Efficient Data Gathering in Wireless Sensor Networks Based on Matrix Completion and Compressive Sensing

Gathering data in an energy efficient manner in wireless sensor networks is an important design challenge. In wireless sensor networks, the readings of sensors always exhibit intra-temporal and inter-spatial correlations. Therefore, in this letter, we use low rank matrix completion theory to explore the inter-spatial correlation and use compressive sensing theory to take advantage of intra-temporal correlation. Our method, dubbed MCCS, can significantly reduce the amount of data that each sensor must send through network and to the sink, thus prolong the lifetime of the whole networks. Experiments using real datasets demonstrate the feasibility and efficacy of our MCCS method

Transform-based Distributed Data Gathering

A general class of unidirectional transforms is presented that can be computed in a distributed manner along an arbitrary routing tree. Additionally, we provide a set of conditions under which these transforms are invertible. These transforms can be computed as data is routed towards the collection (or sink) node in the tree and exploit data correlation between nodes in the tree. Moreover, when used in wireless sensor networks, these transforms can also leverage data received at nodes via broadcast wireless communications. Various constructions of unidirectional transforms are also provided for use in data gathering in wireless sensor networks. New wavelet transforms are also proposed which provide significant improvements over existing unidirectional transforms

Data Aggregation & Transfer in Data Centric Network Using Spin Protocol in WSN

The advancement in the wireless communications and electronics has led to the growth of low-cost sensor networks. Due to which the sensor networks is part of different application areas now. Low-cost, low-power and multifunctional small-sized sensor devices are the great end-products of wireless sensor network technologies. These sensor nodes together in a group form a sensing network. A sensor network can offer access to data anytime, anywhere by gathering, processing, evaluating and distributing data. The evolution of information sending in wireless sensor networks is boosting to devise newer and more advanced routing strategies. Many strategies have considered data collection and data dissemination. In this project, the data produced by the sensor nodes is aggregated and provide the further guaranteed data transmission to sink node/ base station using clustering mechanism and node concentration with SPIN protocol. The proposed scheme provides increased network lifetime, better data gathering and period of stability as compared to M-LEACH protocol

AUV Data Gathering in Underwater Wireless Sensor Networks

[[notice]]補正完畢[[conferencetype]]兩岸[[conferencedate]]20150712~20150714[[ispeerreviewed]]Y[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]安徽省滁州市[[countrycodes]]CH

Optimal energy balanced data gathering in wireless sensor networks

Unbalanced energy consumption is an inherent problem in wireless sensor networks where some nodes may be overused and die out early, resulting in a short network lifetime. In this paper, we investigate the problem of balancing energy consumption for data gathering sensor networks. Our key idea is to exploit the tradeoff between hop-by-hop transmission and direct transmission to balance energy dissipation among sensor nodes. By assigning each node a transmission probability which controls the ratio between hop-by-hop transmission and direct transmission, we formulate the energy consumption balancing problem as an optimal transmission probability allocation problem. We discuss this problem for both chain networks and general networks. Moreover, we present the solution to compute the optimal number of sections in terms of maximizing the network lifetime. Numerical results demonstrate that our methods outperform the traditional hop-by-hop and direct transmission schemes and achieve significant lifetime extension especially for dense sensor networks.Haibo Zhang, Hong Shen, Yasuo Ta

INVESTIGATION ON ENERGY BASED DATA GATHERING APPROACH FOR WSN

Wireless Sensor Networks plays a vital role in all emerging areas of Wireless Platforms like Interne of Things (IoT), WiFi, WiMAX etc. Sensor nodes are communicated with or without the presence of administrator. Data gathering is a major issue in WSN which influences the throughput, energy and data delivery. In previous research, there was not taken efforts to focus on balanced data gathering.  In this research, we propose Reliable Energy Efficient Data Gathering Approach (REEDGA) to balance data gathering and overhead. To achieve this, proposed work consists of three phases. In first phase, estimation of information gathering is implemented through stable paths. Stable paths are found based on link cost. In second phase, data gathering phase is initialized to save energy in the presence of mobile sensor nodes. Overhead is kept low while keeping round trip time of gathered data. From the analytical simulation using NS2, the proposed approach achieves better performance in terms of data delivery rate, data gathering rate, throughput, delay, link availability and control overhead

Random traveling wave pulse coupled oscillator (RTWPCO) algorithm of energy-efficient wireless sensor networks

Energy-efficient pulse-coupled oscillators have recently gained significant research attention in wireless sensor networks, where the wireless sensor network applications mimic the firefly synchronization for attracting mating partners. As a result, it is more suitable and harder to identify demands in all applications. The pulse-coupled oscillator mechanism causing delay and uncharitable applications needs to reduce energy consumption to the smallest level. To avert this problem, this study proposes a new mechanism called random traveling wave pulse-coupled oscillator algorithm, which is a self-organizing technique for energy-efficient wireless sensor networks using the phase-locking traveling wave pulse-coupled oscillator and random method on anti-phase of the pulse-coupled oscillator model. This technique proposed in order to minimize the high power utilization in the network to get better data gathering of the sensor nodes during data transmission. The simulation results shown that the proposed random traveling wave pulse-coupled oscillator mechanism achieved up to 48% and 55% reduction in energy usage when increase the number of sensor nodes as well as the packet size of the transmitted data compared to traveling wave pulse-coupled oscillator and pulse-coupled oscillator methods. In addition, the mechanism improves the data gathering ratio by up to 70% and 68%, respectively. This is due to the developed technique helps to reduce the high consumed energy in the sensor network and increases the data collection throughout the transmission states in wireless sensor networks

Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks

Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information to multiple sinks and reduce energy consumption