In most wireless sensor network (WSN) applications, data are typically gathered by sensor nodes and reported to a data collection point called sink. To support such a data collection pattern, a tree structure rooted at the sink is defined. Depending on various factors, including the WSN topology and the availability of resources, the energy consumption of nodes in different paths of the data collection tree may vary largely, thus affecting the overall network lifetime. This paper addresses the problem of lifetime maximization of WSNs based on data collection trees. Specifically, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai), that aims at extending the lifetime of WSNs through load balancing. Given an initial data collection tree, RaSMaLai randomly switches some sensor nodes from their original paths to other paths with lower load. We prove that, under appropriate settings of the operating parameters, RaSMaLai converges with a low time complexity. We further design a distributed version of our algorithm. Through an extensive performance evaluation study that includes simulation of large-scale scenarios and real experiments on a WSN testbed, we show that the proposed RaSMaLai algorithm and its distributed version achieve a longer network lifetime than the state-of-the-art solutions.

Participatory sensing is an approach to data collection where individuals use their smartphones to capture different scenarios, and upload to a server to create knowledge. A fundamental issue in such data collection process is the coverage of the collected data that reflects how well the targeted area is monitored. To ensure coverage requirements, the traditional approaches require the server to know the location of the participating mobile devices that takes significant amount of energy. In this paper, we propose a novel framework called PLUS for data collection in participatory sensing ensuring partial coverage, where a mobile device does not send location updates to the server. We also propose an energy efficient localization scheme called sLoc, designed for a mobile device to participate in the data collection process with PLUS. Finally, simulating a continuous monitoring application, we show that PLUS can save significant amount of energy, as compared to traditional approaches.

Participatory sensing is an approach to data collection for monitoring different scenarios with the help of smartphone sensors. As more and more sensors are being added to smartphones, monitoring a wide range of scenarios has become possible with participatory sensing. An important issue in such participatory sensing application is the coverage of the collected data that reflects how well the data samples represent the monitored area. In the traditional approach, the data collection process is assisted by a server that knows the location of the participating devices and selects the necessary ones to cover the monitored area efficiently. However, for battery powered devices like smartphones, sending frequent location updates to the server is quite energy expensive. In this paper, we propose a framework, called STREET, for data collection from urban streets that can address the coverage problem where a participating mobile device is not required to send location updates to the server. In particular, our framework can collect data samples to ensure the requirements of a specified partial coverage, full coverage and k-coverage. STREET is assisted by a simple localization scheme for mobile devices that minimizes the usage of location sensor (e.g., GPS) while participating in the data collection process. Experiments from simulation studies show that our approach can significantly reduce energy consumption of the participating mobile devices.

Periodic data collection is an important application in wireless sensor networks (WSNs). Since sensors are power constrained, building energy efficient data collection topology offers significant challenge. Compression of correlated data is one of the widely used techniques in WSNs where sensory data are compressed along their routes toward the sink. Consequently, a data compression tree is formed in which a sensor (say, child) selects its parent based on the degree of correlation among their sensed data, and the data of the child node is compressed at the parent node. In periodic data collection, the data collected by individual sensors can be considered as a time series. The amount of correlation between time series of two sensory data streams may not be constant over time. Most existing works in this direction do not consider the temporal effect of correlation among data streams generated by periodic sensing. Moreover, the compression can introduce some imperfection that may affect the reliability of the collected data. In this paper, we address the problem of energy efficient data gathering in WSNs while considering variability of correlation among data streams of neighboring sensors. We propose a bucket approximation based framework named EFFECT (energy efficient framework for compression tree) that produces a compression tree based on the compression ratio of data streams from the neighboring sensors in a given sensor network. We perform experiments on real data sets and show that our framework can produce trees with significantly higher lifetime while reducing the average energy consumption of the sensors by at least 20%.

In most wireless sensor network (WSN) applications, data are typically gathered by the sensor nodes and reported to a data collection point, called the sink. In order to support such data collection, a tree structure rooted at the sink is usually defined. Based on different aspects, including the actual WSN topology and the available energy budget, the energy consumption of nodes belonging to different paths in the data collection tree may vary significantly. This affects the overall network lifetime, defined in terms of when the first node in the network runs out of energy. In this paper, we address the problem of lifetime maximization of WSNs in the context of data collection trees. In particular, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai) that aims at maximizing the lifetime of WSNs through load balancing with a low time complexity. We further design a distributed version of our algorithm, called D-RaSMaLai. Simulation results show that both the proposed algorithms outperform several existing approaches in terms of network lifetime. Moreover, RaSMaLai offers lower time complexity while the distributed version, D-RaSMaLai, is very efficient in terms of energy expenditure.