Cluster Based Algorithm for Energy Conservation and Lifetime Maximization in Wireless Sensor Networks

One of the most critical issues in designing Wireless Sensor Network (WSN) is to minimize the energy consumption. In Wireless Sensor Networks, data aggregation reduces the redundancy among sensed data and optimal sensor routing algorithm provides strategy for data gathering with minimum energy. The energy consumption is reduced by combining data fusion and cluster based routing. In this paper, we propose a K-means Fusion Steiner Tree (KFST) for energy efficient data gathering in sensor networks, which optimizes data transmission cost and the data fusion cost. This cost reduction increases the lifetime of a Sensor Network. The result of the proposed protocol KFST is compared with Adaptive Fusion Steiner Tree (AFST) and KFST produces better result than the existing protocols

Conceptual Analysis for Timely Social Media-Informed Personalized Recommendations

Integrating sensor networks and human social networks can provide rich data for many consumer applications. Conceptual analysis offers a way to reason about real-world concepts, which can assist in discovering hidden knowledge from the fused data. Knowledge discovered from such data can be used to provide mobile users with location-based, personalized and timely recommendations. Taking a multi-tier approach that separates concerns of data gathering, representation, aggregation and analysis, this paper presents a conceptual analysis framework that takes unified aggregated data as an input and generates semantically meaningful knowledge as an output. Preliminary experiments suggest that a fusion of sensor network and social media data improves the overall results compared to using either source of data alone

Data gathering with multi-attribute fusion in wireless sensor networks

This chapter addresses the problem of data gathering with multi-attribute fusion over a bandwidth and energy constrained wireless sensor network (WSN). As there are strong correlations between data gathered from sensor nodes in close physical proximity, effective in-network fusion schemes involve minimizing such redundancy and hence reducing the load in wireless sensor networks. Considering a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets; hence, the complexity for the fusion process is increased due to the existence of various physical attributes. In this chapter, by investigating the process and performance of multi-attribute fusion in data gathering of WSNs, we design a self-adaptive threshold to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Then, a novel energy equilibrium routing method is proposed to balance and save energy in WSNs, which is named multi-attribute fusion tree (MAFT). The establishment of MAFT is determined by the remaining energy of sensor nodes and the energy-conservation efficiency of data fusion. Finally, the energy saving performance of the scheme is demonstrated through comprehensive simulations. The chapter is concluded by identifying some open research issues on this topic

A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks

Wireless sensor networks consist of sensor nodes that are physically distributed over different locations. Spatial filtering procedures exploit the spatial correlation across these sensor signals to fuse them into a filtered signal satisfying some optimality condition. However, gathering the raw sensor data in a fusion center to solve the problem in a centralized way would lead to high energy and communication costs. The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve these signal fusion problems in a distributed fashion, which reduces the communication and energy costs in the network. The DASF framework assumes that there is a common goal across the nodes, i.e., the optimal filter is shared across the network. However, many applications require a node-specific objective, while all these node-specific objectives are still related via a common latent data model. In this work, we propose the DANSF algorithm which builds upon the DASF framework, and extends it to allow for node-specific spatial filtering problems.Comment: 5 page

The Hybrid Algorithm for Data Collection over a Tree Topology in WSN

Wireless sensor networks have wide range of application such as analysis of traffic, monitoring of environmental, industrial process monitoring, technical systems, civilian and military application. Data collection is a basic function of wireless sensor networks (WSN) where sensor nodes determine attributes about a phenomenon of concern and transmits their readings to a common base station(sink node). In this paper, we use contention-free Time Division Multiple Access (TDMA) support scheduling protocols for such data collection applications over tree-based routing topology. We represent a data gathering techniques to get the growing capacity, routing protocol all along with algorithms planned for remote wireless sensor networks. This paper describes about the model of sensor networks which has been made workable by the junction of micro-electro-mechanical systems technologies, digital electronics and wireless communications. Firstly the sensing tasks and the potential sensor network applications are explored, and assessment of factors influencing the design of sensor networks is provided. In our propose work using data compression and packet merging techniques; or taking advantage of the correlation in the sensor readings. Consider continuous monitoring applications where perfect aggregation is achievable, i.e., every node is capable of aggregate the entire packets expected from its children as well as that generate by itself into a particular packet before transmit in the direction of its sink node or base station or parent node. Keyword: Aggregation, Data Converge-cast, Data fusion, Energy Efficiency, Routing and TDMA