Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks

FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods

Energy-efficient routing protocols in heterogeneous wireless sensor networks

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. The usage of cheap and tiny wireless sensors will allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Such large-scale deployments will require routing protocols that scale to large network sizes in an energy-efficient way. This thesis addresses the design of such network routing methods. A classification of existing routing protocols and the key factors in their design (i.e., hardware, topology, applications) provides the motivation for the new three-tier architecture for heterogeneous networks built upon a generic software framework (GSF). A range of new routing algorithms have hence been developed with the design goals of scalability and energy-efficient performance of network protocols. They are respectively TinyReg - a routing algorithm based on regular-graph theory, TSEP - topological stable election protocol, and GAAC - an evolutionary algorithm based on genetic algorithms and ant colony algorithms. The design principle of our routing algorithms is that shortening the distance between the cluster-heads and the sink in the network, will minimise energy consumption in order to extend the network lifetime, will achieve energy efficiency. Their performance has been evaluated by simulation in an extensive range of scenarios, and compared to existing algorithms. It is shown that the newly proposed algorithms allow long-term continuous data collection in large networks, offering greater network longevity than existing solutions. These results confirm the validity of the GSF as an architectural approach to the deployment of large wireless sensor networks

Analysis of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol

Sensor network consists of tiny sensors and actuators with general purpose computing elements to cooperatively monitor physical or environmental conditions, such as temperature, pressure, etc. Wireless Sensor Networks are uniquely characterized by properties like limited power they can harvest or store, dynamic network topology, large scale of deployment. Sensor networks have a huge application in fields which includes habitat monitoring, object tracking, fire detection, land slide detection and traffic monitoring. Based on the network topology, routing protocols in sensor networks can be classified as flat-based routing, hierarchical-based routing and location-based routing. These protocols are quite simple and hence are very susceptible to attacks like Sinkhole attack, Selective forwarding, Sybil attack, Wormholes, HELLO flood attack, Acknowledgement spoofing or altering, replaying routing information. Low Energy Adaptive Clustering Hierarchy (LEACH) is an energy-efficient hierarchical-based routing protocol. Our prime focus was on the analysis of LEACH based upon certain parameters like network lifetime, stability period, etc. and also the effect of selective forwarding attack and degree of heterogeneity on LEACH protocol. After a number of simulations, it was found that the stability region’s length is considerably increased by choosing an optimal value of heterogeneity; energy is not properly utilized and throughput is decreased in networks compromised by selective forwarding attack but the number of cluster-heads per round remains unaffected in such networks

Energy-Efficient Routing Control Algorithm in Large-Scale WSN for Water Environment Monitoring with Application to Three Gorges Reservoir Area

Published version of an article in the journal: The Scientific World Journal. Also available from the publisher at: http://dx.doi.org/10.1155/2014/802915 Open AccessThe typical application backgrounds of large-scale WSN (wireless sensor networks) for the water environment monitoring in the Three Gorges Reservoir are large coverage area and wide distribution. To maximally prolong lifetime of large-scale WSN, a new energy-saving routing algorithm has been proposed, using the method of maximum energy-welfare optimization clustering. Firstly, temporary clusters are formed based on two main parameters, the remaining energy of nodes and the distance between a node and the base station. Secondly, the algorithm adjusts cluster heads and optimizes the clustering according to the maximum energy-welfare of the cluster by the cluster head shifting mechanism. Finally, in order to save node energy efficiently, cluster heads transmit data to the base station in single-hop and multihop way. Theoretical analysis and simulation results show that the proposed algorithm is feasible and advanced. It can efficiently save the node energy, balance the energy dissipation of all nodes, and prolong the network lifetime

Determining the State of the Sensor Nodes Based on Fuzzy Theory in WSNs

The low-cost, limited-energy, and large-scale sensor nodes organize wireless sensor networks (WSNs). Sleep scheduling algorithms are introduced in these networks to reduce the energy consumption of the nodes in order to enhance the networklifetime. In this paper, a novel fuzzy method called Fuzzy Active Sleep (FAS) is proposed to activate the appropriate nodes of WSNs. It uses the selection probability of nodes based on their remaining energy and number of previous active state. Theproposed method focuses on a balanced sleep scheduling in order to belong the network lifetime. Simulation results show that the proposed method is more efficient and effective than the compared methods in terms of average network remaining energy, number of nodes still alive, number of active state, and network lifetime.