Self-organizing Wireless Sensor Networks Research Articles

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Related Biological Factors of Mobile Sensor Network Technology on Sprint Performance

Node localization and temporal synchronization, as two key parts of each self-organized and localization-aware wireless sensor network (WSN), have been a key topic for research and applications. The initial prototype of the sensor network is the same as that of the local area network. All nodes are connected by wires, and there is a central control node. All nodes transmit data to the central node point-to-point. With the development and progress of wireless communication technology, the current sensor network has developed into a WSN. Without a central node, all nodes can communicate with each other; so, it is natural to develop positioning technology in WSN. Node positioning in wireless sensor networks refers to the process in which sensor nodes determine the location information of other nodes in the network through a certain positioning technology based on the location information of a few known nodes in the network. The principle of positioning is purely geometric in mathematics. With the in-depth promotion of WSN in the application field, there are more and more requirements for high precision positioning, the higher the positioning accuracy, the higher the requirements for network time synchronization, and the problem of node clock synchronization and high precision positioning of the node can be studied together. Solving the problem of node clock synchronization can further provide support for node positioning in a variety of different environments. As a new ultrabroadband (UWB) carrier-free communication technology with nanoscale temporal resolution, it has been widely used in high-precision node positioning systems in recent years, UWB technology is the most advanced noncarrier wireless communication technology that uses bandwidths above 1 GHz and uses nonsine wave narrow pulses from nanoseconds to picoseconds to transmit data. Therefore, it occupies a very wide spectrum. UWB technology has the advantages of low system complexity, low transmit signal power spectrum density, insensitive to channel fading, and high positioning accuracy. It is especially suitable for high-speed wireless access in dense multipath places such as indoors, providing a technical basis for the engineering implementation of high-precision positioning algorithms. However, the current situation of Chinese track and field events has not kept pace with the development of Chinese competitive sports, and even the level of individual events has a gradual decline. Therefore, it is very meaningful to study the relevant biological factors that affect sprint performance. This article analyzes the related biological factors that affect the performance of the sprint, combines the knowledge of physiology to analyze the training methods that appear in the sprint from the physiological perspective, and analyzes the related biological factors that affect the performance of the sprint. This article chooses to divide them into men’s group (3 groups) and women’s group (3 groups), with 4 people in each group. Experiment proved that after the experiment, these are the following factors: the fatigue of the nervous system, the technical difference of sprinting, the change of muscle fiber enzyme activity, the order of muscle fiber cross-sectional area and muscle activation, and the recruitment of muscle fiber types. Impact P value less than 0.05, which shows that the factors that affect sprint performance are complex, and the biological factors that affect step length can be studied through anthropometry. The impact of step frequency on sports performance is very important.

Practice of Air Environment Quality Monitoring Data Visualization Technology Based on Adaptive Wireless Sensor Networks

This paper begins by outlining the evolution and current state of traditional wireless sensor networks, followed by an examination of the principle and architecture of wireless sensor networks, as well as the challenges that traditional wireless sensor networks face in environmental monitoring. This paper designs and develops a data visualization system for wireless sensor networks based on the visualization system and the advantages of other visualization systems, after analyzing and comparing the current visualization systems for monitoring wireless sensor networks. A real-time monitoring system for urban air quality based on wireless sensor network is designed to solve the problem that the traditional method of combining manual sampling with laboratory analysis for air quality monitoring is expensive and cannot carry out real-time online monitoring for multiple points at the same time. The system’s implementation is based primarily on air quality monitoring data collected in the Shenyang area for visual display and analysis. The air quality monitoring system proposed in this paper has a complete structure and independent modules and is simple to use. The dynamic adaptability of the monitoring system is greatly improved by utilizing the self-adaptability and self-organization of wireless sensor networks, which brings great convenience to air quality monitoring.

Security Analysis for a VANET Privacy Protection Scheme

Vehicular ad hoc network (VANET) is a self-organizing wireless sensor network model, which is extensively used in the existing traffic. Due to the openness of wireless channel and the sensitivity of traffic information, data transmission process in VANET is vulnerable to leakage and attack. Authentication of vehicle identity while protecting vehicle privacy information is an advantageous way to improve the security of VANET. We propose a scheme based on fair blind signature and secret sharing algorithm. In this paper, we prove that the scheme is feasible through security analysis.

Investigation of energy efficient protocols based on stable clustering for enhancing lifetime in heterogeneous WSNs

There are certain challenges faced with wireless sensor networks (WSNs) performances, consumption can be seen amongst all these challenges as a serious area of research. Data from sensor nodes are transmitted by most WSN energy either among many nodes or to the base station (BS), and due this connection, several routing protocols were developed for supporting in data transmission in the WSNs. Extending network lifetime in an operational environment is the major objective of the wireless sensor network. Charging or exchanging sensor node batteries is almost impossible. Energy balancing and energy efficiency are significant research scopes as per designing of routing protocols aimed at self-organized WSNs. A heterogeneous WSN is one where every node has different amount of energy linked to it before it is deployed in a network. Therefore, different energy efficient routing protocols have been proposed which enables lesser consumption of energy, longer stability period which leads to the network lifetime increasing. In this study, the average energy of a WSN is computed after every logical round of operation for our protocol-HPEEA and compare it with two well-known heterogeneous protocols namely-SEP and CCS. At the end of the considered number of logical operations, MATLAB with simulation results confirm that HPEEA protocol have a reduction in the energy consumption compared to other protocols.

Modelling attacks in self-organizing wireless sensor networks of smart cities

Nowadays self-organizing wireless sensor networks for solving the problems of arranging operational processes in smart city systems are becoming rapidly developed and widespread. Such networks are used to collect and aggregate data from physical sensors of devices as well as for its processing and transmission over the network in conditions of time-varying loading characteristics of the communication channels, changing location of devices and their operating modes. The paper discloses an approach to modeling and analysis of attacks aimed at compromising devices, data and services of smart city systems built on the basis of self-organizing wireless sensor networks.

Resource Allocation Based on Chaotic Ant Colony Algorithm in Self-organizing Wireless Sensor Network

A resource allocation scheme based on chaotic ant colony algorithm in self-organizing wireless sensor network is proposed, and corresponding chaotic sequence generator is designed in this paper. Simulations are conducted to compare the proposed method with conventional genetic algorithm and dynamic programming algorithm under self-organizing wireless sensor network environment. Results show that compared with the scheme based on dynamic allocation algorithm and conventional genetic algorithm, the scheme based on chaotic ant colony algorithm has higher target detection rate and lower power consumption, which also extends the networks lifetime.

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Using autonomously operating mobile sensor nodes to form adaptive wireless sensor networks has great potential for monitoring applications in the real world. Especially in, e.g., disaster response scenarios—that is, when the environment is potentially unsafe and unknown—drones can offer fast access and provide crucial intelligence to rescue forces due the fact that they—unlike humans—are expendable and can operate in 3D space, often allowing them to ignore rubble and blocked passages. Among the practical issues faced are the optimizing of device–device communication, the deployment process and the limited power supply for the devices and the hardware they carry. To address these challenges a host of literature is available, proposing, e.g., the use of nature-inspired approaches. In this field, our own work (bio-inspired self-organizing network, BISON, which uses Voronoi tessellations) achieved promising results. In our previous approach the wireless sensors network (WSN) nodes were using knowledge about their coverage areas center of gravity, something which a drone would not automatically know. To address this, we augment BISON with a genetic algorithm (GA), which has the benefit of further improving network deployment time and overall coverage. Our evaluations show, unsurprisingly, an increase in energy cost. Two variations of our proposed GA-BISON deployment strategies are presented and compared, along with the impact of the GA. Counter-intuitively, performance and robustness increase in the presence of noise.

Hierarchical Protocol Based on Recursive Clusters for Smart Parking Applications Using Internet of Things (IOT)

With the increasing number of vehicles, the management of parking spaces in cities is becoming increasingly important in improving the quality of life and combating air pollution. Indeed, finding a parking space at peak times and in congested areas of the population becomes a huge challenge for drivers. To remedy this problem, most modern cities have smart parking. The equipment of these smart parking is mainly based on the implementation of wireless sensor networks (WSN) to monitor, track, and collect real-time information on the occupancy status of each parking space. This information is then made available to drivers who are looking for an available parking space. However, sensor nodes have limitations in terms of energy and communication that affect the performance and quality of the wireless sensor network. Therefore, the design of a self-organization protocol for WSN that minimizes power consumption and maximizes the longevity of the WSN network must be taken into account when implementing and developing a sustainable and viable intelligent parking system. In this paper, we propose a protocol for self-organization of wireless sensor networks (WSN) for the management of parking spaces in outdoor and urban car parks. This protocol is based on building clusters using ZigBee transmission technology for multihop communication. Each sensor node will be installed in the ground of each parking space to monitor its availability by sending the empty or busy state of that space to the gateway using cluster head nodes (CHs). This approach has a robust and efficient self-organizing algorithm that minimizes energy dissipation and increases the lifetime of sensor nodes and the WSN network. The simulation results show that parking management systems in outdoor and urban car parks using the self-organization protocol presented are efficient and sustainable in terms of energy consumption, reliability of data transmission, and the longevity of the WSN network compared to other existing parking systems that use different self-organizing protocols for wireless sensor networks.

An Immune Chaotic Niche Genetic Algorithm for Monitoring Distribution Problem in Self-Organizing Wireless Sensor Network

Due to the sensors are restricted in sensing capabilities, monitoring distribution has become a challenging problem in self-organizing wireless sensor network (SOWSN). Micro nodes are self-organized tiny devices with limited sensing capabilities. Careful monitoring distribution scheme can be a suitable optimizing means for achieving the required high target detection rate goals, comprising sensing capacity restrictions. However, the monitoring distribution problem is a typical NP-hard combinatorial stochastic optimization problem. In this study, an immune chaotic niche genetic algorithm (ICNGA) to enhance the target detection rate is explored. Advanced operators such as immune operator and chaotic operator are also incorporated into the ICNGA to increase the explore capacity. The represented immune selection and chaotic generation depending on ICNGA with global search capability can simultaneously optimize multiple variables. Simulation results identify that the proposed algorithm can get a higher target detection rate over SFLA and SA. Besides that, the convergence speed of the presented ICNGA is much higher than that of SFLA and SA. So the proposed method significantly enhanced the efficiency.

SEEN: A Selective Encryption Method to Ensure Confidentiality for Big Sensing Data Streams

Resource constrained sensing devices are being used widely to build and deploy self-organizing wireless sensor networks for a variety of critical applications such as smart cities, smart health, precision agriculture and industrial control systems. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. A Data Stream Manager (DSM) at the server collects the data streams (often called big data) to perform real time analysis and decision-making for these critical applications. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to assure the trustworthiness of the collected data so that any decisions are made on the processing of correct data. Assuring high data trustworthiness requires that the system satisfies two key security properties: confidentiality and integrity. To ensure the confidentiality of collected data, we need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity of emerging applications or the sensed data types or the sensing devices. For example, a temperature in a precision agriculture application may not be as sensitive as monitored data in smart health. Providing multilevel data confidentiality along with data integrity for big sensing data streams in the context of near real time analytics is a challenging problem. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality and data integrity. Our method is based on two key concepts: common shared keys that are initialized and updated by DSM without requiring retransmission, and a seamless key refreshment process without interrupting the data stream encryption/decryption. Theoretical analyses and experimental results of our SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams.

An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network.

Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.

Data Collection Through Mobile Vehicles in Edge Network of Smart City

With increasing technological facilities, billions of sensor devices are deployed in the smart city, which allows to sense and collect the status and information of all kinds of infrastructures in the city. Those large number of sensing nodes form many self-organized wireless sensor networks (WSNs). Although the single WSN has been widely studied by researchers. However, few studies have focused on how to effectively connect to Internet of Things (IoT) and collect data in many decentralized WSNs of smart city with a low cost. In this paper, a cluster head rotation joint mobile vehicle data collection (CHR) scheme is proposed to effectively collect data from many decentralized WSNs in smart city with a low cost. In CHR scheme, each WSN selects one or multiple nodes which can connect to mobile vehicles as the cluster heads. Then all the in-network sensor nodes send their data to cluster head through multi-hop communication, due to the cluster head can connect to mobile vehicles, so when mobile vehicles pass by cluster head, the data of cluster head can be sent to mobile vehicles and brought to the data center to process. We first propose a single cluster head rotation joint mobile vehicle data collection (SCHR) scheme to collect data by only using a single CH in a WSN in which the CH rotation and clustering algorithms are carefully designed to balance in-network energy consumption. Then multiple cluster head rotation joint mobile vehicle data collection (MCHR) scheme is proposed to further balance the energy consumption and prolong the network lifetime in which multiple cluster heads are selected to be jointly responsible for the data collection task. The extensive experiments show that the CHR scheme has good performance in the network energy, network lifetime and network transmit capacity.

Special issue on machine learning applications for self-organized wireless sensor networks

Special issue on machine learning applications for self-organized wireless sensor networks

A Self-organizing Wireless Sensor Networks Based on Quantum Ant Colony Evolutionary Algorithm

<p><span style="font-size: medium;"><span style="font-family: 宋体;">Aiming at the coverage problem of self-organizing wireless sensor networks, a target coverage method for wireless sensor networks based on Quantum Ant Colony Evolutionary Algorithm (QACEA) is put forward. This method introduces quantum state vector into the coding of ant colony algorithm, and realizes the dynamic adjustment of ant colony through quantum rotation port. The simulation results show that the quantum ant colony evolutionary algorithm proposed in this paper can effectively improve the target coverage of wireless sensor networks, and has obvious advantages compared with the other two methods in detecting the number of targets and the convergence speed. Based on the above findings, it is concluded that the algorithm proposed plays an essential role in the improvement of target coverage and it can be widely used in the similar fields, which has great and significant practical value.</span></span></p>

ESTIMATING THE MAXIMUM SYNCHRONIZATION DRIFT IN SELF ORGANIZING WIRELESS SENSOR NETWORKS

The nodes in self-organizing wireless sensor networks (WSNs) do not have means for synchronizing their clock with a global reference. To maintain a local synchronization, the nodes to periodically propagate a small control packet called SYNC. The propagation delay suffered by the packet caused the synchronization drift in the network, which is proportional to the length of the propagation path. This paper aims to investigate ways to calculating the maximum synchronization drift that could happen in a network. We propose a method to theoretically calculate the upper bound of the maximum schedule drift in a network, which serve as the worst case scenario. We then run simulations to see the effect of network density and node’s transmission range to the maximum synchronization in the network for various network sizes. Finally, we propose a way to estimate the maximum synchronization drift in a dense network.

Towards a commodity solution for the internet of things

Embedded-class processors found in commodity palmtop computers continue to become increasingly capable. Moreover, wireless connectivity in these systems provides new opportunities in designing flexible and smarter wireless sensor networks (WSNs). In this paper, we present Lynx, a self-organizing wireless sensor network framework. Leveraging palmtop systems as sensor hubs, Lynx provides fundamental functionality to make a distributively managed, customizable WSN system implementation. Second, we describe Ocelot, a mobile distributed grid-like computing engine for commodity palmtop platforms. The combination of Lynx and Ocelot provides sensor nodes that are capable of collecting, recording, processing, and communicating data without any central server support. Significant energy savings can be achieved for light to medium weight tasks through the Lynx and Ocelot combined system compared to traditional server-class grid-solutions such as BOINC. We demonstrate Lynx and Ocelot in the context of life-cycle building energy usage.

A universal digital platform for the construction of self-organizing wireless sensor networks for industrial safety and ecological monitoring systems

The design results of a universal digital platform intended for the construction of self-organizing wireless sensor networks of industrial safety and ecological monitoring systems are given. The design-manufacturing principles for the creation of miniature wireless sensor assemblies (WSAs), ensuring the integration of gas sensors of different types, their self-contained power supply, self-deployment of the sensor network, and design features of the miniature WSAs based on a specialized receiving–transmitting module, constructed in accordance with the up-to-date 2.5D assembly technique using a silicon substrate, are considered. The basic principle of the operation of highly sensitive WSA elements, which are intended for the determination of toxic and explosive gases, and the basic development principles of a smart power source integrated into the sensor assembly housing, which ensures its uninterrupted operation for a long time through the electric energy generation and accumulation units from alternative energy supply sources, are presented. Problems related to the development and remote automatic renovation of the WSA software, architecture of the server software for data acquisition, and analysis of industrial and ecological monitoring problems are considered.

Asymmetric Clustering Algorithm with Gravitational Model (ACAGM)

In wireless sensor networks, the formation of a clustered network topology plays a crucial role in maximizing the network lifetime and effectively improving the network performance. Deploying the scalable wireless sensor networks requires multi-hop operations; in other words, intermediate nodes act as gateways. Most existing works on self-organization in wireless sensor networks do not support the scalability so that a large number of clusters could be generated. Furthermore, producing a smaller number of clusters can tend toward a flat architecture. Thus, the main challenge of these protocols is to find the balance between the size and number of clusters generated in order to have greater energy efficiency. In this paper, we propose a new approach of clustering inspired from the formation of the celestial bodies through universal gravitational model. Thus, each CH candidate node will have a G gravitational parameter expressing the capacity to « attract » sensors around its « orbit ». This parameter is obtained from energy discretization between adjacent clusters. Thanks to gravity, the size and the number of clusters will be neatly and dynamically determined to ensure maximum network lifetime.

Energy Efficient Coverage Optimization in Wireless Sensor Networks based on Genetic Algorithm

There has been a great deal of attention paid to Genetic Algorithm (GA). The algorithm, as a methodology, is a multi objective methodology which can be used in different fields such as self-organizing wireless sensor network. The technique examines the applied parameters and at the same time takes into consideration the fitness function by the way of or considering the whole operational modes in produced feasible states. Majority of the GA implementations in clustering algorithm only deal with optimization of few parameters including coverage and energy consumption with noticeable effect on network quality. Keeping network coverage can be modeled as mathematical programming problem which is featured with heavy load of computation. On the other hand, wireless sensor networks (WSNs) can be of dynamic nature, if so it needs to have proper reaction to events; so that slightest management decision may lead to considerable problems on the quality of the network. This problem is dealt with in this study through a hybrid method in MATLAB with the help of Genetic Algorithm toolbox and custom codes. The optimum solution was obtained by mathematical algorithm that conforms to all the mentioned parameters.