A Sensor Network Data Compression Algorithm Based on Suboptimal Clustering and Virtual Landmark Routing Within Clusters

A kind of data compression algorithm for sensor networks based on suboptimal clustering and virtual landmark routing within clusters is proposed in this paper. Firstly, temporal redundancy existing in data obtained by the same node in sequential instants can be eliminated. Then sensor networks nodes will be clustered. Virtual node landmarks in clusters can be established based on cluster heads. Routing in clusters can be realized by combining a greedy algorithm and a flooding algorithm. Thirdly, a global structure tree based on cluster heads will be established. During the course of data transmissions from nodes to cluster heads and from cluster heads to sink, the spatial redundancy existing in the data will be eliminated. Only part of the raw data needs to be transmitted from nodes to sink, and all raw data can be recovered in the sink based on a compression code and part of the raw data. Consequently, node energy can be saved, largely because transmission of redundant data can be avoided. As a result the overall performance of the sensor network can obviously be improved

A Data Compression Algorithm for Wireless Sensor Networks Based on an Optimal Order Estimation Model and Distributed Coding

In many wireless sensor network applications, the possibility of exceptions occurring is relatively small, so in a normal situation, data obtained at sequential time points by the same node are time correlated, while, spatial correlation may exist in data obtained at the same time by adjacent nodes. A great deal of node energy will be wasted if data which include time and space correlation is transmitted. Therefore, this paper proposes a data compression algorithm for wireless sensor networks based on optimal order estimation and distributed coding. Sinks can obtain correlation parameters based on optimal order estimation by exploring time and space redundancy included in data which is obtained by sensors. Then the sink restores all data based on time and space correlation parameters and only a little necessary data needs to be transmitted by nodes. Because of the decrease of redundancy, the average energy cost per node will be reduced and the life of the wireless sensor network will obviously be extended as a result

Antioxidants: nanotechnology and biotechnology fusion for medicine in overall

Antioxidant is a chemical substance that is naturally found in our food. It can prevent or reduce the oxidative stress of the physiological system. Due to the regular usage of oxygen, the body continuously produces free radicals. Excessive number of free radicals could cause cellular damage in the human body that could lead to various diseases like cancer, muscular degeneration and diabetes. The presence of antioxidants helps to counterattack the effect of these free radicals. The antioxidant can be found in abundance in plants and most of the time there are problems with the delivery. The solution is by using nanotechnology that has multitude potential for advanced medical science. Nano devices and nanoparticles have significant impact as they can interact with the subcellular level of the body with a high degree of specificity. Thus, the treatment can be in maximum efficacy with little side effect

Big Data Reduction and Optimization in Sensor Monitoring Network

Wireless sensor networks (WSNs) are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel

Joint routing protocol and image compression algorithm for prolonging node lifetime in wireless sensor network

Wireless sensor network (WSN) are among the emerging modern technologies, with a vast range of application in different areas. However, the current WSNs technology faces a key challenge in terms of node lifetime and network connectivity due to limited power resource of the node. The conventional data routing protocols do not consider the power available at the node on the path from source to sink, thus they result in the exhaustion and eventual death of nodes surrounding the sink node, thus generating routing holes reducing the network throughput. In order to address the issue in this research presents a novel protocol based on equal power consumption at all network nodes. The consume power fairly (CPF) protocol achieves a high power efficiency by distributing power consumption equal on all the network nodes. The protocol compares the power available on all the paths from source to sink and then selects the path with highest power. Additionally in order to reduce the transmitted data size, a lossy image compression technique based on adaptive Haar wavelet transform has been implemented. The simulation designs based on MATLAB consists of 100 randomly distributed nodes over an area of 100 m2, with 30 Kbits and 40 Kbits of packet sizes. The comparison between the proposed CPF protocol and the energy aware protocol has been carried out on the basis of number of iterations and the dead nodes in the network. Thorough simulations have been carried out based on different number of network iterations to validate the potential of the proposed solution. Moreover the implemenetation of multiscale retinex technique results in image enhancement and impoved classification. An implementation of the CPF protocol and image compression technique on a 100 node network with 500 iterations, results in the death of 13 nodes as compard to 38 dead nodes with energy aware protocol for the same network. Thus the performance comparision of CPF and energy aware protocol demonstrates an improvement of 81.19% for the energy consumption of the network. Thus the proposed algorithm prolongs the network under consideration by 57 – 62% as compared to networks with conventional routing protocols