Wireless Sensor Networks Network Research Articles

Wireless Sensor Network Implementation for IoT-Based Environmental Security Monitoring

The use of Wireless Sensor Network (WSN) technology with Internet of Things (IoT) technology has been developed. Internet of Things (IoT) is a technology that enables control, communication and collaboration with various hardware devices over computer networks. The Internet of Things (IoT) has been a form of transformation and development of information technology and Internet networks. This article uses IoT-based environmental monitoring networks to monitor environmental safety in real time. The proposed WSN network is structured to allow continuous monitoring of environmental safety and environmental issues, such as: For example, the potential risk of fire or crime in the environment and many others. In practice, the WSN network consists of sensors and local servers connected via the Internet. The generated data comes from several electronic sensors, such as proximity sensors and fire sensors, all of which are connected to the local server via the gateway. A number of interesting experimental results, including the analysis of energy consumption, are discussed in detail.

Data Transmission Direction Based Routing Algorithm for Improving Network Performance of IoT Systems

With the continuous development of wireless communication technology, the Internet of Things (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts of data with objects, either tangible or intangible, such as sensors or physical devices, connected to the Internet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in various IoT systems, such as monitoring, tracking, and detection systems, to extract relevant information and deliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions. Because sensor nodes have limited resources, such as power and memory, a reduction in the energy efficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance and an increase in packet loss, which affects IoT system performance. Therefore, this study aimed to find an energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery use of sensor nodes to improve the network performance of IoT systems. Conserving energy from sensor nodes and increasing network throughput in WSNs involves having protocols. The low-energy adaptive clustering hierarchy (LEACH ) protocol is a well-known hierarchical routing protocol in WSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmitting data from sensor nodes to the base station (BS) through the cluster head. It is widely adopted in the WSN network field, and many protocols are being studied to improve cluster header selection and data transmission to increase the energy efficiency of sensor nodes. In this study, we attempted to improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when the sensor node is located between the BS and the cluster head, the sensor node transmits data to the cluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in the direction of the BS. Thus, transmission in the opposite direction consumes unnecessary energy and affects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-based LEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiency of transmission energy considering the data transmission direction of sensor nodes. D-LEACH aims to balance the energy of the sensor nodes and improve the performance of WSNs in the IoT system by reducing unnecessary energy consumption caused by reverse transmission considering the data transmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocol of WSNs to improve network performance and describe LEACH. We also explain the D-LEACH protocol proposed in this paper and confirm the performance improvement of WSNs in an IoT system through simulation.

Efficient fault-tolerant routing in IoT wireless sensor networks based on path graph flow modeling with Marchenko–Pastur distribution (EFT-PMD)

In the internet of things (IoT) based wireless sensor network (WSN), the nodes are scattered to segregate the rapt data in the relevant field of application. In general, sensor nodes of IoT possess heterogeneous property and display cluster-based routing to transmit the data as it is considered as an efficient routing method. When one or more cluster heads (CHs) fail, the sensed data of sensor nodes that are currently serving cannot be forwarded by the faulty CHs. Consequently, data of the IoT application will not be sufficiently sensed by the sink node (gateway). As a result, information processing of this field will be affected profusely. This paper focuses on the development of a paired cluster fault-tolerant disjoint path routing in a path graph and a novel approach to solve this dilemma in polynomial time of the degree of the graph. The objective of this proposed IoT–WSN architecture is to diminish the latency, end-to-end delay as well as energy consumption and thereby improving the performance in terms of throughput and packet delivery ratio. The performance of this proposed method in IoT–WSN network is measured and affirmed using benchmark network simulator.

Low-Complexity Security Algorithm Again Black-hole Attacking Node Considering Energy Balance for WSNs

Wireless sensor network (WSN) is a network through radio waves to link a large number of unevenly distributed nodes on a wide range of areas for the purpose of sensing, processing and collecting data.The data transmitted in WSNs is usually sensitive that needs to be protected. WSNs are more vulnerable to attacks due to network characteristics such as wireless transmission, changing network topologies, computing power, limited memory and power of nodes comparing to wired networks. There are many types of attacks on WSNs such as sinkhole attacks, data integrity attacks, wormhole attacks, Black hole attacks, etc. In this article, we will evaluate the impact of Black-hole attacks in the WSN network when using the AODV routing protocol (Ad-hoc On-demand Distance Vector routing). The Black hole attack model will be simulated by Network Simulator 2 (NS-2) by the number of lost packets by counting the number of packets sent by the sending node, receiving packets to destination, and rest energy of the nodes in two cases.

A Survey on WSN and MCN Convergence Networks

In this paper, we present a survey concerned with research focusing on the convergence of wireless sensor networks (WSN) and mobile cellular networks (MCN). The convergence of WSNs and MCNs may be a trigger stimulating new research dealing with such issues as architecture, protocols and air interfaces. The highlights and constraints of the phenomenon are discussed in this paper as well. The survey deals with convergence networks and with their smarty city applications. A few open research issues are also brought to the attention of researchers specializing in this field

A many-objective optimization WSN energy balance model

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

Complexity Analysis of Data Aggregation and Routing Algorithms for Automated Utility Management Using WSN

At present most of the houses in the country have the traditional electromechanical or digital utility usage meters, water meter, and gas meters. Presently most of the utility meter reading, billing system and utility management is not automated. The recent advances in the wireless sensor networks (WSNs) have made strong impact on the development of low cost remote monitoring systems. The WSN based remote automated utility management, remote meter reading and billing for future smart cities will enhance the quality and service by government. This increases the revenue of government. Due to unpleasant trend in the growth of congestion in urban areas, the smart utility meter data traffic aggregation and routing faces more challenges in the traditional automated service departments. In this work we propose an integrated architecture that include electricity, water and gas utility meters and discuss the methodology for Data Aggregation and Routing for Integrated Public Utility Services (IPUS-DAR) using WSN network. This work aims to integrate three types of utility meters and minimize redundant routing data in the network by applying data aggregation that improves traffic performance. We discuss the computational complexity of proposed Data Routing with Data Aggregation algorithm. The comparative analysis of proposed Data Routing with Data Aggregation methodology with previous methods is analyzed. We investigate performance metrics which include packet delivery ratio, end-to-end delay, jitter, throughput and energy consumption with respect to varying network size using QUALNET.

Honey Bee Optimization based Sink Mobility Aware Heterogeneous Protocol for Wireless Sensor Network

Wireless Sensor Network (WSN) has emerged as one of the most important technologies serving an array of solutions for critical applications such as defense, industrial monitoring and decision purposes. Data routing in WSN is effective or non-effective depending upon the energy saving for nodes while transferring data packets to the sink. Mainly WSN divided into two modes; heterogeneous and homogeneous. Heterogeneous network in WSN mainly focused on the cluster head selection. Sink mobility in the heterogeneous network has still many open research issues, it is observed that it makes the network more energy efficient. The optimization in the network leads to the stability of the network at a much higher level. In this paper, the sink mobility is optimized for WSN using Honey Bee Optimization (HBO) technique by considering the parameters such as energy and distance. The proposed protocol shows significant improvement in the stability period by 33 % by covering 2928 rounds and enhanced network lifetime by 1500 rounds in compared with 2033 and 14084 rounds for iMBEENISH protocol respectively.

An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things

The Internet of Things (IoT) depicts the network that contains the objects or the “things” that have been embedded along with the network connectivity, the sensors, electronics or the software that enables the objects to collect and exchange data. Wireless sensor networks (WSNs) connect different sensors/things to the Internet by means of a gateway which interfaces the concept of the WSN to the Internet. They have a certain trait that collects all sensed data and duly forwards it to a gateway using a one-way protocol. Huge amount of either unstructured or semi-structured data collected by the WSN is transmitted to IoT for processing. To improve the efficacy of the storing and processing of data, it is required to classify the data. Genetic algorithm is used to find optimal solutions in IoT. Stochastic diffusion search is a heuristic algorithm which has a robust mathematical model and is distributed. This work proposed a Stochastic AdaBoost algorithm for efficient classification of data obtained from WSN and IoT network.

Method of limiting the emissivity of WSN networks

Wireless sensor networks (WSNs) are a current topic of research that find usage in many applications from environmental monitoring and health protection to military applications. In this paper, analysis of the possibility of reducing the emissivity of radio sensor networks with the assumed transmission probability is discussed. This has a direct influence on power consumption by the nodes and network lifetime. A method based on introducing retransmissions on individual links creating paths between the nodes is presented. Two approaches are used in analyses, the first one being deterministic methods and the second one simulation. Both methods are used to determine the emissivity of the network. The obtained results are compared with a method that uses retransmission on entire paths. This shows that emissivity is more than five times less than the values obtained by retransmission on entire paths.

Secure Routing through Refining Reliability for WSN against DoS Attacks using AODSD2V2 Algorithm for AMI

Secure and reliable routing expands the performance of wireless communication infrastructure of the Advanced Metering Infrastructure (AMI).This paper tries to deliver reliable routing using combination of AODV(Reactive type protocol) and DSDV(proactive type protocol) protocol considering WSN. Different kinds of Attack annoys the enactment of communication infrastructure of AMI. This paper defends communication infrastructure from DoS (Denial of service) attack. The main aim of this paper try to provide reliable routing with security. Communication infrastructure is a key element of AMI. Providing reliability and security for communication infrastructure we can improve the performance of AMI. Due to this electricity sector can save millions of dollars and we provide social awareness about importance of electricity security or Smart Grid. This paper calculates the security in terms of delay, energy consumption, throughput, PDR (Packet Delivery Ratio) and overhead. By considering these parameters we will calculate Confidentiality, Integrity, Availability and Accountability (non- repudiation). Wireless Sensor Network (WSN) considered for wireless communication infrastructure for the AMI. Sensor nodes are battered for attack. Intended for AODSD2V2 (Ad Hoc on Demand Destination Sequenced Distance Vector Routing Protocol) protects the data packets from malicious nodes and DoS attack. For the WSN network infrastructure two kinds of topologies are considered 1. Random deployment strategy 2. Grid deployment. Network Simulator2 (NS2) delivers comparatively simulation results intended for the calculation of reliability and security.

Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization

As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes “external dispersion method” to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes “free-particle swarm optimization” to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage.

Maximization of wireless sensor network lifetime using solar energy harvesting for smart agriculture monitoring

The wireless sensor networks (WSNs) are used for the real-life implementation of the Internet of Things (IoT) in smart agriculture, smart buildings, smart cities, and online industrial monitoring applications. Generally, traditional WSN nodes are powered by limited energy capacity, non-rechargeable batteries. The WSN lifetime (days) depends upon, duty cycle, type of application deployment, and battery state of charge (SoC) level. We propose an innovative solution to the limited energy availability design problem by utilizing the ambient solar energy harvesting for battery charging of WSN nodes. However, there are many challenges in solar energy harvesting like intermittency of available power, solar energy prediction, thermal issues, solar panel conversion efficiency, and other environmental issues. The objective of this research work is to maximize the WSN network lifetime using solar energy harvesting technique. From our simulation results, it is proved that the sensor network lifetime is increased from 5.75 days to 115.75 days @ 25% duty cycle and higher, ideally up to infinite network lifetime. Furthermore, the network throughput is also increased from 100 K bits/s to 160 K bits/s. in SEH-WSNs.

Queue models for wireless sensor networks based on random early detection

Wireless sensor networks (WSNs) are widely used in many fields of industry, agriculture, national defense, and technology. Their performance has a profound impact on their application. The storage and forwarding of WSN network packets is a typical queuing system. Based on queuing theory, this paper studies the performance analysis models of the WSN node, constructs a WSN node queuing model based on M/M/1, a WSN node queuing model with priority, a WSN node M/M/1 queuing model based on random early detection (RED) and a WSN node M/M/1 queuing model with priority based on RED. The models are analyzed theoretically and the expressions of the average delay and queue length are mathematically derived. The experimental results are consistent with the theoretical analysis. Differentiating the packets into different priorities can improve the quality of service of the network. Introducing the RED congestion control and packet discarding policy can effectively solve the congestion problem.

IEEMARP- a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks

In the past few years, research and development in Wireless Sensor networks (WSNs) have gained momentum due to its numerous applications in agriculture, industrial manufacturing, military surveillance, environmental monitoring, consumer electronics, medical & healthcare, disaster recovery operations etc. Dynamic WSNs offer a robust blend of distributed sensing, computing and communication. Dynamic sensor networks are characterized by large scale deployment, dynamic and unstructured topology, power limitations, less memory and limited computational capabilities. Sensor nodes deployed in real-time environment’s for sensing data have power-limitations which hampers the overall performance of WSNs. So, the only obvious solution is to propose an energy efficient routing protocol to optimize WSN real-time performance. Different specialists have proposed various directing conventions for WSNs dependent on Fuzzy Logic, Genetic Algorithms, Meta-Heuristics, and other improvement strategies. However, every solution suggested till date has its advantages and limitations. In this paper, our primary objective is to utilize Swarm-Intelligence based approach i.e. “Ant Colony Optimization (ACO)”, for routing protocol development. Ant colony optimization (ACO) based approach gives optimal solution in terms of efficient routing path determination, energy efficiency and delivering high performance in terms of packet delivery and throughput. In this paper, we propose a novel energy efficient ACO based multipath routing protocol for WSN i.e. IEEMARP (Improvised Energy Efficient Multipath ACO based Routing Protocol). The proposed protocol works in three phases (Neighbor Discovery via Link Knowledge, Packet Transmission via exponentially weighted moving average method and ACKR packet delivery for assuring end-to-end delivery. To validate the performance of the protocol proposed, extensive simulations were conducted using NS-2.35-allinone simulator on diverse parameters like (PDR), throughput, routing overhead, energy consumption and end-to-end delay. In addition to this, the performance of protocol is compared with traditional routing protocols like Basic ACO, DSDV and DSR and other ACO based WSN protocols like ACEAMR, AntChain, EMCBR, IACR, AntHQSeN, FACOR and ANTALG. Simulation based results, clearly states that as compared to Basic ACO, DSDV and DSR, the performance of WSN network is improvised to around 10% in all performance metrics via IEEMARP routing protocol. And as compared to ACEAMR, AntChain, EMCBR and IACR, IEEMARP performs 20% better in overall functionality and almost 10–12% better as compared to AntHQSeN, FACOR, ANTLAG routing protocols in varied WSN scenarios. It is also observed that IEEMARP protocol is highly efficient in TCP packet transmission from source to destination node.

AN IMPROVED ROUTING PROTOCOL SCHEME IN ADHOC NETWORKS

Nowadays, with the rapid development of science and technology and the ever increasing demand in every field, wireless sensor networks are emerging as a necessary scientific achievement to meet the demand of human in modern society. The wireless sensor network (WSN) is designed to help us not lose too much energy, workforce, avoid danger and they bring high efficiency to work. Various routing protocols are being used to increase the energy efficiency of the network, with two distinct types of protocols, homogenous and heterogeneous. In these two protocols, the SEP (Stable Election Protocol) is one of the most effective heterogeneous protocols which increase the stability of the network. In this paper, we propose an approaching the eFCM algorithm in clustering the SEP protocol which makes the WSN network more energy efficient. The simulation results showed that the SEP-eFCM proposed protocol performed better than the conventional SEP protocol.

Energy Efficient Cluster Head Selection using Hybrid Squirrel Harmony Search Algorithm in WSN

The Wireless Sensor Network (WSN) has found an extensive variety of applications, which include battlefield surveillance, monitoring of environment and traffic, modern agriculture due to their effectiveness in communication. Clustering is one of the significant mechanisms for enhancing the lifespan of the network in WSN. This clustering scheme is exploited to improve the sensor network’s lifespan by decreasing the network’s energy consumption and increasing the stability of the network. The existing cluster head selection algorithm suffers from the inconsistent tradeoffs between exploration – exploitation and global search constraints. Therefore, in this research, the hybridization of two popular optimization algorithms, namely, Harmony Search Algorithm (HSA) and Squirrel Search Algorithm (SSA) is executed for optimal selection of cluster heads in WSN with respect to distance and energy. The proposed Hybrid Squirrel Harmony Search Algorithm (HSHSA) is found to be energy efficient when compared with first node death (FND) and last node death (LND) of existing Cluster Head Selection (CHS) techniques. In addition to this, the proposed HSHSA shows enhancements in overall throughput and residual energy of the wireless sensor network by 31.02% and 85.69%, respectively than the existing algorithms.

Monitoring and warning system of a route based on roadside sensors in VANET

Abstract The implementation of vehicle communication systems requires the development of a communication protocol between vehicles. For the design and development of active safety systems, it is intended to develop autonomous systems to collect and analyze information related to the external environment of the vehicle (based on radar systems, video cameras, image processing, etc.); and design and implementation of systems that exchange information between traffic vehicles. Intelligent solutions across a wide range of wireless networks, from large cellular networks to home networks, are being deployed in the field of transportation. A problem with information management and transmission is observed in e WSNs (wireless sensor networks), which usually contain hundreds or even thousands of terminal nodes, which must transmit collected data or commands to a data repository. Creating a means of communication between vehicles and traffic control devices is essential, thus forming an ad hoc network of variable sizes. The so-formed network provides vehicle drivers with information on road conditions, traffic congestion, accidents, warnings, and can help optimize traffic. The paper addresses the issue of inter-vehicle communication by presenting a minimalistic approach, facilitating intervehicular communication through a WSN network, considering extra-urban traffic areas with a high degree of complexity and changing environment (altitude changes, dangerous curves, difficult road conditions).

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

On QoS evaluation for ZigBee incorporated Wireless Sensor Network (IEEE 802.15.4) using mobile sensor nodes

The design of an efficient and scalable Wireless Sensor Network (WSN) to accommodate the fluctuations in topology, node-mobility, node-density, and network-size is an exigent task. A meticulous investigation is necessitated to implement the sensor nodes in an appropriate topology at optimum node-mobility in ZigBee incorporated WSN networks to offer optimum Quality of Services (QoS). Subsequently, an attempt to compute the comprehensive recital of a non-beacon mode based 802.15.4/ZigBee integrated WSN network is demonstrated. An analytical model is designed using parameters such as back-off number, retransmission limit, and back-off exponent considering the impact of node-mobility which has not reported earlier. Further, the investigation is carried out experimentally by evaluating the different QoS metrics, for instance, throughput, network load, bit error rate (BER), received power, signal to noise ratio (SNR) and end-to-end delay for diverse node-density and network-size of a mobile Wireless Sensor Network. Based on the measurements obtained, the authors recommend implementing the mobile sensor nodes in a cluster-tree fashion to afford the best possible QoS services.