Sensor Network Topology Research Articles

IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN

Wireless sensor networks (WSNs) have proven effective in military applications of surveillance and reconnaissance. Sensors capable of detecting pressure, temperature, movement and presence of specific chemicals are deployed in such applications. Traditionally, sensor data is collected and transferred to a centralized high-capacity node or control station. Analysis of data is carried out at such centralized facilities. Information or intelligence gathered from sensor data after analysis is used to generate control and management commands that are relayed back to sensor nodes. The situation is analogous to an actual wartime scenario where soldiers who are on the field are equivalent to the sensors. Soldiers observe and sense the situation and communicate their observations to the decision maker who is stationed in the control tent. On gathering field information, the decision maker analyses the data and arrives at his decision which is again communicated to the soldiers on the field. Soldiers as well as sensors are not placed illogically or randomly but intentionally and strategically. Observations made on the field ultimately affect how the soldiers or sensors continue to function. Intelligence gained on the field ultimately gets used on the field itself. Our attempt is to observe, analyze and apply intelligence on the field itself. This work proposes an intelligent algorithm that is aware of the sensor network topology, analyses sensor data within the network and uses the network framework to arrive at usable intelligence. Locally generated intelligence avoids communication to and from the command/control and adds value to military surveillance and reconnaissance applications of WSN. Intelligent sensor management allows us to use just the necessary number of sensors while saving resources on otherwise redundant expenditure. In the present work we have designed and applied a dynamic boundary computation algorithm to determine the boundary of the area under attack. We have compared the results of simulation experiments incorporating the proposed algorithm against a control experiment without the algorithm.

Mechanical Vibration Test Based on the Wireless Vibration Monitoring System

In order to apply wireless sensor networks to mechanical vibration monitoring, the author proposes a wireless network topology with multiple data collection points for mechanical vibration monitoring. This structure reduces the transmission load of the data collection point, increases the data transmission rate of the network, balances the energy dissipation in the network, and utilizes the general wireless sensor network hardware platform. The network transmission protocol and related auxiliary mechanisms are designed and implemented, and a wireless vibration monitoring test platform is constructed. The transmission performance of the network structure with multiple data collection points is evaluated through the actual test. The experimental results show that by using the wireless sensor network topology with multiple data collection points, it can meet the requirements of continuous transmission of vibration data obtained by 1 kHz sampling. Conclusion. The system performance of the wireless sensor network based on this network structure has been improved under the condition of general hardware, and the network structure of multiple data collection points shows good performance in the process of high-speed data transmission.

Minimally Invasive Online Water Monitor

Sensor installation on water infrastructure is challenging due to requirements for service interruption, specialized personnel, regulations and reliability as well as the resultant high costs. Here, a minimally invasive installation method is introduced based on hot-tapping and immersion of a sensor probe. A modular architecture is developed that enables the use of interchangeable multisensor probes, nonspecialist installation and servicing, low-power operation and configurable sensing and connectivity. A prototype implementation with a temperature, pressure, conductivity and flow multisensor probe is presented and tested on an evaluation rig. This article demonstrates simple installation, reliable and accurate sensing capability as well as remote data acquisition. The demonstrated minimally invasive multisensor probes provide an opportunity for the deployment of water quality sensors that typically require immersion, such as pH and spectroscopic composition analysis. This design allows dynamic deployment on existing water infrastructure with expandable sensing capability and minimal interruption, which can be key to addressing important sensing parameters, such as optimal sensor network density and topology.

Malicious anchor node extraction using geodesic search for survivable underwater wireless sensor network

Localization in underwater wireless sensor network (UWSN) faces an imminent threat when the triangulating anchor node starts to malfunction. Traditional geometric approaches are insufficient to cope with the survivability of UWSN topology. To address these issues, this paper presents a symplectic geometry for identification of the malicious anchor node. Consequently, a geodesic search algorithm (GSA) based Target localization is proposed which reduces the positioning error by exploiting the phase-space constancy of the underwater acoustic sensor network topology to effectively triangulate the target node despite its mobility. First, a malicious anchor node model is presented. The node movement is expressed in the form of “ripple region”. GSA is then proposed which effectively frees the node metastasis from anchor node geometry, thereby making the underwater system more survivable and resilient. Simulation results evaluate the survivability of the geodesic formalism in terms of the reduced penalty incurred by node movement, as well as the reduced impact of anchor node malfunction. An improvement of 13.46% and 9.26% reveals the utility of the geodesic technique in aquamarine sensor deployments, which would be beneficial in underwater resource exploration and defense planning.

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.

Multi-stage resource-aware congestion control algorithm in edge computing environment

Due to the uneven distribution of network resources and network, traffic, congestion is an inherent attribute of the Internet network. Network congestion will increase data transmission delay, reduce network throughput, increase the packet loss rate in the network, and even cause overall network performance degradation, which may cause large losses to users. This article aims to study multistage resource-aware congestion control algorithms based on edge computing environments. This article first compares and analyzes edge computing and traditional cloud computing. There are certain differences between edge computing technology and traditional cloud computing models. In the context of the Internet of Everything, the traditional cloud computing model also has some drawbacks. The low latency of edge computing can provide a good application experience and enhance the responsiveness of user applications. At the same time, the communication load of each network department is also reduced correspondingly, which solves the network congestion. Then, a congestion control algorithm based on cooperative paths was proposed, and the wireless sensor network topology and the limit of the congestion threshold of the wireless sensor network were introduced. Then it analyzes the problem of the MPTCP-based congestion control algorithm and analyzes the simulation results based on the energy-efficiency congestion control algorithm. The experimental results show that, compared with MPTCP, the throughput of the EECCA algorithm is increased by 52.6%, and it has better throughput performance. At the same time, the energy efficiency of the three algorithms in the simulation time is 1.1813 Mbits/J, 0.8311 Mbits/J, and 0.8021 Mbits/J, respectively. Compared with MPTCP, the energy efficiency of EECCA is increased by 47.3%, achieving the goal of energy saving.

A distributed energy-efficient opportunistic routing accompanied by timeslot allocation in wireless sensor networks

Sensed data can be forwarded only in one direction to the base station in one-dimensional queue wireless sensor networks different from mesh structure, so the network lifetime will be shortened if some continuous neighboring nodes have run out of their energy. So designing routing protocols for balancing energy consumption is a challenging problem. However, traditional and existing opportunistic routing protocols for one-dimensional queue wireless sensor network proposed so far have not yet addressed this problem to prolong the network lifetime by introducing sleep mode. In this article, we propose a distributed energy-efficient opportunistic routing algorithm accompanied by timeslot allocation by using specific network topology of one-dimensional queue wireless sensor network. In our new algorithm, clustering and routing tree construction is performed while introducing the optimal relay transmission distance achieved by using opportunistic routing principle, and at the same time, interference-free wake up time is scheduled, which may optimize energy consumption and decrease the number of various control messages as possible to prolong the network lifetime. Furthermore, this improves energy efficiency by introducing the operation mode giving up cluster head role. Simulation results show that the proposed protocol can significantly improve the network performance such as energy consumption and network connectivity, when compared with other existing protocols.

Link Expiration Time and Minimum Distance Spanning Trees based Distributed Data Gathering Algorithms for Wireless Mobile Sensor Networks

The high-level contributions of this paper are the design and development of two distributed spanning tree-based data gathering algorithms for wireless mobile sensor networks and their exhaustive simulation study to investigate a complex stability vs. node-network lifetime tradeoff that has been hitherto not explored in the literature. The topology of the mobile sensor networks changes dynamically with time due to random movement of the sensor nodes. Our first data gathering algorithm is stability-oriented and it is based on the idea of finding a maximum spanning tree on a network graph whose edge weights are predicted link expiration times (LET). Referred to as the LET-DG tree, the data gathering tree has been observed to be more stable in the presence of node mobility. However, stability-based data gathering coupled with more leaf nodes has been observed to result in unfair use of certain nodes (the intermediate nodes spend more energy compared to leaf nodes), triggering pre-mature node failures eventually leading to network failure (disconnection of the network of live nodes). As an alternative, we propose an algorithm to determine a minimum-distance spanning tree (MST) based data gathering tree that is more energy-efficient and prolongs the node and network lifetimes, at the cost frequent tree reconfigurations.

Event-Triggered Distributed Estimation with Decaying Communication Rate

We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate. Specifically, increasing the decay speed of the threshold would make the communication rate decay faster, but reduce the convergence rate of the estimation error. Numerical simulations are provided to illustrate the developed results.

Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function

Wireless sensor networks (WSN) enable the acquisition of multisource environmental data and crop states in precision agriculture. However, the complex agricultural environment causes the WSN topology to change frequently and link connection probability is difficult to predict. In order to improve the utilization of network resources and balance the network energy consumption, this paper studies an agricultural fault-tolerant topology construction method based on the potential game and cut vertex detection. Considering the connectivity redundancy, node lifetime, and residual energy, a fault-tolerant topology algorithm for agricultural WSN based on a double price function is designed. The network is clustered according to the node location and residual energy to form a single-hop effective cluster. Based on the network cluster, the price function is constructed in order to reduce energy consumption and balance network energy efficiency. The initial transmit power set supporting inter-cluster communication is obtained by potential game theory. While preserving the game characteristics of topology, the redundant links are eliminated and the transmit power is adjusted by a cut vertex detection algorithm to realize the construction of a 2-connected cluster head network. Simulation results show that the network topology constructed by the studied algorithm can balance the energy consumption and prolong the network lifetime effectively.

A Data Aggregation Approach Exploiting Spatial and Temporal Correlation among Sensor Data in Wireless Sensor Networks

Wireless sensor networks (WSNs) have various applications which include zone surveillance, environmental monitoring, event tracking where the operation mode is long term. WSNs are characterized by low-powered and battery-operated sensor devices with a finite source of energy. Due to the dense deployment of these devices practically it is impossible to replace the batteries. The finite source of energy should be utilized in a meaningful way to maximize the overall network lifetime. In the space domain, there is a high correlation among sensor surveillance constituting the large volume of the sensor network topology. Each consecutive observation constitutes the temporal correlation depending on the physical phenomenon nature of the sensor nodes. These spatio-temporal correlations can be efficiently utilized in order to enhance the maximum savings in energy uses. In this paper, we have proposed a Spatial and Temporal Correlation-based Data Redundancy Reduction (STCDRR) protocol which eliminates redundancy at the source level and aggregator level. The estimated performance score of proposed algorithms is approximately 7.2 when the score of existing algorithms such as the KAB (K-means algorithm based on the ANOVA model and Bartlett test) and ED (Euclidian distance) are 5.2, 0.5, respectively. It reflects that the STCDRR protocol can achieve a higher data compression rate, lower false-negative rate, lower false-positive rate. These results are valid for numeric data collected from a real data set. This experiment does not consider non-numeric values.

A Proactive Greedy Routing Protocol Precludes Sink-Hole Formation in Wireless Sensor Networks

A tree topology is a commonly employed topology for wireless sensor networks (WSN) to connect sensors to one or more remote gateways. In many-to-one traffic, routing imposes a heavy burden on downstream nodes, as the same routes are repeatedly used for packet forwarding from one or more sensor chains. The challenge is traffic paths that ensure balanced energy consumption at sink-hole to protect sensors from fast death. This paper proposes an energy consumption pattern-aware greedy routing protocol that proactively protects many-to-one topology from the sink-hole formation. The proposed protocol, Energy Balance-Based Energy Hole Alleviation in Tree Topology (EBEHA-T), precludes energy hole formation rather than retrospectively responding to a hole detection. Updated status of variations in energy consumption patterns at the sink-hole and construction feature of joint nodes in the tree topology aids in routing decision. Performance evaluation of EBEHA-T against benchmark method RaSMaLai shows increased energy-saving across the entire network and a marked improvement in energy consumption balance in energy hole zones. This precludes energy hole formation and the consequent network partitioning, leading to improved network lifetime beyond that of the RasMaLai.

Group acknowledgement mechanism for beacon-enabled wireless sensor networks

Wireless sensor networks are network of a large number of sensor nodes used for remote applications. To the expressive growth in remote sensor networks, medium access control (MAC) works faster in wireless sensor networks. The MAC protocol has to be appropriately designed to ensure the proper operation of the sensor nodes in WSNs. This paper provides energy saving by proposing group acknowledgement MAC (GACK-MAC) for an aggregated data frame transmitted to a single destination, and reducing the overhead in IEEE 802.15.4 beacon enabled wireless sensor networks. In our proposed scheme, type of acknowledgement of each sensor node is decided by the coordinator based on traffic load of the network or number of packets present in the sensor node. We performed this by adopting the star and peer-to-peer tree topologies with a coordinator having coordination with all other member nodes. Simulations are conducted, and compared with the analytical models constructed to measure the throughput, and delay metrics. Moreover, our method enhances the battery lifetime by 35% on average, throughput of 42% at maximum, and decrease the delay by 33% for each packet in network of star topology and twice the above mentioned values for battery lifetime, throughput and reduces the delay by 49% in P2P topology of wireless sensor networks.

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

Reliability Analysis of Social Network Data Transmission in Wireless Sensor Network Topology

In this paper, the reliability of data transmission in social networks is thoroughly studied and analyzed using wireless sensor network topology technology. This paper, based on the introduction of sensor network reliability analysis-related technology, combined with the characteristics, and needs of the sensor network itself, focuses on the study of the reliability analysis of the sensor network under the state of perturbation scheme. Based on the idea of making full use of data changes to respond to the sensor state, this paper takes the actual monitoring data of the wireless sensor network as the research object, selects the temporal correlation and spatial correlation of the measured environmental data as the reliability index by extracting the features of the wireless sensor network data, and proposes the Evidential reasoning rule- (ER-) based wireless sensor network data reliability assessment model based on Evidential reasoning rule (ER) is proposed. The data are mined, analyzed, and quantified from the perspective of content popularity, and the interest indicators of nodes on data under content popularity are analyzed to derive stable interest quantification values. Combined with the network properties, i.e., node autoassembly community, we analyze the data dissemination characteristics of social networks in wireless sensor network topology environment and derive the upper and lower bounds of data transmission capacity under node interest-driven and its variation on network performance. Social relationships among nodes affected by social attributes are considered; in turn, the data forwarding behavior of nodes is modeled using data transmission probability and data reception probability; finally, the data forwarding process is analyzed and a closed expression for the average end-to-end transmission capacity is derived in turn.

Методологія проектування автоматичної системи структурного моніторингу технічного стану будівель та споруд

The development of smart systems in various architectural structures leads to the fact that if earlier information about the structural condition was not always important, now when talking about the safety of the population, it is a very high-priority information and direction of research. In general, systems for monitoring the condition of structures allow identification, localization and assessment of damage. Such information about the object allows making appropriate decisions in advance to eliminate possible emergency situations. To solve the task of developing a methodology for the design of an automatic structural monitoring system for various architectural structures, a list of stages that allow designing the necessary system was proposed. So, the research is generally divided into three main stages. At the first stage, topologies of sensor networks are considered. Questions regarding the choice of topology for a wireless sensor network are discussed, and the main advantages and disadvantages of each topology are presented. This will allow to choose the right topology for the system. At the second stage, the levels of damage identification that must be performed, which approaches are based on the used algorithms for damage detection are considered. The most popular algorithms used for damage identification are also listed, their disadvantages and advantages were displayed. The third stage is devoted to the selection of the element base of the sensor node, the comparative characteristics of digital and analog accelerometers are highlighted. Also in this section, popular digital accelerometers are presented and an analysis of the characteristics of how and what is affected by each of them is performed. It was described what characteristics should be taken into account when choosing a microcontroller. When designing, it is necessary to take into account the location of sensor nodes and how this affects the distance of data transmission.

Reinforcement Learning TDMA-Based MAC Scheduling in the Industrial Internet of Things: A Survey

As technology progresses, almost all Industrial Internet of Things applications are becoming more data-driven. Hence, more advanced ways of communication are required to both save energy and become efficient to collect huge amounts of data. Researchers have now agreed that the root improvement of IIoT communication can start from a lower layer which is Data Link. Time Division Medium Access-based protocols on this layer have drawn academia's attention to decrease the energy consumption of IIoT devices and achieve higher levels of throughput. Yet, a problem arises, that is, developing a scheduler has always been troublesome while designing a Medium Access Control protocol. Scheduling communication in TDMA-based MAC protocols is considered an NP-hard problem. It is one of the topics highly debated among researchers to make the scheduling adaptive to the huge variation of IIoT application requirements. Conventional scheduling algorithms may suffer from the rapid changes in sensor network topology, network throughput, and applications’ delay targets. Therefore, Reinforcement Learning algorithms are being integrated into scheduling techniques because of their adaptability and efficiency. This paper surveys RL-based TDMA MAC protocols and compares them in terms of several unified features. Each protocol is explained and discussed with others regarding its advantages and drawbacks.

Implementation of NLOS based FPGA for distance estimation of elderly using indoor wireless sensor networks

Location or distance estimate data for the elderly is a major drawback in wireless sensor networks (WSNs). Previous research used an Artificial Neural Network (ANN) in the indoor spaces to calculate the distance between elderly people and a unique anchor node in the ZigBee WSN. The neural network was evaluated on a “Field Programmable Gate Array (FPGA)” because it was being used in a real-world application. To training, testing, and validating the ANN, the RSSI values of anchor nodes were extracted. As a result, the MAE-estimated distance error improved. In addition, one of the issues that might affect distance estimate accuracy is the mobility of persons inside the testing phase. As a result, the proposed work aims to concentrate on NLOS circumstances to examine their impact on estimated distance. NLOS situations are most frequent indoors when several rooms within a structure are involved. Difficulties can cause Non-Line-Of-Sight (NLOS) propagation when WSNs are implemented in indoor environments. However, the proposed method overcomes the issue of selecting the nodes by combining an optimization method including PSO with an FPGA to find the smallest number of nodes with a suitable “Distance Estimation Error”. To execute the indoor wireless sensor network (WSN) topology, the system includes ZigBee technology. As a result, the FPGA implementation's complexity can be decreased and it outperforms the other existing algorithms. As can be observed, the analysis of the proposed hybridizing method based on WSN is highly effective in enhancing the elderly's well-being.

An Invulnerability Algorithm for Wireless Sensor Network's Topology Based on Distance and Energy

An Invulnerability Algorithm for Wireless Sensor Network's Topology Based on Distance and Energy

Artificial Intelligence Based Sensor Network Congestion Fuzzy Control Algorithm

The topology control of sensor sensor network was studied based on fuzzy control algorithm. Aiming at the dynamic changes of the topology of large-scale and heterogeneous artificial intelligence sensor networks and the incomplete information between nodes, a smart network-based congestion control algorithm for sensor networks was proposed and the performance of fuzzy control algorithms was analyzed. Based on this, a fuzzy control algorithm was designed. The algorithm fully considered the residual energy of nodes and the distribution of nodes in the network. Therefore, the reasonable election of the cluster head can be realized through the game between nodes, which effectively avoided energy holes, made the network energy consumption more uniform, prolonged the network life cycle, and optimized the network topology.