A Hybrid Algorithm for Reliable and Energy-efficient Data Gathering in Wireless Sensor Networks

Reliability and energy efficiency are two important requirements of the data gathering process in wireless sensor networks. Accordingly, we propose a novel data gathering algorithm which meets these requirements. The proposed scheme categorizes the sensed data into valuable and normal data and handles each type of data based on its demands. The main requirement of valuable data is reliability. Thus, the adopted strategy to gather this type of data is to send several copies of data packets toward the sink. The rise of energy exhaustion in this scheme is tolerable. This is due to that, the valuable data is generated at a low rate. On the other hand, our main concern in gathering normal data is energy efficiency. As most of the sensed data is normal, an energy-efficient approach to gather normal data results in considerable energy conserving. Thus, we exploit clustering technique for normal data gathering. We also propose a lightweight intrusion detection system to detect malicious nodes. Simulation results and theoretical analysis confirm that our proposed algorithm provides reliability and energy efficiency at an acceptable level

Network correlated data gathering with explicit communication: NP-completeness and algorithms

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal

A Efficiency & Latency based Compression of Hierarchical Network and Flat Network

Wireless Sensor Network (WSN) compromised of maximum number sensor nodes which cooperatively send data to base station. These networks are worn in various applications outline such as habitat monitoring, environment, military, and security, etc. As the sensor nodes are broadly operated over battery driven, an efficient utilization of power is essential. Therefore, to increase the lifetime of a sensor network, power efficient methods has to be fitting to choose and aggregate data. It's essential because of the majority

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

Wireless sensor network as a distribute database

Wireless sensor networks (WSN) have played a role in various fields. In-network data processing is one of the most important and challenging techniques as it affects the key features of WSNs, which are energy consumption, nodes life circles and network performance. In the form of in-network processing, an intermediate node or aggregator will fuse or aggregate sensor data, which are collected from a group of sensors before transferring to the base station. The advantage of this approach is to minimize the amount of information transferred due to lack of computational resources. This thesis introduces the development of a hybrid in-network data processing for WSNs to fulfil the WSNs constraints. An architecture for in-network data processing were proposed in clustering level, data compression level and data mining level. The Neighbour-aware Multipath Cluster Aggregation (NMCA) is designed in the clustering level, which combines cluster-based and multipath approaches to process different packet loss rates. The data compression schemes and Optimal Dynamic Huffman (ODH) algorithm compressed data in the cluster head for the compressed level. A semantic data mining for fire detection was designed for extracting information from the raw data by the semantic data-mining model is developed to improve data accuracy and extract the fire event in the simulation. A demo in-door location system with in-network data processing approach is built to test the performance of the energy reduction of our designed strategy. In conclusion, the added benefits that the technical work can provide for in-network data processing is discussed and specific contributions and future work are highlighted