Energy-Efficient Data Acquisition in Wireless Sensor Networks through Spatial Correlation

The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the 'Virtual Sampling Scheme' (VSS). This scheme is formed from two components: an algorithm for forming virtual clusters, and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method

Adaptive MAC protocol for wireless sensor networks in periodic data collection applications

In this paper, we propose a new medium access control (MAC) protocol for wireless sensor networks for environmental monitoring applications. The proposed MAC scheme is specifically designed for wireless sensor networks which have periodic traffic with different sampling rates. In our protocol design, only sink can start and maintain synchronization and also determine the time schedule for all other nodes in the network. We discuss the design of TA-PDC-MAC protocol and provide a comparison with the previous PDC-MAC protocol through simulation. Under different traffic generation rate, our protocol outperforms the previous one in terms of energy consumption, packet loss rate and packet delay

Traffic Adaptive Schedule-Based Mac Protocol For Wirelesssensor Networks

Wireless sensor networking is an emerging technology that has a wide range of potential applications inc1uding monitoring, medical systems, real-time, robotic exploration and etc. Energy is a critical resource in battery-powered sensor networks. Medium access control has an important role in minimizing energy consumption while it is responsible for successful data transferring in the network. Periodic data collection is the most comprehensive way of data gathering mechanism in wireless sensor network in which nodes report their samples in specific time interval s . It is possible to h ave some nodes with different update interval s in the network and therefore, finding a solution to accommodate nodes with different sampling intervals while maintaining the energy efficiency is the primary concern of this thesis

Changes of data sampling procedure to avoid energy and data losses during microclimates monitoring with wireless sensor networks.

Wireless sensor networks are gaining importance in agricultural applications, such as monitoring crops microclimates. Precision agriculture is one of the areas that can most benefit from this technology in the sense that wireless sensors networks allow data collection with high resolution, enabling better decision making. Such networks have restrictions on their deployment in a real environment, for example, on energy. Thus, several studies have been conducted in order to optimize the use of this technology. Depending on the application, it is desirable that the available energy on sensor nodes batteries allows operation for months or even years. One proposed solution to extend the lifetime of sensor nodes, so as to avoid unnecessary data collection, is the implementation of a routing protocol that allows a differentiated data sampling. An application that can benefit from this approach is vineyard microclimates monitoring, which is very important to monitor temperature and reIative humidity, and can apply precision agriculture techniques to the crop. Thus, in the program to be installed into sensor nodes, rules for data collection are defined, so that the value collected by the sensor at a given time is in the rule that defines normal conditions, the rate of sampling data used will be higher; however, when the value collected by the sensor is out ofthis rule, the sampling rate will automatically be reprogrammed to a higher value. This differentiated data collection allows savings in power consumption under normal conditions, and generates less data to be analyzed. Keywords: Wireless sensor network, microclimates monitoring, vineyards differentiated data samplin

Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks

The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay

Comparison of CSMA based MAC protocols of wireless sensor networks

Energy conservation has been an important area of interest in Wireless Sensor networks (WSNs). Medium Access Control (MAC) protocols play an important role in energy conservation. In this paper, we describe CSMA based MAC protocols for WSN and analyze the simulation results of these protocols. We implemented S-MAC, T-MAC, B-MAC, B-MAC+, X-MAC, DMAC and Wise-MAC in TOSSIM, a simulator which unlike other simulators simulates the same code running on real hardware. Previous surveys mainly focused on the classification of MAC protocols according to the techniques being used or problem dealt with and presented a theoretical evaluation of protocols. This paper presents the comparative study of CSMA based protocols for WSNs, showing which MAC protocol is suitable in a particular environment and supports the arguments with the simulation results. The comparative study can be used to find the best suited MAC protocol for wireless sensor networks in different environments.Comment: International Journal of AdHoc Network Systems, Volume 2, Number 2, April 201

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption