events detected by these mobile nodes in the heterogeneous network should be involved in communication with the sink called Connectivity and network is not separated while on sensor node communication.

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Available In. Technical University, Chennai, India. DOI: Abstract In the past years, wireless sensor networks WSNs have increased successful real-world deployment in a wide range of civil and military applications. As sensor nodes are battery-operated devices, the wide utilization of WSNs is obstructed by the severely limited energy constraints, this article tackles these kinds of issues by proposing an approach based on the energy model and aims at enhancing the network lifetime by improved balancing the movement and energy losses in the network.

This article proposes a design which minimizes the power consumption and movement cost, thus enhancing the network lifetime. Finally, the authors compared the energy efficiency of the proposed approach with that of the existing approach.

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Volume 4 Issues : 0 Released, 4 Forthcoming. Volume 4 Issues Volume 9: 2 Issues Wireless sensor networks WSNs have been considered as one of the fine research areas in recent years because of vital role in numerous applications. To process the extracted data and transmit it to the various location, a large number of nodes must be deployed in a proper way because deployment is one of the major issues in WSNs.

Hence, the minimum number of node deployment to attain full coverage is of enormous significance for research. The prime agenda of the presented paper is to categorize various coverage techniques into four major parts: computational geometry-based techniques, force-based techniques, grid-based techniques, and metaheuristic-based techniques.

Additionally, several comparisons amid these schemes are provided in view of their benefits and drawbacks. Our discussion weighs on the classification of coverage, practical challenges in the deployment of WSNs, sensing models, and research issues in WSNs.

Moreover, a detailed analysis of performance metrics and comparison among various WSNs simulators is listed. In conclusion, standing research issues along with potential work guidelines are discussed. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Yaqoob I et al Internet of things architecture: recent advances, taxonomy, requirements, and open challenges. Google Scholar.

J Inf Commun Converg Eng — Almonacid V, Franck L Extending the coverage of the internet of things with low-cost nanosatellite networks. Acta Astronaut — J Supercomput — Sensor Rev 37 1 — Liu Y Wireless sensor network applications in smart grid: recent trends and challenges. Int J Distrib Sens Netw Comput Netw — Zheng J, Jamalipour A Wireless sensor networks: a networking perspective. In: Embedded systems handbook.

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