Deploying ground network monitoring system based on wireless sensor network

New security approach in real-time wireless multimedia sensor networks

Department of Earthquake Engineering of Kandilli Observatory and Earthquake Research Institute (KOERI) has installed and been operating for some time a large number of ground and structural seismic monitoring networks in Istanbul. The ground systems include 100-station Istanbul Rapid Response Network, 10-station Early Warning Network, and one 4-level downhole array. Installation of three more similar downhole arrays, and a small-aperture 72-channel dense array at the Air-Force Academy grounds are currently in progress. The structural systems include 27-channel real-time system at the St. Sophia Museum, 42-channel real-time system at Fatih Mosque, 15-channel real-time system at Fatih Sultan Mehmet Bridge (the 2nd Bosporus bridge), 24-channel triggered system at Suleymaniye Mosque, and 15-channel triggered systems at Kanyon Building (high-rise office building), Isbank Tower (a high-rise office building), and ENRON Power Plant. Installation of more real-time structural monitoring networks are in progress or at planning stages, such as those for Sultanahmet (Blue Mosque), Bayezit, and Mihrimah Sultan Mosques, and the Marmaray Immersed Tube Tunnel under the Bosporus. The objective of the early warning ground network is to provide 5–7 s early warning of an earthquake, which can be used for automatic shutdown of some of the critical systems in Istanbul. The rapid response network provides the critical values of ground shaking (PGA, PGV , and spectral accelerations at 0.2 and 1.0 s) automatically within minutes of an earthquake, which are then transmitted to those agencies dealing with emergency response. Downhole arrays are located in the areas of Istanbul, where site amplification is expected to be critical. The objective in structural arrays is to monitor the health of the structure by continuously measuring its vibrations, and detect and locate damage after an extreme event, such as a large earthquake. A large component of structural monitoring activities is directed towards the research on developing new tools and techniques for system identification and damage detection. In addition to standard modal identification, more advanced real-time techniques are explored, such as wave the propagation approach and interferometric imaging.

Wireless Sensor Networking is a sensor information gathering technology that has a wide range of applications in numerous fields. However due to the fact that sensor nodes have limited battery and are deployed in remote and harsh environments, energy efficient and real time transmission of the information are still open challenges. Since in a wireless sensor network data transmission is the most power consuming task, so far most useful techniques for the purpose of energy efficient and real time transmissions are based on data compression, the majority of them are based on wavelet transform. This paper give performance analysis of different wavelets for energy efficient and real time image data transmission in application like environment monitoring using wireless visual sensor networks. The performance evaluation shows that Haar wavelet is better in terms of energy efficiency and transmission time.

The Wireless Sensor Networks (WSN) particularly for real time applications raise fundamental problems for the scientific community. These problems are related to the limit of energy resource and the real time constraints on the communication delay. The well functioning of such networks depends mainly on the network lifetime result of nodes energies and the communication delay which should meet the required deadlines. Thus, the well design of Real-Time Wireless Sensor Networks must be with the prediction of the energy consumption and the communication delay. Therefore, this paper propose an analytical model to predict the lifetime and the delay in IEEE 802.15.4/ZigBee WSN. Our proposed model is based on realistic assumptions. It considers the most important network features such as idle times from the Backoff, overhearing and interferences by collisions and transmission errors. Compared to simulation results and other analytical approaches, our model gives a reliable lifetime and delay prediction.

THE USE OF WIRELESS SENSOR NETWORKS IN AGRICULTUTE A TOOL FOR A SUSTAINABLE FOOD SUPPLY IN THE ARAB WORLD In this paper some ideas of how to apply the technological advancement of the wireless sensor networks (WSN) in agriculture as a tool for food security is discussed. This will include some examples where the technology can be applied, what changes can this technology brings to the traditional farming, which can help within the solution of the Arab World food security problems. This will help the region to develop and be part of the global strategy for the World food security. WSN features of having a low power consummation nature with different options of using different energy sources will make it as the just technology suitable in a remote and less or without infrastructural bases environment for many countries within the region. The applications of WSN which are suitable for the purpose will be discussed. Keywords: WSN, Food Security, Agriculture in Arab region, WSN Farmer Kits Tool, Energy Sources. Introduction Arabic countries are naturally rich, many countries within the region have a wide virgin land. But unfortunately some of these countries have been a focus in term of food shortages for long time which cause by many factors like land degradation, water crisis, land deals, climate change, agricultural disease, governing system, wars, population growth, land use change, living style change, high prices and lack of technology. As technology has rational parts in solving the most difficult problems for today's society, WSN can be applied in agriculture to reduce or solve this problem. Use of WSN in Agriculture as a solution As traditional farming is the lead in the region, people use their own human sense to perceive the surrounding to investigate the soil and predict the weather. But the remote sensing of the WSN as an activity of recording, observing, perceiving objects or events, wirelessly these happenings are processes and analyzed in a real time. The idea of applying this technology is to be adopted in different applications shortly can be said here. *Soil Investigation by using the specialized soil test sensor detectors to determine the fertility of soil nutrient deficiencies, elements like Nitrogen is very important for plants. *Weather Prediction by using a web data from WSN can be transmitted enabling a remote online interactive retrieval access, and then these data can be shared with the farmers. *Detection of ground water using WSN the level and the amount of sufficient water can be detected before planting. *Using the WSN the water and the area can be controlled during irrigation which will help in reducing the energy consumption. *Food storage system will be advanced by the use of WSN in knowing usage validity time and the space of storing. *Knowing where how and when to deliver what kind of product by using WSN will help in the logistical distribution of crops. *Farming Dissemination by using an integrated WSN tool kit messages can be disseminated.

Recent advances in urban traffic network modeling have led to the proposal of several large-scale control strategies aimed at improving network efficiency, including metering vehicle entry, pricing network use, and allocating limited street space between multiple modes. However, these strategies typically require accurate real-time predictions of networkwide traffic conditions to be implemented, and it is often taken for granted that this information is available. In practice, this is not a trivial issue, because measuring traffic conditions across a large urban network in real time is not straightforward. For that purpose, this paper presents a method of indirectly estimating average vehicle densities across a network in real time by combining travel speed information from a few circulating probe vehicles with the macroscopic fundamental diagram (MFD) of urban traffic. The proposed method is advantageous because it requires relatively little data and involves few calculations. Tests of this methodology on a simulated network showed that the results were not accurate when the network was uncongested, but reliable density estimates could be obtained when the network was congested or approaching congestion, even if only a small fraction of vehicles served as probes. This result is promising because congested states are the most critical. Therefore, this methodology seems useful as a traffic-monitoring scheme to complement networkwide control strategies, provided that the network exhibits a well-defined and reproducible MFD.

Wireless sensor networks (WSN) has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. Now a days, many machine learning and deep learning algorithms are used for improving the real time data transmissions in WSNs. From the survey on existing methods, it is clear that using machine learning makes the computational methods more reliable, powerful and economical. This paper proposes a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work and confirm the accuracy of the proposed ML-MAC protocol strategy is higher than the existing work.

Produced sand in oil pipelines poses a serious problem in many production situations, since a small amount of sand in the produced fluid can result in significant erosion in a very short time stage. Reliable sand detection is an important component of oil production systems; it would be valuable for assisting with optimizing well productivity and detecting sand as early as possible. In this work, a new reliable distributed sand-monitoring framework using a Wireless Sensor Network (WSN) is proposed. The framework combines three modules: Data Acquisition (DA) module, Wireless Receiving and Transmission (WRT) module, and Distributed Data Fusion module (DDF). The WRT module implementation is based on TinyOS and Crossbow MICAz mote. The DDF module is based on a resource-aware Distributed Kalman Filter (DKF). The data is collected from pipeline using acoustic sensor (SENACO AS100) and Flow Analyzer (MC-II) in real time. The experimental results validate the proposed framework. Sand production is considered as one of the major problems facing the petroleum industry. In the oil and gas industry, the sand production causes considerable erosion damage in the well tubing, piping, fittings, separators, valves, and other equipment. It can cause poor performance in injection wells, and can lead to lost production. It arises in the case of failure of sand control measures. Sand screening is also a critical part of the mining process (1). Every year, cleaning and repairing operations related to sand production and restricted production rates cost the industry millions of dollars. However, a reliable sand monitor system is essential to decide whether sand control measures need to be installed during well operations or not. Sand monitoring allows timely actions to be taken to handle sand production such as: (a) Increasing inspection to detect erosion; (b) Reducing the flow rate or stopping the production in some of the extreme cases; (c) Installation of sand handling systems (2). The sand problem is not new and affects the entire oil industry. In some cases several tones of sand can come from a reservoir in one day. Deciding when it is necessary to deal with sand is a difficult decision. Several fields have installed a sand detection system. Installation of a system to monitor and measure sand production would be helpful to detect sand as early as possible. Early sand detection would then lead to possible remedial action that could avoid the erosion and improve the system production. However, to be able to do this, we need repeatability, sensitivity, and also real time measurements (3). Oil and gas companies started to integrate their legacy sensors with Wireless Sensor Networks (WSNs) technology to help them to increase production, streamline operations, and reduce expenses. We introduced some solutions toward this direction. In (4), a reliable prototype of a remote measuring of flow rate using a MC-II Flow Analyzer was introduced. In (5), we developed a Wireless Sensor Data Acquisition (WSDA) prototyping. It composed of both legacy and modern sensors. Some other solutions introduced centralized and distributed fusion algorithms for sand detection using WSN (6-9). These fusion algorithms include Fuzzy Art (FA), Maximum Likelihood Estimator (MLE), Moving Average Filter (MAF), and Distributed Kalman Filter (DKF). The DKF showed promising results over the other algorithms. Unlike the other algorithms, Kalman filter does not lend itself for easy implementation in WSN; this is because WSN has limited computation capability, small memory, and limited energy. DKF involves seventeen matrix operations: ten matrix multiplications, two matrix inversions, four matrix additions, and one matrix subtraction. These operations are computationally intensive and could strain the energy resources of any single computational node in WSN. In order to share the information gathered by all the sensors, we encounter fundamental problems: communication congestion and scalability. The scalability issue and communication congestion are closely related in DKF algorithm. The more sensors we add to our system, the more communication we will require. Moreover, the scalability problem is not only related to communication issues, but also to computation problems. Higher dimensional measurement vectors mean higher computational demands. In this paper, a new distributed sand-monitoring framework using WSN is proposed. In order to meet with constraints of WSN, a resource-aware DKF is proposed. The proposed DKF is based on fast polynomial to increase the convergence rate. Fast convergence contributes to energy saving because it saves time. In addition, we reduce the communication traffic by exchanging the estimates between the nodes instead of exchanging the estimates and its variance. Each node uses its measurement, its associated variance, and its neighbors' estimates. This leads not only to significant energy saving, but also reduces the memory usage and avoids congestion. Moreover, we redesigned the DKF to reduce the number of arithmetic operations. Thus, the proposed optimizations make the DKF applicable to be implemented in WSN. The experimental results validate the proposed framework.

Energy minimization by exploiting data redundancy in real-time wireless sensor networks

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

Wireless local area network (WLAN) has changed the paradigm of communication and become ubiquitous. As the number of devices connected to wireless is increasing, it is therefore, essential to find vulnerabilities in the security of wireless networks. In this research WLAN security vulnerabilities are exposed in real time. WLAN security techniques (MAC filtering, Hidden SSID and WPA2) available to residential users were practically exploited. Tenda WiFi wireless access point was set up with MAC filtering, hidden SSID and WPA2 security mechanisms in different combinations in such a way as to add an additional layer of security to previous configuration. Kali Linux version 2017.1 was used as a platform to exploit WLAN networks in real time. In this research, the sensitive information broadcasted by WLAN was utilized to carry out attacks on wireless networks. A practical demonstration of exposing vulnerabilities in MAC filtering, Hidden SSID with MAC filtering and WPA2-PSK with hidden SSID and MAC filtering security mechanisms of AP was carried out in real time. It was observed that the existing security mechanisms were vulnerable. Researchers have exploited many security mechanisms of WLAN focusing upon a single parameter of WLAN security at a time. However, in this work, the three available security mechanisms were cascaded to produce a three layer security mechanism i.e. WPA2 with hidden SSID and MAC filtering. This cascaded model was exploited in real time.

The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.

Wireless sensor networks (WSNs) offer a powerful feasible integration of distributed sensing capability, real-time data analysis, and remote surveillance due to the combined result of miniaturization of electronic devices and availability of powerful computational capability, larger information storage, and ubiquitous Internet connection. With these advances, WSNs are starting to be translated into a new ecological knowledge. They are providing a new insight into the observation of the world in new ways of extended spatial and temporal scales. Through WSNs, more unexpected phenomena can be obtained, and new paradigms can be developed. Recently, more and more ecological WSNs have been established, and lagre WSNs are deployed to monitor habitats with different scales. The research in the temporal scale ranges from the evaluation of soil moisture dynamics at several minutes to daily precipitation. Spatial measurements, on the other hand, range from the evaluation of global climate change to those related to the monitoring of forest and riparian environments in the range of a few meters. Although we are seeing more use of ecological WSNs, opportunities and challenges begin to be realized, including newly better design of software and hardware, formulation of new questions, discovery of previously unobservable phenomena, and development of new sensors, etc.KeywordsSensor NodeWireless Sensor NetworkEnergy HarvestingEcological MonitoringFaulty NodeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

SummaryWith the rapid development of artificial intelligence, the intelligent distribution network can effectively improve the overall power supply quality of the distribution system. The designed distributed power has been able to achieve an optimal operation of grid connection, while the high‐speed operation of distribution equipment can achieve effective interaction with users. At present, intelligent distribution network is widely studied and applied in power industry because of its advantages of high efficiency energy and environmental protection. Because of its unique characteristics, such as low cost and fast speed, wireless sensor network is cleverly applied in intelligent distribution network. However, wireless sensor network has some defects in energy utilization due to its large range and too many nodes. This paper presents a cross‐layer cooperative control model, which can control the real time and reliability of network power data communication through stack protocol layer. This method can learn the weight value adaptively by training the influence relationship between the parameters of each protocol layer and achieve real‐time and reliable data communication by adjusting the policy and parameters of each layer in real time. This method takes the parameters of each wireless sensor node as the unit to establish the influence relationship between each other so as to realize the connection between each node of wireless sensor network and then to control the wireless sensor network as a whole to ensure the real‐time reliability of data communication in intelligent distribution network. In this paper, a lot of simulation experiments have been done, and the results show that the proposed cross‐layer cooperative control model can control the whole wireless sensor network to achieve efficient data transmission capacity of intelligent distribution wireless sensor network.