Sampling In Wireless Sensor Networks Research Articles

Time Triggered Hybrid Scheduler with Dynamic Frequency Scaling for Distributed Real Time Embedded Systems

In this paper, the time triggered hybrid scheduler (TTH) is implemented in the distributed real time embedded systems along with the operation of dynamic frequency scaling (DFS) since it overcomes the problem of time triggered cooperative scheduler (TTC) for distributed systems and also the decreases the power consumption. This scheduler is implemented along with the token ring network protocol for the communication scheduling. In hard real time systems, ideally, no deadline may ever be missed and hence the co-operative and pre-emptive are combined in this paper to achieve optimum efficiency. DFS is used to provide robustness to the network and also reduces the power utilization. This TTH scheduler is more advantageous than TTC scheduler in distributed system, since long task problem can be overcome by means of this algorithm. The model is simulated using the TORSCHE toolbox of the MATLAB. Some of the applications of this scheduling are data acquisition and sampling in wireless sensor networks, control system engineering, patient monitoring using wireless sensor networking and so on.

Proposed Coordinating Multiple Sampling Tasks in Sensor Field Using Geometric Progression Algorithm for Efficient Data Collection in Wireless Sensor Networks

This paper proposes a new coordinating multiple sampling tasks in sensor field using geometric progression (CMSTGP) algorithm technique for enhancing the mobile sampling in wireless sensor networks. It is possible by the sensor nodes to have multiple sampling tasks, initiated by the same or different mobile objects, whose sampling regions overlap. Hence, it is desirable to have an efficient coordination mechanism such that overlapped regions need reply only once for the sampling tasks. A geometric progression technique is proposed in this research work as a coordination mechanism to coordinate the multiple sampling tasks to facilitate the rebroadcast scheme by consuming minimum energy. Experimental simulations have been conducted to estimate the performance of the proposed coordinating multiple sampling tasks in sensor field. The performance of the proposed algorithm has been analyzed in terms of average number of messages, overlap percentage and Throughput. From the simulated results it has been reported that the proposed CMSTGP algorithm reduces the overlapping percentage upto 8 % and increases the throughput of 70 % when compared with existing Band-based Directional Broadcast method.

Comparison of approaches to time-synchronous sampling in wireless sensor networks

Time synchronization is required in many data acquisition applications for sensor networks. A large variety of time synchronization protocols for wireless sensor networks has been suggested. Yet, setting up a sensor network for synchronized data acquisition based on those algorithms is not a trivial task. This paper outlines different approaches to time synchronous sampling in wireless sensor networks. The approaches are implemented on a wireless sensor network. Experiments are done to compare the signal quality of the signals acquired with the different approaches in terms of frequency error, signal-to-noise ratio (SNR) and total-harmonic-distortion (THD). Furthermore, the approaches are compared in terms of the effort required for communication and implementation.

A neural data-driven algorithm for smart sampling in wireless sensor networks

Wireless sensor networks (WSN) take on an invaluable technology in many applications. Their prevalence, however, is threatened by a number of technical difficulties, especially the shortage of energy in sensors. To mitigate this problem, we propose a smart reduction in data communication by sensors. Indeed, in case we have a solution to this end, the components of a sensor, including its radio, can be turned off most of the time without noticeable influence on network operation. Thus, reducing the acquired data, the sensors can be idle for longer and power can be saved. The main idea in devising such a solution is to minimize the correlation between the data communicated. In order to reduce the measurements, we present a data prediction method based on neural networks which performs an adaptive, data-driven, and non-uniform sampling. Evidently, the amount of possible reduction in required samples is bounded by the extent to which the sensed data is stationary. The proposed method is validated on simulated and experimental data. The results show that it leads to a considerable reduction of the number of samples required (and hence also a power saving) while still providing a good approximation of the data.

Decentralized control of adaptive sampling in wireless sensor networks

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximizes the information value of the data collected is a significant research challenge. Within this context, this article concentrates on adaptive sampling as a means of focusing a sensor's energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor's observations to be expressed. We then use this metric to derive three novel decentralized control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naïve nonadaptive manner, in a uniform nonadaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute).

Efficient duty cycling through prediction and sampling in wireless sensor networks

We present BoostMAC, a CSMA-based MAC layer protocol for wireless sensor networks that provides an adjustable interface to achieve ultra-low power operation. To reach low power operation, we adapti...

Efficient duty cycling through prediction and sampling in wireless sensor networks

AbstractWe present BoostMAC, a CSMA‐based MAC layer protocol for wireless sensor networks that provides an adjustable interface to achieve ultra‐low power operation. To reach low power operation, we adaptively set two radio parameters. First, BoostMAC implements a preamble sampling scheme that allows a mote to dynamically set the length of its duty cycles. Second, we apply machine learning such that a sender can predict its destination's channel polling time and set each outgoing packet's preamble length accordingly. Our two improvements require no extra communication overhead, and each mote in the network implements simple, local models. Energy conservation is one of the most fundamental problems in sensor networks, and by optimizing low‐level operation at the MAC layer, sensor network applications realize an increase in overall performance.We choose habitat monitoring as a motivating sensor network application because it has unique MAC layer demands, such as fluctuating levels of network traffic. Static configuration at the MAC layer inhibits optimal energy conservation within habitat monitoring applications. We implemented BoostMAC in TinyOS simulator (TOSSIM), and we compare its performance to B‐MAC, a well‐known low power MAC protocol with static behavior. We found that BoostMAC saves energy relative to B‐MAC in bursty networks without affecting latency. Copyright © 2007 John Wiley & Sons, Ltd.