Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions


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Journal Communications and Networks. Energy efficient strategies for deployment of a two-level wireless sensor network. Power-efficient sensor placement and transmission structure for data gathering under distortion constraints. Relay deployment and power control for lifetime elongation in sensor networks. Maleki M, Pedram M. QoM and lifetime-constrained random deployment of sensor networks for minimum energy consumption. Li W, Zhang W. Coverage analysis and active scheme of wireless sensor networks.

Shakkottai S. Unreliable sensor grids: Coverage, connectivity and diameter. Back-tracking based sensor deployment by a robot team. Indian Journal of Science and Technology. Reegan AS, Baburaj E. An effective model of the neighbor discovery and energy efficient routing method for wireless sensor networks.

An alternative node deployment scheme for WSNs. Abbas W, Koutsoukos X. Efficient complete coverage through heterogeneous sensing nodes. Sensing models and its impact on network coverage in wireless sensor network. Coverage and connectivity issues in wireless sensor networks: A survey.

Pervasive Mobile Computing. Minimizing movement for target coverage in mobile sensor networks. A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications. Wireless power transfer and applications to sensor networks. Electrons Letters. Traffic-aware relay node deployment: Maximizing lifetime for data collection wireless sensor networks. Sensor deployment and scheduling for target coverage problem in wireless sensor networks. The use of many wireless distributed sensors enables the creation of a more accurate map of the water status, and allows the permanent deployment of monitoring stations in locations of difficult access, without the need of manual data retrieval.

Wireless sensor networks can be effective in preventing adverse consequences of natural disasters , like floods. Wireless nodes have been deployed successfully in rivers, where changes in water levels must be monitored in real time. Wireless sensor networks have been developed for machinery condition-based maintenance CBM as they offer significant cost savings and enable new functionality. Wireless sensors can be placed in locations difficult or impossible to reach with a wired system, such as rotating machinery and untethered vehicles.

Wireless sensor networks also are used for the collection of data for monitoring of environmental information. The statistical information can then be used to show how systems have been working. The advantage of WSNs over conventional loggers is the "live" data feed that is possible. It may be used to protect the wastage of water. Wireless sensor networks can be used to monitor the condition of civil infrastructure and related geo-physical processes close to real time, and over long periods through data logging, using appropriately interfaced sensors.

Wireless sensor networks are used to monitor wine production, both in the field and the cellar. WATS would be made up of wireless gamma and neutron sensors connected through a communications network. Data picked up by the sensors undergoes "data fusion" , which converts the information into easily interpreted forms; this data fusion is the most important aspect of the system. The data fusion process occurs within the sensor network rather than at a centralized computer and is performed by a specially developed algorithm based on Bayesian statistics.

Data processed in the field by the network itself by transferring small amounts of data between neighboring sensors is faster and makes the network more scalable. An important factor in WATS development is ease of deployment , since more sensors both improves the detection rate and reduces false alarms. One barrier to the implementation of WATS is the size, weight, energy requirements and cost of currently available wireless sensors.

WATS was profiled to the U. House of Representatives' Military Research and Development Subcommittee on October 1, during a hearing on nuclear terrorism and countermeasures.


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Cross-layer is becoming an important studying area for wireless communications. So the cross-layer can be used to make the optimal modulation to improve the transmission performance, such as data rate , energy efficiency, QoS Quality of Service , etc. They usually consist of a processing unit with limited computational power and limited memory, sensors or MEMS including specific conditioning circuitry , a communication device usually radio transceivers or alternatively optical , and a power source usually in the form of a battery.

Other possible inclusions are energy harvesting modules, [21] secondary ASICs , and possibly secondary communication interface e. RS or USB. The base stations are one or more components of the WSN with much more computational, energy and communication resources. They act as a gateway between sensor nodes and the end user as they typically forward data from the WSN on to a server.

Other special components in routing based networks are routers, designed to compute, calculate and distribute the routing tables. One major challenge in a WSN is to produce low cost and tiny sensor nodes. There are an increasing number of small companies producing WSN hardware and the commercial situation can be compared to home computing in the s.

Many of the nodes are still in the research and development stage, particularly their software. Also inherent to sensor network adoption is the use of very low power methods for radio communication and data acquisition. The Gateway acts as a bridge between the WSN and the other network. This enables data to be stored and processed by devices with more resources, for example, in a remotely located server.

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There are several wireless standards and solutions for sensor node connectivity. Thread and ZigBee can connect sensors operating at 2. The IEEE With the emergence of Internet of Things , many other proposals have been made to provide sensor connectivity. Wi-SUN [23] connects devices at home. WSNs may be deployed in large numbers in various environments, including remote and hostile regions, where ad hoc communications are a key component.

For this reason, algorithms and protocols need to address the following issues:. To conserve power, wireless sensor nodes normally power off both the radio transmitter and the radio receiver when not in use. Wireless sensor networks are composed of low-energy, small-size, and low-range unattended sensor nodes. Recently, it has been observed that by periodically turning on and off the sensing and communication capabilities of sensor nodes, we can significantly reduce the active time and thus prolong network lifetime.

However, this duty cycling may result in high network latency, routing overhead, and neighbor discovery delays due to asynchronous sleep and wake-up scheduling. These limitations call for a countermeasure for duty-cycled wireless sensor networks which should minimize routing information, routing traffic load, and energy consumption. Researchers from Sungkyunkwan University have proposed a lightweight non-increasing delivery-latency interval routing referred as LNDIR.

This scheme can discover minimum latency routes at each non-increasing delivery-latency interval instead of each time slot. Simulation experiments demonstrated the validity of this novel approach in minimizing routing information stored at each sensor. Furthermore, this novel routing can also guarantee the minimum delivery latency from each source to the sink. Performance improvements of up to fold and fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes.

Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems.

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They more strongly resemble embedded systems , for two reasons. First, wireless sensor networks are typically deployed with a particular application in mind, rather than as a general platform. Second, a need for low costs and low power leads most wireless sensor nodes to have low-power microcontrollers ensuring that mechanisms such as virtual memory are either unnecessary or too expensive to implement. However, such operating systems are often designed with real-time properties. TinyOS is perhaps the first [27] operating system specifically designed for wireless sensor networks.

TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed of event handlers and tasks with run-to-completion semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS signals the appropriate event handler to handle the event. Event handlers can post tasks that are scheduled by the TinyOS kernel some time later. Online collaborative sensor data management platforms are on-line database services that allow sensor owners to register and connect their devices to feed data into an online database for storage and also allow developers to connect to the database and build their own applications based on that data.

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Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions
Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions

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