WiseNET: an ultralow-power wireless sensor network solution
A wireless sensor network consists of many energy-autonomous microsensors distributed throughout an area of interest. Each node monitors its local environment, locally processing and storing the collected data so that other nodes can use it. To optimize power consumption, the Swiss Center for Electronics and Microtechnology has developed WiseNET, an ultralow-power platform for the implementation of wireless sensor networks that achieves low-power operation through a careful codesign approach. The WiseNET platform uses a codesign approach that combines a dedicated duty-cycled radio with WiseMAC, a low-power media access control protocol, and a complex system-on-chip sensor node to exploit the intimate relationship between MAC-layer performance and radio transceiver parameters. The WiseNET solution consumes about 100 times less power than comparable solutions.
Dynamic Power Management in Wireless Sensor Networks: State-of-the-Art
In the last few years, interest in wireless sensor networks has increased considerably. These networks can be useful for a large number of applications, including habitat monitoring, structural health monitoring, pipeline monitoring, transportation, precision agriculture, supply chain management, and many more. Typically, a wireless sensor network consists of a large number of simple nodes which operate with exhaustible batteries, unattended. Manual replacement or recharging the batteries is not an easy or desirable task. Hence, how energy is utilized by the various hardware subsystems of individual nodes directly affects the scope and usefulness of the entire network. This paper provides a comprehensive assessment of state-of-the-art of dynamic power management (DPM) in wireless sensor networks. It investigates aspects of power dissipation in a node and analyses the strength and limitations of selective switching, dynamic frequency, and voltage scaling.
Robust computation of aggregates in wireless sensor networks: distributed randomized algorithms and analysis
A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network like system monitoring, data querying, and collaborative information processing. In this paper we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our distributed random grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigen-structure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs much less transmissions than other distributed localized schemes, namely gossip and broadcast flooding.
A Novel Cluster Head Selection Method based on K-Means Algorithm for Energy Efficient Wireless Sensor Network
Wireless sensor network consists of hundreds to thousands of sensor nodes gathering various data including temperature, sound, location, etc. They have been applied to numerous fields such as healthcare, monitoring system, military, and so forth. It is usually difficult to recharge or replace the sensor nodes which have limited battery capacity. Energy efficiency is thus a primary issue in maintaining the network. In this paper we propose an efficient cluster head selection method using K-means algorithm to maximize the energy efficiency of wireless sensor network. It is based on the concept of finding the cluster head minimizing the sum of Euclidean distances between the head and member nodes. Computer simulation shows that the proposed approach allows better performance than the existing hierarchical routing protocols such as LEACH and HEED in terms of network lifetime.
SWATS: Wireless sensor networks for steamflood and waterflood pipeline monitoring
State-of-the-art anomaly detection systems deployed in oilfields are expensive, not scalable to a large number of sensors, require manual operation, and provide data with a long delay. To overcome these problems, we design a wireless sensor network system that detects, identifies, and localizes major anomalies such as blockage and leakage that arise in steamflood and waterflood pipelines in oilfields. A sensor network consists of small, inexpensive nodes equipped with embedded processors and wireless communication, which enables flexible deployment and close observation of phenomena without human intervention. Our sensor-network-based system, Steamflood and Waterflood Tracking System (SWATS), aims to allow continuous monitoring of the steamflood and waterflood systems with low cost, short delay, and fine-granularity coverage while providing high accuracy and reliability. The anomaly detection and identification is challenging because of the inherent inaccuracy and unreliability of sensors and the transient characteristics of the flows. Moreover, observation by a single node cannot capture the topological effects on the transient characteristics of steam and water fluid to disambiguate similar problems and false alarms. We address these hurdles by utilizing multimodal sensing and multisensor collaboration, and exploiting temporal and spatial patterns of the sensed phenomena.
Robust computation of aggregates in wireless sensor networks: distributed randomized algorithms and analysis
A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network like system monitoring, data querying, and collaborative information processing. In this paper we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our distributed random grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigen-structure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs much less transmissions than other distributed localized schemes, namely gossip and broadcast flooding.
A Sentinel Sensor Network for Hydrogen Sensing
A wireless sensor network is presented for in-situ monitoring of atmospheric hydrogen concentration. The hydrogen sensor network consists of multiple sensor nodes, equipped with titania nanotube hydrogen sensors, distributed throughout the area of interest; each node is both sensor, and data-relay station that enables extended wide area monitoring without a consequent increase of node power and thus node size. The hydrogen sensor is fabricated from a sheet of highly ordered titania nanotubes, made by anodization of a titanium thick film, to which platinum electrodes are connected. The electrical resistance of the hydrogen sensor varies from 245 Ω at 500 ppm hydrogen, to 10.23 kΩ at 0 ppm hydrogen (pure nitrogen environment). The measured resistance is converted to voltage, 0.049 V at 500 ppm to 2.046 V at 0 ppm, by interface circuitry. The microcontroller of the sensor node digitizes the voltage and transmits the digital information, using intermediate nodes as relays, to a host node that downloads measurement data to a computer for display. This paper describes the design and operation of the sensor network, the titania nanotube hydrogen sensors with an apparent low level resolution of approximately 0.05 ppm, and their integration in one widely useful device.
An Efficient Wireless Sensor Network for Precision Agriculture
Wireless sensor network consists of many node with capability of sensing, computation, and wireless communications. The wireless sensor network technologies are increasingly being implemented for modern precision agriculture monitoring. The privileges of wireless sensor network in agriculture are for several causes: high performance, increase the production efficiency while decreasing cost, low-power consumption and collected distributed data.
TinyOS: An Operating System for Wireless Sensor Networks
TinyOS is an open-source, flexible and application-specific operating system for wireless sensor networks. Wireless sensor network consists of a large number of tiny and low-power nodes, each of which executes simultaneous and reactive programs that must work with strict memory and power constraints. The wireless sensor network’s challenges of event-centric concurrent applications, limited resources and low-power operation impel the design of TinyOS. TinyOS meets these challenges and has become the platform of choice for sensor network research. It is very prevalent in sensor networks these days and supports a broad range of applications and research topics.
Design considerations for a large-scale wireless sensor network for substation monitoring
This paper describes the design and deployment of a large scale wireless sensor network (WSN) for monitoring the health of power equipment in a substation. The sensor network consists of 122 low power nodes that that are spread over an area approximately 1000 × 400 feet in size and perform monitoring of equipment such as transformers, circuit breakers, and compressors. All nodes communicate over a multihop wireless mesh network that uses a dynamic link-quality based routing protocol. A primary objective of this project is to develop effective monitoring applications for the substation using low-cost wireless sensor nodes that can sustain long periods of battery life. We study the battery consumption in the network and present a transmission scheme that conserves communication cost by enabling the sensor nodes to transmit observation samples only when their values are significantly different from those transmitted previously. Experimental results that demonstrate the performance of the sensor network for several monitoring applications are presented.
Energy-Driven Adaptive Clustering Hierarchy (EDACH) for Wireless Sensor Networks
Wireless sensor network consists of small battery powered sensors. Therefore, energy consumption is an important issue and several schemes have been proposed to improve the lifetime of the network. In this paper we propose a new approach called energy-driven adaptive clustering hierarchy (EDACH), which evenly distributes the energy dissipation among the sensor nodes to maximize the network lifetime. This is achieved by using proxy node replacing the cluster-head of low battery power and forming more clusters in the region relatively far from the base station. Comparison with the existing schemes such as LEACH (Low-Energy Adaptive Clustering Hierarchy) and PEACH (Proxy-Enabled Adaptive Clustering Hierarchy) reveals that the proposed EDACH approach significantly improves the network lifetime.
Simulation & performance study of wireless sensor network (WSN) using MATLAB
A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Different approaches have used for simulation and modeling of SN (Sensor Network) and WSN. Traditional approaches consist of various simulation tools based on different languages such as C, C++ and Java. In this paper, MATLAB (7.6) Simulink was used to build a complete WSN system. Simulation procedure includes building the hardware architecture of the transmitting nodes, modeling both the communication channel and the receiving master node architecture. Bluetooth was chosen to undertake the physical layer communication with respect to different channel parameters (i.e., Signal to Noise ratio, Attenuation and Interference). The simulation model was examined using different topologies under various conditions and numerous results were collected. This new simulation methodology proves the ability of the Simulink MATLAB to be a useful and flexible approach to study the effect of different physical layer parameters on the performance of wireless sensor networks.
A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks
A wireless ad hoc sensor network consists of a number of sensors spreading across a geographical area. The performance of the network suffers as the number of nodes grows, and a large sensor network quickly becomes difficult to manage. Thus, it is essential that the network be able to self-organize. Clustering is an efficient approach to simplify the network structure and to alleviate the scalability problem. One method to create clusters is to use weakly connected dominating sets (WCDSs). Finding the minimum WCDS in an arbitrary graph is an NP-complete problem. We propose a neural network model to find the minimum WCDS in a wireless sensor network. We present a directed convergence algorithm. The new algorithm outperforms the normal convergence algorithm both in efficiency and in the quality of solutions. Moreover, it is shown that the neural network is robust. We investigate the scalability of the neural network model by testing it on a range of sized graphs and on a range of transmission radii. Compared with Guha and Khuller's centralized algorithm, the proposed neural network with directed convergency achieves better results when the transmission radius is short, and equal performance when the transmission radius becomes larger. The parallel version of the neural network model takes time O(d) , where d is the maximal degree in the graph corresponding to the sensor network, while the centralized algorithm takes O(n 2). We also investigate the effect of the transmission radius on the size of WCDS. The results show that it is important to select a suitable transmission radius to make the network stable and to extend the lifespan of the network. The proposed model can be used on sink nodes in sensor networks, so that a sink node can inform the nodes to be a coordinator (clusterhead) in the WCDS obtained by the algorithm. Thus, the message overhead is O(M), where M is the size of the WCDS.
Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing
The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks. The maximum energy consumption takes place in communicating the data from the nodes to the sink. Multiple sink WSN has an edge over the single sink WSN where very less energy is utilized in sending the data to the sink, as the number of hops is reduced. If the energy consumed by a node is balanced between the other nodes, the lifetime of the network is considerably increased. The network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink. Only those nodes are connected to a sink which makes the total energy of the sink less than the threshold. This energy balancing through network restructuring optimizes the network lifetime. This paper depicts this fact through simulations done in MATLAB.
An approach to increase the wireless sensor network lifetime
A wireless sensor network consist of small devices, called sensor nodes that are equipped with sensors to monitor the physical and environmental conditions such as pressure, temperature, humidity, motion, speed etc. The nodes in the wireless sensor network were battery powered, so one of the important issues in wireless sensor network is the inherent limited battery power within network sensor nodes. Minimizing energy dissipation and maximizing network lifetime are important issues in the design of sensor networks so if the power exhausted node would quit from the network, and it overall affect the network lifetime. Minimizing energy dissipation and maximizing network lifetime are important issues in the design of applications and protocols for sensor networks. In this paper there is improvement of lifetime of wireless sensor network in terms increasing alive nodes in network by using a different approach to select cluster head. The cluster head selection is based on the basis of maximum residual energy and minimum distance and chooses a optimal pat between the cluster heads to transmit to the base station.
Localization of Sensor Nodes in a Wireless Sensor Network Using the nanoLOC TRX Transceiver
A wireless sensor network consist of spatially distributed autonomous sensor nodes for data acquisition. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. This paper describes the localization of sensor nodes using range measurements and trilateration with the Extended Kalman Filter. The commercially available nanoLOC sensor network is utilized for range measurements. Experimental results show, that a localization accuracy better than 0.5 m can be achieved in most cases.
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