Algorithm For Sensor Networks Research Articles

A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method

A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method

Path loss model based on exponential water cycle algorithm for wireless sensor network

SummaryWireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient . The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient of the proposed EWC model is 0.9993, whereas the existing PE‐PSO, LNSM, and PSO‐Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.

Robust information filtering for distributed networks using mixed Gaussian distribution

This paper proposes a robust distributed state estimation algorithm for sensor networks. In a distributed sensor network, sensor nodes may be affected by outliers and thus influence the information fusion results. In this respect, the measurement likelihood and one-step state prediction are modeled by a mixture of Gaussian distributions. In this Gaussian mixture, one Gaussian uses nominal covariance, while the other uses adaptive covariance with larger values and lower probabilities. We build a centralized multi-sensor fusion model in the variational Bayesian framework based on the aforementioned mixture of Gaussian distributions. Then, a consensus strategy is employed to convert the centralized method into a distributed one. Each node completes estimation tasks separately based on the information from itself and nearby nodes. In addition, the weight of sensor information will adaptively update according to the Gaussian distribution that mainly affects the current sensor measurement noise. Finally, simulation experiments demonstrate the effectiveness of the proposed method in handling outlier effects.

Enhanced Cost and Sub-epoch Based Stable Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Enhanced Cost and Sub-epoch Based Stable Energy-Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

A hybrid sensing of rotation-induced stress of segmental lining during shield tunneling via WSN and surrogate numerical modeling

The structural responses of segmental lining to the construction loads during shield tunneling are not well understood yet. To this end, a hybrid sensing method is proposed to understand the mechanical responses of tunnel lining to the construction loads via a wireless sensor network (WSN) and machine learning algorithms. Firstly, the displacement of tunnel lining is monitored in real-time during construction by using the WSN. The rotation of the whole segmental ring is found for the first time based on the in-situ data. A refined three-dimensional numerical model is then applied to investigate the additional stress of segmental rings under such a new deformation mode. Subsequently, a scenario-based construction database is established, including 130 numerical cases of load conditions. For the dynamic control of shield tunneling, an efficient multi-layer perception surrogate model is proposed with the help of the established database. The additional stress state of segmental rings is sensed on-site in a hybrid mode efficiently, both from real structural responses via WSN and the construction parameters via surrogate model. Meanwhile, the construction parameters can be adjusted timely if the additional stress exceeds its limit. For the case study of this paper, with the measured maximum ring rotation at 0.25° and the maximum arc length of circumferential misalignment between two adjacent rings at 7.1 mm, an additional rotation-induced stress of 19.34*103 kN/m2, i.e., 39% of the concrete compressive strength, is found for the first time at longitudinal bolt holes of segmental rings, which has not been considered but should be emphasized in the future design practice.

Application of multi-target positioning algorithm in basketball teaching system and injury prevention

In today's era, the rapid spread of information and the rise of major network platforms provide good conditions for online learning and at the same time provide a new development idea for basketball teaching. The establishment of modern multimedia teaching theory provides theoretical support for the research and development of this subject. Construct and apply the online learning system of basketball basic technology, provide high-quality education, and can accommodate a large number of basketball fans to study and exchange together, and improve the physical fitness of young people. This thesis combines the multi-target localization algorithm in wireless sensor networks, first introduces the related concepts and basic theories of UWSNs, and then summarizes the related theories of node localization technology. In the course of basketball teaching, the advantages and disadvantages of the wireless sensor network multi-target positioning algorithm and the research direction are introduced. Aiming at the problem of low positioning accuracy and high complexity of the basketball motion prediction positioning algorithm, a multi-target positioning algorithm improved by backtracking search is proposed. The MBSA algorithm improves the positioning accuracy of anchor points, and uses cooperation mechanisms and basketball mobility to locate nodes for unknown actions. An improved BSA algorithm, namely the KLBSA-LoT algorithm, is proposed. The KLBSA-LoT algorithm accurately locates the anchor point and uses a cooperative mechanism to predict the position information of unknown nodes in basketball. Applying this algorithm to basketball teaching can greatly improve the accuracy of its positioning training. Sports teaching provides new methods.

ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network

ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network

Energy‐optimized cluster head selection based on enhanced remora optimization algorithm in underwater wireless sensor network

SummaryWith the technological advancements, wireless sensor network (WSN) has played an impeccable role in monitoring the underwater applications. Underwater WSN (UWSN) is supported by WSN but subjected to data dissemination in an acoustic medium. Due to challenging conditions in underwater scenario, the limited battery resources of these sensor nodes stem to a crucial research problem that needs to address the energy‐efficient routing in UWSN. In this research work, we intend to propose an energy‐optimized cluster head (CH) selection based on enhanced remora optimization algorithm (ECERO) in UWSN. Since CH devours the maximum energy among the nodes, we perform selection of CH based on EROA while considering energy, Euclidean distance from sink, node density, network's average energy, acoustic path loss model and lastly, the adaptive quantity of CHs in the network. Further, to reduce the load on CH node, we introduce the concept of sleep scheduling among the closely located cluster nodes. The proposed work improves the performance of recently proposed EOCSR algorithm by great magnitude which claims to mitigate hot‐spot problem, but EOCSR still suffers from the same due to relaying a large magnitude of data.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

Low-power time synchronization algorithm for wireless sensor networks

Time synchronization technology is very significant for wireless sensor networks. A typical time synchronization algorithm is applied to the multi-hop topology network, which has the problems of poor time synchronization accuracy, slow convergence speed, and high power consumption. In this paper, a low-power time synchronization algorithm (LPTSA) is proposed. It is based on clustering in a multi-hop environment and adopts a node hierarchy strategy to reduce the synchronous communication overhead. The synchronization error compensation mechanism is adopted to reduce the impact of algorithm synchronization errors. The clustering algorithm divides the whole network into a primary network and a secondary network. By using different time synchronization algorithms in the primary and secondary networks respectively, unnecessary message overhead is reduced. Simulation shows that the algorithm effectively reduces energy consumption and improves synchronization accuracy.

Estimation of coverage and energy in bio inspired wireless sensors using Harris hawk algorithm

Wireless sensor networks have various sensors which are wide spread and also equipped with supplies. For the deployment sensor nodes are used for capturing the information, the region of interest is selected and the nodes are deployed. Lower sensing power degrades the DC supply reducing the life of wireless sensor networks, this can also be due to improper sensor deployment. Based on the above various wireless sensor network algorithms are available to compute and implement the required optimal figures. Harris hawk is among the one such algorithm used in wireless sensors. It works on the principle of the bird Harris hawk which catches the prey from a very high altitude, resemblance to this and many other features it is implemented in wireless sensors. Various wireless sensor characteristics can be found, figured and tabulated which are essential in this domain. The characteristics like coverage, connectivity, location, energy, and can be estimated. In the work the coverage and energy fitness is estimated using Harris hawk algorithm and its results are being illustrated.

Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks

Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an essential approach for extending the lifetime of wireless sensor networks (WSNs), which makes sensor nodes adapt to changing environments by calculating optimal decision solutions in real time. However, as WSN grows, error propagation cannot keep up with topological changes in the networks, which prevents the sensor nodes’ routing decisions from being timely optimized. In this paper, we propose the DNN-HR algorithm, which optimizes the reinforcement learning process of WSN in three ways. First, this paper constructs a two-level learning structure. Discrete nodes perform reinforcement learning during the interaction with their neighboring nodes. Simultaneously, the neural networks that fit the V values guide the underlying nodes’ learning. Second, the algorithm divides the sensor networks into two-level subspaces according to their geographic locations, and cluster heads are elected in each subspace to form weight incremental propagation trees. Third, each sensor node sends the weight increments it learned to its first-level cluster head. At the first-level cluster head, the weight increments sent by multiple nodes are received and accumulated, and then the results are sent to the second-level cluster head. The base station acquires the weight increments of all the second-level cluster heads and then broadcasts the updated neural network weights to all the WSN nodes. Experiments determine the appropriate heuristic parameters. Compared to other algorithms, the experiments show that our proposed scheme significantly improves the packet delivery rate, node survival rate, and energy standard deviation of nodes.

HABC-MD: a novel routing algorithm for wireless sensor network

HABC-MD: a novel routing algorithm for wireless sensor network

Traffic and delay aware routing using optimization algorithm for wireless sensor networks

The Internet of Things (IoT) is a system of machines, computing devices, electronic equipment, and different sensors. It forms a network, where the transmission of device-related data can be accomplished. The devices in the IoT are connected to each other through wireless links and form ad-hoc networks. In IoT based applications, the lifetime of the communicating nodes is a greater concern. The network lifetime can be maximized by introducing energy efficient data transmission in the network. Therefore, a traffic and delay-aware energy-efficient routing (TADEER) protocol for IoT-based networks are proposed in this work. The proposed technique assigns delay for transmitting data based on the criticality level of data and traffic rate at the forwarding nodes. Fixing delays for data transmission helps to avoid unnecessary transmissions. The route selection process is implemented using an optimization algorithm. A Fuzzy logic (FL) based biogeography-based optimization (BBO) algorithm is presented in this work. Thus, the number of data transmission and energy consumption can be reduced. The performance of the proposed method is evaluated by analyzing transmission delay, network lifetime, and energy consumption. By comparing the simulation results to the existing methods TEAR and ETASA, the simulation results are validated.

Data Fusion Algorithm For Wireless Sensor Networks Based On Cluster Routing And Deep Learning

In order to make full use of node energy and extend the network’s lifetime, a data fusion algorithm for wireless sensor networks is proposed in this paper. The algorithm improves the traditional clustering routing protocol and introduces a deep learning model to build the data fusion algorithm based on the optimized clustering architecture. When optimizing cluster routing protocol, the cluster head is selected by comprehensively considering such factors as node density, residual energy of nodes and the reasonable cluster size. The deep learning model and classification model are introduced in the data fusion process. The optimal parameters of the network are obtained by training the deep learning model of the sink node, then the sink nodes transfer these parameters to the corresponding sensor nodes. The cluster head extracts, classifies and fuses similar features of the data. The nodes in the cluster use the deep learning model to fit the collected raw data. Simulation results show that the data fusion algorithm proposed in this paper can reduce the redundant data transmitted to the sink node of wireless sensor networks, reduce the network energy consumption and prolong the network’s life. The overall performance of the network is improved.

Correction to: Green and friendly media transmission algorithms for wireless body sensor networks

Correction to: Green and friendly media transmission algorithms for wireless body sensor networks

FTOR-Mod PSO: A fault tolerance and an optimal relay node selection algorithm for wireless sensor networks using modified PSO

The wireless sensor network (WSN) is deployed with enormous sensors responsible for data collection. The most critical issue in WSN is higher energy utilization which degrades network lifetime and communication ability. Hence, several methods are applied in WSNs to expand network lifetime. Clustering is a proven technique for reducing energy consumption in WSNs. CH collects data from different sensor nodes (SN) in every cluster and then forwards it to the sink node. On the other hand, fault occurrence in CH collapses entire data communication. Hence, this paper proposes a fault-tolerant cluster-based routing technique to avoid such difficulties. The proposed scheme is fault tolerance and optimal relay node with modified Particle Swarm Optimization FTOR-Mod PSO. Here, fault tolerance is achieved by selecting the backup cluster head (BKCH) for every cluster through the battle royale optimization (BRO) algorithm. Moreover, an aggregator node (AG) is adopted for inter-cluster data transmission, and modified PSO (Mod PSO) identifies CH and AG’s best relay. Results demonstrate that the proposed method reduces delay to 0.15 s by reducing the routing overhead to 2.4%, thereby improving the network lifetime to 92.714%.

Improved algorithms in directional wireless sensor networks

Improved algorithms in directional wireless sensor networks

Parallel Finite Field Algorithms for Communications and Sensor Networks

Sensor network integrates sensor technology, embedded computing technology, modern network and wireless communication technology, distributed information processing technology, etc. It can monitor, perceive and collect the information of various environments or monitoring objects in real time through various integrated micro sensors, and process the information through the embedded system. The perceived information is transmitted to the user terminal by multi-hop relay through the random ad-hoc wireless communication network. The coding of communications and sensor networks affects the efficiency of communication. The main coding technology is based on finite field. Among operations in finite fields, divisions are time-consuming operations since they are generally computed via inversions and multiplications. We present techniques to exploit extend binary trees for fast divisions in finite fields. We implement the finite field algorithm on Field Programmable Logic Arrays (FPGAs) in a parallel way. The implementation result from FPGAs shows that the parallel finite field algorithm can speed up the process of coding of communications and sensor networks.

Routing optimization algorithm based on mobile agent for wireless sensor networks

A routing model about mobile agent in sensor networks was drawn out, and an optimization problem of mobile agent static route was derived. In order to optimize the route of mobile agent, a target location algorithm in wireless sensor networks was proposed. All the sensor nodes which know target compete to get the mobile agent in order to track the location of targets. Competition rules are defined by the remaining value of the energy and the distance between the nodes and the target. Simulation results showed that this algorithm could provide less energy consumption compared to other schemes and prolong survival cycle time. It balances the network energy, reduces the amount of data transmission in the network, reduces the network energy overhead, prolongs the lifetime of the entire network, and effectively reduces the network delay.