Performance Of Wireless Sensor Networks Research Articles

A Wormhole Attack Detection Algorithm Integrated With the Node Trust Optimization Model in WSNs

The openness of Wireless Sensor Networks (WSNs) layout makes them vulnerable to attacks. One of the most threatening attacks is the wormhole attack. The wormhole attack is difficult to be detected since it causes false routing by private tunnels and damages to WSNs in data transmission. Therefore, in order to resist wormhole attacks and improve network performance, this paper proposes a detection algorithm integrated with the node trust optimization model (NTOM-DA) against wormholes in WSNs. First, add the node whose number of neighbors exceeds the threshold to the list of suspicious nodes, and then the exclusive neighbors of suspicious nodes communicate with each other. Mark the path whose hops exceed the wormhole threshold as the path to be tested. Establish a trust model to calculate the node trust and evaluate the path trust. The simulation results show that NTOM-DA is superior in detecting wormhole attacks. Even in the face of a network with high node density and high attack degree, it still has a high detection rate and a low false-positive rate, which effectively guarantees the safe and reliable operation of WSNs.

Plane sweep algorithms for data collection for energy harvesting wireless sensor networks using mobile sink

Usage of mobile sink(s) for data gathering in wireless sensor networks(WSNs) improves the performance of WSNs in many respects such as power consumption, lifetime, etc. In some applications, the mobile sink $MS$ travels along a predefined path to collect data from the nearby sensors, which are referred as sub-sinks. Due to the slow speed of the $MS$, the data delivery latency is high. However, optimizing the {\em data gathering schedule}, i.e. optimizing the transmission schedule of the sub-sinks to the $MS$ and the movement speed of the $MS$ can reduce data gathering latency. We formulate two novel optimization problems for data gathering in minimum time. The first problem determines an optimal data gathering schedule of the $MS$ by controlling the data transmission schedule and the speed of the $MS$, where the data availabilities of the sub-sinks are given. The second problem generalizes the first, where the data availabilities of the sub-sinks are unknown. Plane sweep algorithms are proposed for finding optimal data gathering schedule and data availabilities of the sub-sinks. The performances of the proposed algorithms are evaluated through simulations. The simulation results reveal that the optimal distribution of data among the sub-sinks together with optimal data gathering schedule improves the data gathering time.

Collision-Free Dynamic Convergecast in Low-Duty-Cycle Wireless Sensor Networks

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an <u>i</u>ncast-<u>co</u>llision-f<u>re</u>e convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25&#x0025; &#x007E; 57&#x0025; and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

Development of an organic photovoltaic energy harvesting system for wireless sensor networks; application to autonomous building information management systems and optimisation of OPV module sizes for future applications

The emergence of internet of things (IoT) has motivated research into developing Organic Photovoltaic (OPV) devices that can efficiently convert indoor light into electricity. In this work, the performance and operation of an OPV-powered Wireless Sensor network (WSN) for Building Information management system is provided through a case study. Results are shown for the operation of the WSN and how data can be acquired to build machine learning algorithms that can forecast the indoor conditions of a building, when the system is linked to an external weather station. Remarkably, our data indicates only minor degradation of the OPV when tested under indoor conditions over a 21-month period; at a luminance level of 1000 Lux, only a −10% relative drop in performance was measured. Finally, the field data is used to optimise the size of the OPV and battery for future indoor applications which possess different energy loads. Based on the energy efficiency model, the loss of power supply probability (LPSP) of the indoor applications system is calculated for different size combinations of PV, battery sizes and load energies. This model provides a method to calculate the required OPV output power to ensure remote operation of other IoT electronics. • First report of OPV powered indoor WSN. • Forecasting of indoor conditions possible. • Degradation of indoor operated OPV shown to be minor. • Optimisation of OPV/battery size conducted.

A Comprehensive Study on Security in Wireless Sensor Networks (WSNs).

Wireless Sensor Networks(WSNs) are an important component of today's ubiquitous and pervasive computing. Without WSNs, the applications aren't as clever as they could be. Almost every scenario involving WSNs necessitates a quick and precise localization process. Existing frameworks and algorithms, on the other hand suffer from a significant disadvantage when it comes to beacon node trust, which is a critical component in Wireless Sensor Network. For localization this has to be ensured.. This issue is addressed in our current solution. In the harsh environment of WSN operations, malicious nodes are inescapable. As a consequence, A technique has been proposed to find out the problem while simultaneously offering a safe trust based localization system. It focuses on the algorithm for assessing trust and the creation of blockchains. Every beacon node’s truth value(trust value) are determined using various trust criteria with the corresponding weights being dynamically changed during localization process. After that the most reliable beacon nodes are chosen for mining. This two-step process ensures that the blockchain is kept up to current, and that beacon nodes have consistent Tvalues(Trust values). We conducted a series of simulations to test the suggested algorithm’s performance and effectiveness. The accuracy of localization, harmful activity detection, the confusion matrixes are used to compare results.

Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.

Uneven clustering and fuzzy logic based energy-efficient wireless sensor networks

Clustering is the fundamental issue in terms of ensuring long-term operation of wireless sensor networks (WSNs). The problem of hot spots remains the most prominent research challenge relating to the design of energy-efficient clustering algorithm. This paper proposed a protocol, namely an uneven clustering and fuzzy logic-based energy-efficient (UCFLEE), for prolonging network lifetime. Depending on the communication distance, the UCFLEE protocol divides the network into uneven clusters for suppressing the hot spot problem. The fuzzy logic selects the optimal cluster head in accordance with certain parameters. The advocated method adopts a dynamic energy threshold to chnage the cluster head. The UCFLEE protocol is dependent on the iterative deepening A (IDA) star algorithm for identifying the routing path from the cluster heads to the base station. The IDA-star method is reliant upon a cost bounded method to select the optimal solution for the base station. The UCFLEE protocol is tested and subsequently contrasted with other protocols. The results obtained from the UCFLEE protocol enable an energy consumption equilibrium, eradicates the hot spot challenge, while also attaining maximum network lifetime.

Solution for intra/inter-cluster event-reporting problem in cluster-based protocols for wireless sensor networks

&lt;span&gt;In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.&lt;/span&gt;

Dual-line data collection scheme for efficient mobile sink operation in solar-powered wireless sensor networks

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate.

Hierarchical Clustering Nodes Collaborative Scheduling in Wireless Sensor Network

Network lifetime and coverage are the key evaluation metrics of Wireless Sensor Networks (WSNs). Aiming at network lifetime, Hierarchical Clustering-task Scheduling Policy (HCSP) breaks the traditional clustering framework, but it doesnt fully consider the network coverage problem. We propose a Hierarchical Clustering Node Collaborative Scheduling (HCNCS) protocol that optimizes HCSP to extend the network lifetime and improves the coverage performance of WSNs by nodes collaborative sensing. According to the deployment location, HCNCS classifies nodes into Node Collaborative Sensing Disks(NCSDs), which as units for data sensing and transmission. Nodes are rotated in WSNs to balance the energy consumption and through collaborative sensing to improve the coverage of NCSDs. In addition, HCNCS constrains the number of clusters by NCSDs classification based on K-means to reduce the communication energy consumption of clustering. Based on the HCNCS, this paper further proposes an Enhanced Hierarchical Clustering Node Collaborative Scheduling (EHCNCS) protocol. Compared with HCNCS, EHCNCS selects NCSDs according to Node Collaborative Scheduling (NCS) algorithm after clustering, which improves the coverage capability of NCSDs at the cost of increasing a small amount of the communication energy consumption. EHCNCS also rotates Cluster Heads (CHs) by the CH rotation candidate table to balance network energy consumption. The extensive simulations show that HCNCS has an obvious advantage over HCSP in terms of network lifetime and network coverage. Compared with HCNCS, EHCNCS significantly prolongs the network lifetime of WSNs at the expense of a bit of coverage performance.

Optimal Routing Protocol for Wireless Sensor Network Using Genetic Fuzzy Logic System

The wireless sensor network (WSN) is a growing sector in the network domain. By implementing it many industries developed smart task for different purposes. Sensor nodes interact with each other and this interaction technique are handled by different routing protocol. Extending the life of the network in WSN is a challenging issue because energy in sensor nodes are quickly drained. So the overall performance of WSN are degraded by this limitation. To resolve this unreliable low power link, many researches have provided various routing protocols to make the network as dependable and sustainable as possible. While speeding up the data delivery is also considered to be an effective approach to save energy. To achieve this objective, we propose a new energy efficient routing protocol using genetic fuzzy logic system. Our primary objective is to save energy by sending data packets via the shortest path. Numerous studies have proved that the clustering protocol plays an important role in prolonging the life of the sensor node in the WSN. Keeping up with this our second objective is selection of head node from a cluster. This cluster head is selected based on the availability of maximum residual energy among the nodes, lifetime of head-to-head link, and its minimum distance to the base station. The genetic fitness approach is proposed for optimal routing and the selection of cluster head (CH) is employed with fuzzy logic system. As a result, the genetic fuzzy logic system (GFLS) can effectively accelerate the process to solve this problem. MATLAB is used to deploy nodes in WSN. The performance is calculated in terms of efficiency, delay, packet delivery rate and network throughput. The performance is compared with previous pertinent work. The proposed approach has elevated its performance around 8% in packet delivery and 6% in overall network throughput.

Simulated study of the influence of node density on the performance of wireless sensor networks

This paper investigates the impact of local and global node density in cluster-based structured wireless sensor networks (WSNs). The local density represents sensor node density (SND) in a cluster whereas, global node density relates to head node density (HND) in the entire WSN. The literature rarely addresses the impact of density on WSNs performance as the focus is typically on protocols, routing, scheduling, clustering and network longevity. Often, the density of nodes is assumed heuristically, but not based on empirical experiments. In this work, we address this issue by measuring the impact of node density on four performance metrics: isolated sensor nodes, isolated head nodes, network detection effectiveness and network tracking accuracy. Using an in-house simulator, a total of 5,200 experiments were conducted and performance-metrics were collected and analysed. The results revealed interesting relationships among the studied variables and identified best performing node densities locally and globally.

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

The dynamism and successive changes in the distribution of nodes are among the important characteristics of wireless sensor networks (WSNs). To adapt with those changes, network designers frequently have to configure multiple parameters for each layer on WSN architecture (i.e physical, medium access control, network, and application layer). This tuning has an important impact on the network performances (e.g packet loss, energy efficiency, throughput, network lifetime, etc). However, finding the optimal configuration is the main challenge. Deep learning (DL) based on neural network layers can be used to extract patterns from high-dimensional data provided by sensor nodes. In this paper, we survey the most recent DL approaches which aim to predict WSN performances by finding the pattern on the network parameters (such as transmission power level, MAC protocol type, spectrum availability, congestion points, etc.). Moreover, we classify the studied articles by considering the targeted network layer or cross-layer. This paper can be considered as a starting point for researchers to review the recent DL applications on the optimization of WSNs performances based on multiple network parameters.

Broadcasting capability analysis of WSNs in ship information collection system

In the Internet of things environment, the information collection system designed with wireless sensor networks (WSNs) equipment as the transmission medium can realize the collection of marine environmental information. The WSN nodes can work in a harsh environment without the costly and complicate wiring in the ships. It is necessary to distribute monitoring or alarm information through broadcast operation in ship WSNs. Therefore, in order to improve the efficient application of WSNs in ship information system, the broadcast characteristics of WSNs are analysed and the existing broadcast algorithms are summarized and classified. Finally, some problems faced by broadcast technology are pointed out through simulation results.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Adaptive Zone–Assisted Iterative Localization in Energy-Efficient Wireless Sensor Networks

In the public safety-related applications which use wireless sensor networks (WSNs) to estimate a target’s location, identifying the most effective search zone in which the target is located can reduce computational complexity and shorten the response time. We formulated a threshold-based scheme that only uses binary reports from sensors to identify the most effective search zone. This scheme requires no <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information regarding statistics on the sensor’s decision preference. Based on this scheme, we developed an adaptive zone–assisted iterative localization (AZ-IL) scheme. Compared with existing iterative localization schemes, our scheme can reduce the computational complexity of localization processes and maintain the localization performance in energy-efficient WSNs, in which some sensors are placed in sleep mode.

An Efficient Path Generation Algorithm Using Principle Component Analysis for Mobile Sinks in Wireless Sensor Networks

Recently, the data collection problem in wireless sensor networks (WSNs) using mobile sinks has received much attention. The main challenge in such problems is constructing the path that the mobile sink (MS) will use to collect the data. In this paper, an efficient path generation algorithm for the mobile sink based on principal component analysis (PCA) is proposed. The proposed approach was evaluated using two data collection modes—direct and multihop—and it was compared with another approach called the mobile-sink-based energy-efficient clustering algorithm for wireless sensor networks (MECA). When compared with MECA, simulation results have shown that the proposed approach improves the performance of WSN in terms of the number of live nodes and average remaining energy.

An Integrated Signal Allocation Model with Effective Collision Resolution Model for Performance Enhancement of Wireless Sensor Networks

A Wireless Sensor Network (WSN) differs from conventional wireless or wired networks in that it interacts with the environment. Orthogonal Frequency Division Multiplexing (OFDM) was investigated as a possible interface technology for making effective use of bandwidth. Such networks have been proposed for a variety of purposes such as search and rescue, disaster assistance, and smart positioning systems. These applications often require a large number of wireless sensors that are powered by batteries and are designed for long-term, human-free deployment. Collisions between network nodes can significantly degrade performance in WSNs. Although increased bandwidth facilitates wireless access to high data frequencies, it is prohibitively expensive to increase due to spectrum limits. This necessitates making good use of the available bandwidth. OFDM has been considered as a possible interface mechanism for efficiently utilising bandwidth. While many signals available in WSN technology can be employed to mitigate collisions, multi-signal allocations may have a significant impact on the efficiency of multistage communications. Real-time multimedia flow raises the chance of sensor network failures and congestion, which reduces the efficiency of Quality of Service (QoS). The main goal of the Signal Allocation Scheme is to allocate an appropriate number of signals to any node in order to use professional bandwidth and assure QoS. Load balancing is intended to measure and prevent collisions caused by the number of available slots in the frame. Preparation is another important component in preventing collisions because it decreases delay and optimises energy utilisation. In this paper, an Integrated Signal Allocation Model with Effective Collision Resolution Model (ICAM-ECR) is used to deploy non-overlapping signals dynamically for varying application loads based on expected bandwidth estimation. The suggested model is compared to standard methods, and the findings reveal that the proposed model outperforms existing models.

A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work.

Energy saving for wireless sensor network in manufacturing and administrative projects via Smart Nodes

The cost-effectiveness of the operation of Wireless Sensor Network (WSN) in manufacturing and administrative projects can highly boost the production of lower goods and services that in turn enhance the marketing ability. The lifetime increase of the batteries by reducing energy wastage is the main challenge and hence, in the current paper, there is an attempt to save energy via transmitting packets as quickly as can be expected while avoiding congestion and drop packets. The time synchronization allows nodes to sleep and wake at the same time to avoid consuming energy. The suggested algorithm, would, successfully deliver a packet with a fairly high rate and reduce dropping packets. Besides, it manages to avoid congestion and saves energy when reduce the retransmission the dropped packets.