Intelligent Wireless Sensor Network Research Articles

URBAN NETWORK OF AIR QUALITY MEASUREMENT NODES

Smart city is a city that increases the interactivity of its components and put emphasis on their functionality. Internet of Things technology (IoT) is an innovative solution in environmental protection. Usually, information on air quality is very scattered. This paper describes the test stages of pre-implementation works, focusing on the presentation of the technical design of the measurement nodes and the assumptions of the IT project. The goal of the project Intelligent Wireless Sensor Network Infrastructure (IIBSC) is, among others, to create a dense network of air quality measurement nodes at city, district or even street level. The concept is based on Internet of Things (IoT) technology using a matrix construction tool connected to multiple identical measurement nodes located in the test area. The project developed a hardware platform supporting sensors and resistant to external factors, and an ISIMPIO information platform based on edge processing technology for processing data from air quality sensors. Due to the use of Internet of Things (IoT) technology, an edge server using edge processing was designed. Edge server provides a complete ecosystem for building edge applications that are fully optimized for seamless field work. In addition, it allows the implementation of integrated Python software, the MQ Telemetry Transport support protocol (MQTT), time-series database, firmware update over a wireless network, and built-in security system. Measuring the concentration of particulate matter and other substances in the air will be useful for specialists assessing their dynamics. The technology and test installation selected corresponds to the leading solutions in this field in Europe and, in the future, should also be extended to less urbanised areas.

The network of telecommunications cross‐layer cooperation control of wireless sensor communication network applied in smart distribution grid

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.

A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks.

With the continuous progress of science and technology, intelligent wireless sensor network (IWSN) communication has become indispensable in its role in production and life because of its convenient network settings and flexible use. However, with the widespread availability of intelligent wireless sensor networks, the use of many wireless sensor nodes constitutes a multi-node wireless communication system, which turns the accuracy and low complexity of multi-node detection in sensor networks into a problem. Although the traditional algorithm has excellent performance, it cannot give consideration to both accuracy and complexity. Therefore, a maximum logarithm message passing algorithm based on serial and threshold (S-T-Max-log-MPA) for multi-mode detection in IWSN is proposed in this paper. In this algorithm, the threshold is used to determine the necessary conditions of sensor node stability first, and then the sensor node information updating is integrated into the resource node information updating, so that the system can maintain good accuracy, performance, and change the situation of poor system accuracy at low threshold. Compared with the traditional algorithm, the proposed algorithm significantly changes the algorithm complexity reduction rate of the system multi-node detection. Simulation results show that the algorithm has a good balance between accuracy and complexity reduction rate.

The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network

The intelligent wireless sensor network is a distributed network system with high “network awareness”. Each intelligent node (agent) is connected by the topology within the neighborhood which not only can perceive the surrounding environment, but can adjusts its own behavior according to its local perception information to constructs a distributed learning algorithms. Therefore, three basic intelligent network topologies of centralized, non-cooperative, and cooperative are intensively investigated in this paper. The main contributions of the paper include two aspects. First, based on algebraic graph, three basic theoretical frameworks for distributed learning and distributed parameter estimation of cooperative strategy are surveyed: increment strategy, consensus strategy, and diffusion strategy. Second, based on classical adaptive learning algorithm and online updating law, the implementation process of distributed estimation algorithm and the latest research progress of above three distributed strategies are investigated.

Deployment of a Wireless Sensor Network in MobilePhone Environment

Wireless mobile sensor network is the collection of autonomous and distributed sensor with additional capacity of mobility. Mobility of sensor nodes adds additional functionality to the wireless sensor network of self-deployment and relocation of sensors. Sensors find their own position and placed themselves over the target area after initial sensor distribution. Different approaches have been proposed for the deployment of mobile sensor by considering different issues. Coverage and Connectivity is the main issue of deployment. Some additional issues have to be considered while deploying mobile sensors like sensor relocation, energy efficient movements of sensors, obstacle adaptability, lifetime of network, fault tolerance etc. This paper basically presents the study of different mobile sensor network deployment approaches with their features and drawbacks with respect to a mobile phone environment. The issues of mobile sensor network deployment are investigated in detail. It further discusses the algorithm and the way of deployment along with the self-deployment. Development in the technology of sensor such as Micro Electro Mechanical Systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications have contributed a large transformation in Wireless Sensor Network (WSN) recently. It assists and improves work performance both in the field of industry and our daily life. Wireless Sensor Network has been widely used in many areas especially for surveillance and monitoring in agriculture and habitat monitoring. Environment monitoring has become an important field of control and protection, providing real-time system and control communication with the physical world. An intelligent and smart Wireless Sensor Network system can gather and process a large amount of data from the beginning of the monitoring and manage air quality, the conditions of traffic, to weather situations. In this paper, we discuss and review wireless sensor network applications for mobile phone environment. In order to implement a good system, there are several requirements to be followed. From the research, it is also proven that this approach can improve the phone performance, provide a convenient and efficient method and can also fulfill functional requirements.

An Efficient and Safe Road Condition Monitoring Authentication Scheme Based on Fog Computing

In recent years, with the development of intelligent vehicles and wireless sensor network technology, the research on road safety has attracted much attention in vehicular ad-hoc networks (VANETs). By sensing events on the road, vehicles can broadcast information to inform others of traffic jams or accidents. However, the mobile vehicle network has a large transmission delay, which makes real-time content transmission impossible. In this paper, a new certificateless aggregate signcryption scheme (CLASC) is proposed by using a fog computing framework that supports mobility, low latency, and location awareness. It is combined with online/offline encryption (OOE) technology, which reduces many time-consuming operations and improves the security of vehicle users and the efficiency of message authentication. In addition, the scheme has the characteristics of mutual authentication, anonymity, untraceability, and nondeniability. Based on the difficulty of the discrete logarithm problem (DLP) and the computational Diffie-Hellman (CDH) problem, the scheme is further proved to be unforgeability and confidentiality under the random oracle model. The simulation results show that compared with the existing schemes, this scheme can not only ensure the security requirements of the system but also achieve higher efficiency in computing and communication.

Intelligent Wireless Sensor Networks for Early Fire Warning System

DOI : 10.26650/electrica.2019.19019 Forest and wide area fires are one of the biggest obstacles to the protection of green areas, which are the life guarantee for future generations. These fires, which constitute a major threat to the ecosystem, can lead to human and animal deaths. In this work, an early warning system was proposed to detect forest fires with wireless sensor networks. The wireless sensor network consists of spatially distributed sensors to analyze the situations of the environment for various scenarios. Near real-time data from various sensors are transmitted to the server within mesh network structure via redundant paths using low cost and low power Zigbee. The communication in the system is continued thanks to this mesh design without any loss of data when there is a connection break between the sensor nodes and the beacons. Multi-layer feed forward neural networks are utilized for decision-making stage by adopting to the conditions of the environment rhythms. By designing a graphical user interface that works on a local machine, the approximate location of the modules, status color codes and status visualization with Google Maps on web server and notification of the relevant authorities are provided. Cite this article as : Onal AF, Ulver B, Durusoy A, Erkmen B. Intelligent Wireless Sensor Networks for Early Fire Warning System. Electrica, vol. 19, DOI: 10.26650/electrica.2019.19019.

RUbIn: A Framework for Reliable and Ubiquitous Inference in WSNs

Development of IoT applications brings a new movement to the functionality of wireless sensor networks (WSNs) from only environment sensing and data gathering to collaborative inferring and ubiquitous intelligence. In intelligent WSNs, nodes collaborate to exchange the information needed to achieve the required inference or smartness. Efficiency or correctness of many smart applications relies on the efficient, timely, reliable, and ubiquitous inference of information. In this paper, we introduce the RUbIn framework which provides a generic solution for such ubiquitous inferences. RUbIn brings the reliability and ubiquity for inferences using the redundancy characteristic of the gossiping protocols. With RUbIn, the implementation of such inferences and the control of their speed and cost is abstracted by providing developers with a proposed middleware and some dissemination control services.We develop a prototype implementation of the RUbIn framework and a few inference examples on TinyOS. For evaluation, we utilize both the TOSSIM simulator and a testbed of MicaZ motes in various densities and different number of nodes. Results of the evaluations demonstrate that in all nodes, the inferring time after a change is about a few seconds and the cost of maintenance in stability state is about a few sends per hour.

Intelligent Communication in Wireless Sensor Networks

Wireless sensor networks (WSN) are designed to collect information by means of a large number of energy-limited battery sensor nodes. Therefore, it is important to minimize the energy consumed by each sensor, in order to extend the network life. The goal of this work is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent is sent to each sensor in order to process the information and to cooperate with neighboring sensors while mobile agents (MA) can be used to reduce information shared between source nodes (SN) and send them to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the multi-agent system (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the sensors of the network have been grouped into sectors. For each MA, we have established an optimal itinerary, consuming a minimum amount of energy with data aggregation efficiency in a minimum time. Successive simulations in large-scale wireless sensor networks through the SINALGO (published under a BSD license) simulator show the performance of the proposed method, in terms of energy consumption and package delivery rate.

Intelligent Wireless Sensor Network Deployment for Smart Communities

Smart Communities demand a huge amount of data to monitor different human activities. Until now, wireless sensor networks (WSNs) have provided these data with a low cost and low intelligence networks. However, the constraints and the required baseline performance of WSNs have increased exponentially, and there is a lack of a global proposal to satisfy the demand of Smart Communities. There are several approaches regarding efficient WSN deployments to maximize network lifetime and performance. The vast majority of those proposals try to separately address several inherent constraints and limitations related to energy and node lifetime. In fact, they typically provide efficient management approaches based on existing solutions, but they do not propose new tools or algorithms to afford them. To tackle this problem in a global manner, we introduce the concept of intelligent WSNs. In order to outfit the WSN with a huge amount of possibilities to route and control the different nodes, our nodes incorporate the possibility of transmitting and receiving signals from/to several nodes jointly through beamforming. In our approach, this latter technique is intelligently applied to allow the deployment of new WSNs to be able to react and to adapt its features depending on the environmental conditions, maintaining QoS and enlarging network lifetime. This is the main goal of our proposal.

Analysis and simulation of reliability of wireless sensor network based on node optimization deployment model

The reliability of wireless sensor network is affected by the credibility of nodes. With the addition of random nodes and increase of coverage, the reliability decreases. In order to improve the reliability of wireless sensor network, a reliability analysis method of intelligent computing-oriented wireless sensor network is proposed. In this method, a node optimization deployment model of wireless sensor network is established; The adaptive rotation scheduling method is adopted for optimal design of networking routing of wireless sensor network; the sensor quantization fusion tracking method is adopted for quantification of the credibility of sensor gird points; then the reliability of wireless sensor network is measured based on the analysis results with quantization fusion to improve the reliability of wireless sensor network, so as to achieve optimal network design. The simulation results show that with the proposed method in construction of wireless sensor network, the reliability of network is good. With the increase of bit error probability and packet length, the energy cost of network is gradually smaller and smaller, and the minimum energy cost can approach to 0.01 kJ, which indicates that the anti-attack ability of the proposed method is strong. When the number of data forwarding is 700, the success reception rate can reach 100%. Therefore, the proposed method is of good application value in wireless sensor network networking.

Choosing Cluster Head in Artificially Intelligent Wireless Sensor Network

Choosing Cluster Head in Artificially Intelligent Wireless Sensor Network

An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Control and Assessment

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.

Architecture and key technologies for the mission planning system of space sensor network

Considering emerging technologies of the intelligent space and wireless sensor network, a business architecture, system architecture and technology architecture are proposed for the mission planning system of a space sensor network. The business architecture of a mission planning system is constructed from application flows, such as those of task modelling, planning and scheduling. The system architecture is designed from the software platform, resource configuration and node structure. The technology architecture is proposed according to applied key technologies. Key technologies are also proposed for the mission planning system of a space sensor network. Through the strengthening of top-level planning and the innovation of the conception design, the exploration of the architecture and key technologies of the mission planning system of a space sensor network will promote technological innovation and provide comprehensive benefits.

Architecture and key technologies for the mission planning system of space sensor network

Considering emerging technologies of the intelligent space and wireless sensor network, a business architecture, system architecture and technology architecture are proposed for the mission planning system of a space sensor network. The business architecture of a mission planning system is constructed from application flows, such as those of task modelling, planning and scheduling. The system architecture is designed from the software platform, resource configuration and node structure. The technology architecture is proposed according to applied key technologies. Key technologies are also proposed for the mission planning system of a space sensor network. Through the strengthening of top-level planning and the innovation of the conception design, the exploration of the architecture and key technologies of the mission planning system of a space sensor network will promote technological innovation and provide comprehensive benefits.

Road lighting energy-saving system based on wireless sensor network

An intelligent wireless sensor network that is designed to save road lighting energy, while maintaining traffic safety, is presented. It consists of an array of nodes that may be installed on both sides of the road, being mutually separated by a few tens to a few hundreds of meters. Each node consists of (i) vehicle detection subsystem, (ii) wireless communications and control subsystem for message exchange between the neighboring nodes, and (iii) lighting subsystem. During the day, nodes are inactive, while during the night, they turn to an active mode enabling detection of passing vehicles and timely activation of road lighting. Upon detection of a passing vehicle, the node generates a wireless message. Neighboring nodes that receive the wireless message will turn on their road lighting. In order to prevent driver distraction, each lamp will be turned on at a safe distance ahead of the moving vehicle. In order to improve energy efficiency, the lights will be dimmed or turned off behind the passing vehicle. The proposed road lighting energy-saving (RLES) system, which combines advanced sensor and wireless communication technologies, is suitable for implementation both with existing high-intensity discharge (HID) lamps and new light-emitting diode (LED) lamps. Our analysis shows that the RLES system will pay off in less than a year when used with HID lamps, while pay-back period is less than two and a half years when used with LEDs.

Adaptive and intelligent wireless sensor networks through neural networks: an illustration for infrastructure adaptation through Hopfield network

This paper proposes embedding an artificial neural network into a wireless sensor network in fully parallel and distributed computation mode. The goal is to equip the wireless sensor network with computational intelligence and adaptation capability for enhanced autonomous operation. The applicability and utility of the proposed concept is demonstrated through a case study whereby a Hopfield neural network configured as a static optimizer for the weakly-connected dominating set problem is embedded into a wireless sensor network to enable it to adapt its network infrastructure to potential changes on-the-fly and following deployment in the field. Minimum weakly-connected dominating set defined for the graph model of the wireless sensor network topology is employed to represent the network infrastructure and can be recomputed each time the sensor network topology changes. A simulation study using the TOSSIM emulator for TinyOS-Mica sensor network platform was performed for mote counts of up to 1000. Time complexity, message complexity and solution quality measures were assessed and evaluated for the case study. Simulation results indicated that the wireless sensor network embedded with Hopfield neural network as a static optimizer performed competitively with other local or distributed algorithms for the weakly connected dominating set problem to establish its feasibility.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

Management of Intelligent Campus Wireless Sensor Networks Based on Runtime Model

To quick customize and develop intelligent campus internet of things (ICIOT) system more efficiently, in this paper an approach based on runtime model to managing intelligent campus wireless sensor networks is proposed. Firstly, manageability of intelligent campus wireless sensors is abstracted as runtime models which automatically and immediately propagate any observable runtime changes of target resources to corresponding architecture models. Then, a composite model of intelligent campus wireless sensors is constructed through merging their runtime models in order to manage different kinds of devices in a unified way. Finally, a customized model is constructed according to the personalized management requirement and the synchronization between the customized model and the composite model is ensured through model transformation. Thus, all the management tasks can be carried through executing operating programs on the customized model. In the part of the teaching area schools conducted experiments and compared with the traditional method, this method can be more effective management of campus facilities, more energy efficient and orderly, which can reach a 16.7% energy saving.