Algorithm For Wireless Sensor Networks Research Articles

An Improved Image Steganography Scheme Based on Partial Preservation Embedding Algorithm for Wireless Visual Sensor Networks

With the continuous improvement of encryption algorithms, some applications based on the architecture of wireless visual sensor networks have gradually shifted their attention to the imperceptibility and antijamming performance of secret images. To reduce the probability of secret images being detected, the current research focuses on hiding secret data in the least-significant bit of the cover image in the spatial domain or embedding data into the coefficients of the high-frequency band in the transformational domain, which usually leads to poor performance in a hostile environment. Therefore, some researchers proposed to substitute the coefficients of the medium-frequency band in the transformational domain with secret information to enhance the anti-interference performance. However, this idea would severely affect the imperceptibility of secret images. As a result, an improved version based on the partial preservation embedding algorithm was designed in this paper. Theory analysis and simulation results indicate that the proposed scheme performs better than the existing methods by directly substituting the coefficients of the medium-frequency band in the transformational domain, especially in the case of strong noise interference.

Optimized Traffic And Enhanced Lifetime Routing Algorithm In WSN


 
 
 
 Abstract:
 Wireless Sensor Networks (WSNs) have gained significant traction as a viable solution for a diverse range of applications that necessitate the monitoring and regulation of distant environments. Wireless sensor networks (WSNs) are frequently constrained by the energy resources of the individual sensor nodes, resulting in network failures and reduced operational lifetimes. In response to this challenge, we present a proposed optimised traffic and lifetime routing algorithm for Wireless Sensor Networks (WSNs). The algorithm has been specifically designed to minimise network traffic and ensure equitable energy consumption across all sensor nodes. The algorithm utilises a fusion of clustering and network coding methodologies to leverage spatial diversity and redundancy within the network. The performance of the proposed algorithm is assessed through simulation experiments and is juxtaposed with various contemporary routing algorithms. The findings indicate that the algorithm we employed exhibits superior performance compared to other algorithms with respect to network longevity, network flow, and energy usage. The algorithm under consideration exhibits potential for utilisation in diverse Wireless Sensor Network (WSN) applications that necessitate dependable and energy-efficient data transmission.
 
 
 

A Network Traffic Aware Tuning Algorithm in Wireless Sensor Networks

Wireless sensor networks (WSNs) are being widely deployed in many areas such as smart homes and Internet of Things. A main concern in WSNs is energy conservation because battery operated sensor nodes are expected to run for a long period of time without human intervention. The backoff mechanism implemented in the WSNs communication protocol is a key element to ensure energy efficient communication between sensor nodes. Many previous backoff schemes perform well in light or medium loaded networks. However, on a heavily loaded network, performance degrades significantly. Also, most of the proposed schemes in the literature are based on the assumption that the number of nodes is given. In this paper, we propose an adaptive scheme that estimates the number of competing nodes in wireless sensor networks by using the collision probability and adapts the backoff window parameters accordingly, such that higher throughput and energy efficiency are obtained. The numerical results show that the proposed scheme could achieve much higher throughput and energy efficiency than the standard and previous methods for almost all the different competing number of nodes.

Game theory based adaptive transmit power control algorithm for IoT wireless sensor networks

Objectives: To design a transmission power control (TPC) algorithm for the Internet of Things (IoT) based Wireless Sensor Networks (WSN) such that the energy cost is reduced and the expected throughput is increased. Method: In this study, a Game theory based adaptive transmit power control (GT-ATPC) algorithm for IoT-WSN is proposed. In this algorithm, for each sender, the initial transmission power level (TPL) is determined from the difference of Received Signal Strength (RSS) measurements at the base station (BS). The TPL is adaptively adjusted by means of Game theory. For each sender, the expected throughput, energy cost and connectivity values are estimated from which a utility function is derived. Findings: By simulation results, it has been shown that GT-ATPC attains 37% higher throughput with 57% reduced energy cost and 30% lesser overhead when compared with GTS-PM approach. Novelty: The TPL of a node is adjusted based on the estimated values of utility function such that the throughput and connectivity is high and energy cost is less. Keywords: Internet of Things (IoT); wireless sensor network (WSN); transmission power; Energy cost; game theory; utility function

An active model for ranging by deep convolutional neural network and elephant herding optimization algorithm (DCNN-EHOA) in WSNs

PurposeIn modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.Design/methodology/approachThis paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem.FindingsIn this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services.Originality/valueThe proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

Business Classification Based Adaptive Power Control Algorithm for Wireless Sensor Networks in Intelligent Electricity

Business Classification Based Adaptive Power Control Algorithm for Wireless Sensor Networks in Intelligent Electricity

Self-organising map-based dynamic decision-making algorithm for heterogeneous wireless sensor network

ABSTRACT Wireless sensor networks (WSNs) are applicable in most of the domains of engineering. Presently, Heterogeneous WSN (HWSN) is gaining more importance than homogeneous WSN. One of the major challenges of a HWSN is efficient deployment which can provide good coverage. The deployment strategy in WSN can be static or dynamic. It is found that there is a need of dynamic deployment that intelligently places the sensor nodes in the deployment area. This intelligence is possible by one of the artificial neural networks concept like self-organizing map (SOM). This work presents a dynamic decision-making algorithm (DDMA) for deployment of sensors using SOM such that the nodes will adjust their locations to uncovered area by failed sensor node in the changing sensing environment. The DDMA is compared with LEA2C and ECBS which are variants of LEACH. The results show that proposed DDMA performs better than the existing algorithm in-terms of decreased node depletion rate by 35% and 25% more coverage. The experimental analysis also show that SOM-based DDMA performs better than the non-SOM-based DDMA deployment for HWSN in terms of 5% better energy conservation, 30% better network lifetime, 75% more coverage in the deployment area and 90% reduced node depletion rate. This work presents a dynamic decision-making algorithm (DDMA) for deployment of sensors using self-organising map (SOM) such that the nodes will adjust their locations to uncovered area by failed sensor node in the changing sensing environment. The DDMA is compared with variants of LEACH with existing LEA2C and ECBS. The results show that proposed DDMA performs better than the existing algorithm in-terms of decreased node depletion rate by 35% and 25% more coverage. The experimental analysis is performed between SOM-based and non-SOM-based DDMA algorithms. Results show that SOM-based DDMA performs better than the non-SOM-based DDMA deployment for heterogeneous WSN in terms of 5% better energy conservation, 30% better network lifetime, 75% more coverage in the deployment area and 90% reduced node depletion rate.

Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks

Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

Uneven clustering routing protocol based on ant colony algorithm for wireless sensor networks

For the energy of the wireless sensor network node is limited, an uneven clustering routing protocol is proposed for wireless sensor networks. In the protocol, cluster head election uses a competitive manner, and all nodes in the first round participate in the election. The cluster head election considers the remaining energy, the number of neighboring nodes, and the distance between node and sink. The path search from the cluster head to the base station runs the ant colony algorithm to find the optimal multi-hop route. Each round no longer clusters, and re-clustering and routing will update only when the remaining energy of the cluster head is less than the predicted energy. The protocol enables its routing establishment and maintenance more self-adaptive and dynamic. The simulation shows that the routing protocol can efficiently reduce and balance the energy consumption, and prolong the wireless sensor network survival period.

Predator–prey optimization based clustering algorithm for wireless sensor networks

Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents a static clustering algorithm that employs predator–prey optimization (PPO) for identifying cluster heads as well as routes for sending data to the sink. The objective of the optimization algorithm is to reduce the energy consumed in data collection and transmission, to achieve equalization in energy utilization by the wireless sensor nodes and to prolong the wireless sensor network lifetime while avoiding the expenses of cluster reformation in each communication round. The novelty of this algorithm is to treat the identification of cluster heads and the choice of transmission paths a unified optimization problem of minimizing the total energy cost of the network, whereas existing algorithms consider them two separate optimization sub-problems. PPO algorithm is applied to select the most appropriate pair of cluster heads for each cluster. It also identifies the optimum communication path, which can be single or multiple hop. The energy consumed in data transmission is reduced and a uniformity in residual energy of the nodes is achieved. The performance of the novel algorithm has been evaluated by observing the patterns in which nodes consume their energies. The number of packets that are successfully delivered has been found to be better than the existing static clustering algorithms, and at par with the finest dynamic clustering algorithms.

DORA: A Destination-Oriented Routing Algorithm for Energy-Balanced Wireless Sensor Networks

This research presents a new multichain routing strategy named the destination-oriented routing algorithm (DORA). The proposed design generates a new multichain routing scheme to transmit packets for energy-balanced WSNs. Based on the multichain routing, the optimal transmission distance between any two nodes is derived by the mathematical analysis model. With the optimal distance, the design criterion is to select the furthest forwarding node within the communication range and the direction to the sink, thus forming the multichain structures by accurate distance, direction, and multiple paths. With the shorter chain routing, unnecessary energy loss can be reduced to prolong the global network lifespan. The simulation results demonstrate that the proposed DORA doubles the network lifespan, compared with that of conventional PEGASIS and improves 60% lifespan on the RPC protocol.

A novel double sparse structure dictionary learning–based compressive data-gathering algorithm in wireless sensor networks

Purpose As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based on double sparse structure dictionary learning (DSSDL). The purpose of this paper is to reduce the energy consumption of WSNs. Design/methodology/approach The historical data is used to construct a sparse representation base. In the dictionary-learning stage, the sparse representation matrix is decomposed into the product of double sparse matrices. Then, in the update stage of the dictionary, the sparse representation matrix is orthogonalized and unitized. The finally obtained double sparse structure dictionary is applied to the compressive data gathering in WSNs. Findings The dictionary obtained by the proposed algorithm has better sparse representation ability. The experimental results show that, the sparse representation error can be reduced by at least 3.6% compared with other dictionaries. In addition, the better sparse representation ability makes the WSNs achieve less measurement times under the same accuracy of data gathering, which means more energy saving. According to the results of simulation, the proposed algorithm can reduce the energy consumption by at least 2.7% compared with other compressive data-gathering methods under the same data-gathering accuracy. Originality/value In this paper, the double sparse structure dictionary is introduced into the compressive data-gathering algorithm in WSNs. The experimental results indicate that the proposed algorithm has good performance on energy consumption and sparse representation.

Quantum-Inspired Ant-Based Energy Balanced Routing in Wireless Sensor Networks

Background: Limited energy capacity of battery operated Wireless Sensor Networks (WSNs) is the prime impediment in the ubiquity of WSNs as the network lifetime depends on the available energy at the nodes. Prolonging the network lifetime is the principal issue in WSNs and the challenge lies in devising a strategy for judicious use of available energy resources. Routing has been one of the most commonly used strategies for minimizing and balancing the energy consumption of nodes in a WSN. Methods: Routing in large networks has been proved to be NP-Hard and therefore meta heuristic techniques have been applied for handling this problem. Quantum-inspired algorithms are relatively new meta heuristic techniques which have been shown performing better than their traditional counter- parts. Therefore, Quantum inspired ant Based Energy balanced Routing (QBER) algorithm has been proposed in this paper for addressing the problem of energy balanced routing in WSNs. Results: Simulation results confirm that the proposed QBER algorithm performs comparatively better than other quantum inspired routing algorithms for WSNs. Conclusion:

A Systematic Review of Routing Algorithms for Wireless Sensor Networks

A Systematic Review of Routing Algorithms for Wireless Sensor Networks

A Comparative Analysis of Buffer Management Algorithms for Delay Tolerant Wireless Sensor Networks

One of the challenges in Delay Tolerant Wireless Sensor Networks (DT-WSN), is to handle situations where the available buffer space is insufficient- the buffer management problem. Although several buffer management algorithms have been proposed for DT-WSNs, to the best of our knowledge, there is no comprehensive study on the effects different factors have on their performance, and which evaluates the relative performance of these algorithms in different contexts. This paper evaluates in a fixed-factor factorial experiment the performance in terms of latency and Quality of Information (QoI) of four representative buffer management algorithms for DT-WSNs; two traditional, FiFO and Random, and two QoI-based algorithms- one proposed by Humber and Ngai and the SmartGap algorithm. The evaluation suggests that the buffer management algorithm in combination with employed routing protocol and the sensor node buffer sizes have a significant impact on latency, while the obtained QoI rather depends on the characteristics of the transported data and the routing protocol, provided a single-copy routing protocol is used. Moreover, the evaluation suggests that QoI-based buffer management algorithms do offer improved QoI, with an 31% improvement in MAE for SmartGap compared to FIFO. However, they do so at the expense of higher latency, with SmartGap giving a 60% higher latency than FIFO on average.

Three-Dimensional Localization Algorithm Based on Improved A and DV-Hop Algorithms in Wireless Sensor Network.

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

An energy‐efficient non‐uniform clustering routing protocol based on improved shuffled frog leaping algorithm for wireless sensor networks

Wireless sensor networks have been used widely in environmental tracking and monitoring. Nevertheless, WSNs are facing a number of challenges, such as unreasonable cluster head selection and energy-hole problems while nodes have unbalanced energy consumption. Thus, the paper aims to propose an energy-efficient non-uniform clustering routing protocol to enhance nodes energy efficiency and balance the energy consumption in WSNs. In addition, a non-uniform clustering network partition is introduce to reduce the probability of energy hole occurrences and optimize cluster heads dynamical selection method to suggest an improved shuffled frog leaping algorithm. Ultimately, the simulation experiment has demonstrated a 20% gain of energy efficiency to extend network lifetime by the proposed protocol and enhanced algorithm.

Improved localization with RSSI and DTN algorithms in wireless sensor networks

We introduce a Triangulation (DTN) algorithm that uses a WSN along with GP algorithm to enhance the accuracy of different location algorithms. The GP is used for the prediction of direction of the RSSI. The experimental results show that GP can able to predict the RSSI tendency and reduces variations of RSSI signal as the mobile user is in motion. The proposed triangulation algorithm cooperates with GP gives the lowest mean-distance error in runtime. Triangulation method proves better localization algorithm compared with other methods. Anchor nodes measures the RSSI of nearest neighbour nodes, based on RSSI values DTN method approximates the nearest neighbour nodes and DT triangles are formed.

Research on Node localization Algorithm in WSN Based on TDOA

Many applications of wireless sensor networks (WSN) require nodes to know their location information. Monitoring data without node location information is meaningless in many situations. Since the node positioning efficiency of WSN directly affects the performance of the network, designing an efficient positioning algorithm is the focus of WSN research. Based on the principle of TDOA positioning in WSN, it studies two main positioning algorithms: Chan and Taylor algorithm. Meanwhile the least square (LS) method is used to provide the initial position for Taylor algorithm and the first calculation result for Chan algorithm. Simulation results under different conditions show that the Chan algorithm has a good positioning accuracy and less computation than that of Taylor algorithm in the line of sight (LOS) environment. However, under the actual channel, due to the influence of No Line of Sight (NLOS), the positioning accuracy of Taylor algorithm is better than that of Chan algorithm, but its computation is large.

Performance comparison of communication protocol algorithms in WSN based agriculture: PlanTech case study

Wireless sensor networks (WSN) have become increasingly important in agriculture in recent years because they provide critical tools for better decision-making (knowledge -collection- storage- analysis-dissemination of data). These technologies provide resources for making informed decisions and implementing control measures on time. They are also essential tools for understanding the effects of climate change on agriculture, such as shrinking arable land and providing solutions to mitigate the negative consequences of these phenomena. In this paper, we propose a model based on WSN for the supervision of agricultural data. We investigate the impact of deterministic, random, and selective CH routing protocols on the network’s lifetime, residual energy, and exchanged information quantity. The deterministic CH protocol showed significant results in terms of the number of functional nodes and the energy consumption in the network. Thus, the selective CH protocol outperformed both protocols regarding the quantity of exchanged data in the network.