Research on Database Design Based on Wireless Sensor Network

Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN) NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH) is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH) is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

The sensor nodes in wireless sensor network (WSN) come with limited battery life, thus energy usage in WSN is to be handled with great care. The routing protocols are the major research area to work on the QoS of the network such as network lifetime, scalability, energy consumption, packet overhead, etc. Since sensor node posses limited battery life, to use nodes efficiently yet not loosing connectivity in the network becomes a major issue in designing the routing protocols. To achieve energy efficiency and better network lifetime, grouping of sensor nodes into small clusters and selecting one cluster head seems to have more advantages in comparison with other network models to get better scalability, robustness and end-to-end data delivery. In this paper, we have discussed and studied the different energy-efficient routing protocols for clustering of sensor nodes in wireless sensor network, its merits, demerits and applications.

In wireless sensor networks, sensor nodes have limited battery power. Data aggregation is an important technique in a wireless sensor network to reduce the energy consumption by eliminating the redundant data and delivering useful data to the user. Data aggregation technique is used to gather the data sensed by the sensor nodes in the network and aggregates the sensed data in an energy-efficient way so that overall network lifetime can be increased. Cluster, tree, chain, and grid are some of the architectures for data aggregation in wireless sensor networks. In this paper, authors present an analysis-based survey of data aggregation protocols for tree-based architecture in wireless sensor networks. The authors analyze each algorithm on the basis of performance measurements such as network lifetime, energy consumption, and node distance. Authors also compare these algorithms on the basis of the preferred parameter and a key feature of each algorithm. In the fifth section of the paper, authors described the proposed approach to construct the data aggregation tree to maximize the network lifetime. In the proposed approach, authors used distance parameter to construct minimum spanning tree, and other parameters such as load and energy are preferred in a balanced manner to reduce the energy consumption and to maximize the network lifetime.

In this letter, we propose two novel dynamic duty-cycle scheduling schemes (called DSR and DSP) in order to reduce sleep latency, while achieving balanced energy consumption among sensor nodes in wireless sensor networks (WSNs) with energy harvesting capability. In DSR, each sensor node is allowed to adjust its duty-cycle according to the current amount of residual energy only. Since the residual energy of nodes in energy-harvesting WSNs can increase over time due to their harvesting opportunity, the estimation of prospective increase in their residual energy is useful to achieve our goal. Hence, DSP allows each of sensor nodes to reduce its duty-cycle more aggressively in proportion to such an increase. Through NS-2 simulations, we verified that our proposed schemes outperform the duty-cycle scheduling scheme used in a representative existing MAC protocol such as RI-MAC.

The sensor nodes are independent and work as sender, receiver and intermediate node in Wireless Sensor Network (WSN). The sensor nodes are forming the small network in which the one or more sender is sending data to one destination/s through intermediate nodes or sending information to Base Station (BS). The information collect by base station is further analyzed. The nodes are also static or mobile depends on the situation or conditions. The connection establishment and data sending is possible through routing protocols of WSN. The routing protocols of WSN are not same as traditional wireless routing protocols. One major issue on this network is security. The routing protocols are sending information to destination from particular source and the routing strategy is provides the better way of delivery in the network. The sensor nodes in network are performing through limited battery supply called node energy or power. The attacker are also affected the limited energy resource. In this paper the center of consideration is on the security issues related to WSN routing protocols. The routing in WSN remains a key issue because of that without accurately functioning of routing protocols, the network cleanly will not work the technique it's intended to. Unfortunately, routing also one of the most difficult to protect against attacks malicious activities because of the administration in WSN. The better protection scheme is provides the complete security from attacks and the secure routing is provides the better and trustful network performance.

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

A Markov based unified model for energy harvesting nodes in wireless sensor networks is proposed. Using the presented model, the probability of event loss due to energy run out as well as an analytical vulnerability metric, namely average time to energy run-out, are derived. The results provide insight into the performance of energy harvesting nodes in wireless sensor networks as well as design requirements for such nodes. The proposed vulnerability metric can be used in the various harvesting aware techniques at different protocol layers.

Efficient sensor node deployment is extremely important in wireless sensor networks. It earns great practical meanings through using fewer sensor nodes as far as possible to satisfy different requirements such as the requirement on coverage and overcoming the potential sensor node failures and the adverse influence from the environment. We propose an efficient approach for the deployment of sensor nodes in wireless networks, termed as EDSNDA, which is excellent in taking both the requirements of sensor coverage and network connectivity into consideration when minimizing the number of necessary sensor nodes to the best of its ability. We proposed a new coverage model of sensor node. Based on the sensor coverage model, we establish four dynamic programming models in four different practical situations, respectively. The algorithms are then proposed which are used for solving the corresponding dynamic programming models. The validity of the method is justified by simulation studies in which the method is compared with the current representative methods. The simulation results show that our method performs better than the other ones with fewer sensor nodes, better coverage and network connectivity result in the same circumstance.

A wireless sensor network is a special kind of ad-hoc network with distributed sensing and processing capability that can be used in a wide range of applications, such as environmental monitoring, industrial applications and precision agriculture. Despite their potential applications, such networks have particular features imposed by resource restrictions, such as low computational power, reduced bandwidth and specially limited power source. In case of a network with a high density of sensor nodes, some problems may arise such as the intersection of sensing area, redundant data, communication interference, and energy waste. A management application is necessary to make the most of network resources. On the other hand, a high-density network can introduce a fault-tolerant mechanism, increase precision, and provide multi-resolution data. The network density control depends on the application. In this paper, we propose a method to set up which nodes should be turned off or on. The management may take the sensor node out of service temporally. Our design uses a Voronoi diagram, which decomposes the space into regions around each node. That schema could be used in management architecture for a wireless sensor network.

The increasing interest of researchers in service oriented architecture (SOA) for wireless sensor networks (WSNs) is opening new unexplored venues in the field of WSNs. In service oriented systems, services are configured and composed of various other services and thus perform complex tasks. In such composite services, the geospatial locations of services and their coverage is of vital importance as they signify the geospatial relevance of the service to the area of interest to the user. In this paper, we present a service-oriented system for WSNs that is capable of performing service configuration under geospatial and relevancy constraints. We present and evaluate "Cost Based Model (CBM)" and "Gain Based Model (GBM)" approaches to capture the relevancy of services hosted on WSN nodes in composite service configuration. The system is resilient to failures and can operate in manual or autonomous recovery modes. The system supports three service configuration methods namely, distributed, centralized and hybrid. Furthermore, we present a novel emulation mechanism for testing the performance of our proposed relevancy models and show that our system efficiently configures services.

Self-powered wireless sensor networks based on the radioisotope thermoelectric generator for aquatic temperature monitoring