Sink Node Research Articles

A score based link delay aware routing protocol to improve energy optimization in wireless sensor network

The Wireless Sensor Network (WSN) is the most appearing expertise that has potential applications broad ranges that include environment examining, smart spaces, medical systems, and robotic study. Energy efficiency is a consideration of vital design for WSN. The collision is occurred owing to the transmission of data from the sensor nodes and the traffic at the SINK node is high because of the excess data transmission by the sensor nodes. An essential division of the consumption of resources in a WSN is managed by the mechanism of Medium Access Control (MAC). An existing MAC protocols initiated for the utilization of WSNs single channel for data transmission. The deployment of wireless sensor nodes is concerned in the harsh environment in which the drastic change of conditions suffers from sudden changes in the status of a node and the quality of a link. With limited energy, the sensor nodes are supplied and a major concern is towards improving the lifetime of a network. To deal with these issues, a novel and simple routing mechanism Score based Link Delay Aware Routing (SBLDAR) is proposed, which utilizes the multi-channel MAC procedure comprises of Carrier Sense Multiple Access/ Collision Avoidance (CSMA/CA), to exchange the data, the activity of Time Division Multiple Access (TDMA) sequencing nodes and also Frequency Division Multiple Access FDMA) to allow collision-free exchange simultaneously. Also, in order to overcome this issue, an approach has been put out in this work that involves leveraging a variety of trust variables to identify malicious nodes and then using fuzzy based Modified Sun Flower Optimization (FMSFO) algorithm to elect an effective cluster head. The score based route selection increases the network lifetime and improves network performance. By comparing with the other protocols, the proposed SBLDAR combined with the technique of multi-MAC protocol has shown that it increases the network’s overall performance based on the simulation studies.

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

The usefulness of (d,b)-MCs and (d,b)-MPs in network reliability evaluation under delivery or maintenance cost constraints

Reliability and delivery cost or maintenance cost are important indicators showing the stability and the quality of operation of a multi-state flow network. Many authors developed (d,b)-minimal cut based or (d,b)-minimal path based algorithms to compute the network reliability, i.e., the probability that a demand d can be delivered from the source node to the sink node subject to the constraint that the total delivery cost (maintenance cost) is not greater than b. It is proven that contrary to prevalent methods in existing literature, both (d,b)-minimal cuts and (d,b)-minimal paths are insufficient to compute the exact value of the reliability of a multi-state flow network under budget or maintenance budget constraints. Moreover, it is also shown how this value can be determined.

Mobile chargers scheduling algorithm for maximum data flow in wireless sensor networks

Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.

Remote Data Acquisition Using UAVs and Custom Sensor Node Technology

Precision agriculture technology has advanced rapidly in the 21st century. Despite this, the vast majority of US farmers do not employ any form of precision agriculture. Reasons for this include the high initial cost, lack of internet connectivity in rural areas, and complex setup and operation. The basis of this project was to create a low-cost, energy-efficient data collection system using an unmanned aerial vehicle (UAV) as a mobile sink node in a local wireless system. This was accomplished through the design and manufacture of custom sensor nodes and a custom drone-mounted wireless receiver node. The sensor node and drone node enclosures were 3D printed and assembled using low-cost materials and internal components. The system was successfully tested in a field where it collected soil data, including soil moisture, soil temperature, and electrical conductivity. The cost and scalability of the system are discussed, as well as potential improvements and comparisons with existing technologies. The system was concluded to have many potential applications in its current state but with room to expand and improve its operation and features.

Effective Aggregate Data Collection and Enhanced Network Lifetime Using Energy Efficient Aggregation Data Convening Routing in Wireless Sensor Network

Wireless Technology is an integral part of today's applications. It encompasses commercial and human-oriented environmental monitoring, military applications, robotics, and healthcare monitoring systems, home or factory automation, and logistics. A wireless sensor network is a network system that uses wireless sensor nodes to monitor physical or environmental conditions such as voice, temperature, and spatial dispersive movements. Each node can locally sense its environment, process information and data, and send the data to one or more collection points within the WSN. The existing solution is categorized into member nodes and group/cluster heads (CH). The CH election process increases the overhead of the network and reduces the network lifetime. The processing and energy limitations of the nodes are considered for the CH election process. In this cluster formation methods aiming at the Cluster head selection process and providing trust in hierarchical WSN are proposed. The Energy Efficient Aggregation Data Convening Routing (E2ADCR) is used to estimate the routing path, and aggregate data collection to improve the network lifetime. The major advantage of this technique is to avoid the malicious or selfish node from becoming a dominant cluster in a group of clusters. Initially sink node selection forwards the Configuration Message (CM) to every node on the network to construct the performing node. In this, cluster selection is based on connection density, degree of the node angle, and residual energy (Quality Factor) that is evaluated from the link robustness, energy, and degree of the node. Multi-hop link transmission support path optimization technique is estimated in the path when the obstacle is present in the WSN. To introduce an Aggregated Support-based Data Collection to evaluate each packet flow monitor on the network to if any unrelated packet will eliminate to forward to the sink node. The proposed routing path of computational simplicity is achieved by a simple method. The new routing protocols, which were developed during this research, have better energy efficiency.

Secure Transmission in Wireless Sensor Networks via Reconfigurable Intelligent Surface

Nowadays, reconfigurable intelligent surfaces (RISs) are broadly used due to their flexibility and cost-effectiveness in controlling the wireless environment with low-cost passive reflecting elements. In addition, RIS-assisted systems offer extremely power-efficient alternatives to conventional multi-antenna amplify-and-forward relaying systems. This paper presents an RIS-assisted secure wireless sensor network (WSN) that consists of multiple sensor nodes, an eavesdropper, a friendly jammer, and a sink node. The RIS helps to improve the direct link between the sensor nodes and the sink node. Meanwhile, the jammer and the RIS deteriorate the wiretap channel for the eavesdropper. More specifically, we aim to maximize the secrecy rate through the joint optimization of the RIS phase shifts, sensor scheduling strategy and jamming power. At first, the scheduling strategy is selected, and then the problem is solved using the block coordinate descent (BCD) method. Simulation results show the significant improvement in the secrecy performance achieved through the proposed RIS-assisted system.

Joint Effects of Void Region Size and Sink Architecture on Underwater WSNs Lifetime

Underwater wireless sensor networks (UWSNs) typically suffer from the communication void region problem. A common method to handle the void region problem is to re-route the packets around the void regions. As the size of the void regions increases, packets require more hops to circumvent the large void regions, resulting in a short network lifetime. On the other hand, the void region problem is more destructive in UWSNs utilizing a single-sink architecture than a multi-sink architecture since nodes consume excessive energy for bypassing the void regions to reach the sink node, which can be positioned in a hard-to-reach area in single-sink UWSNs. In this work, an integer linear programming (ILP) model is developed for maximizing UWSNs lifetime while bypassing the void regions. Solving the ILP model to optimality, the joint impact of the void region size and the sink architecture type on UWSNs lifetime is investigated. The results show that the performance of UWSNs significantly drops as the size of the void region grows such that UWSNs lifetimes shorten by up to 61% as the total void region size is one-quarter of the network size. Moreover, multi-sink UWSNs yield better performance than single-sink UWSNs in the void region problem.

Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs

Wireless sensor networks (W.S.N.s) are a critical research area with numerous practical applications. W.S.N.s are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As W.S.N.s advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. The proposed model uses bipartite graph properties to derive a controlled mobility model for the mobile sink. As a result, stationary nodes will be visited and planned to reduce routing overhead and enhance the network’s performance. Using the bipartite graph’s properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.

An Efficient Compressive Sensing Event-Detection Scheme for Internet of Things System Based on Sparse-Graph Codes

This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering the high-dimensional integer-valued sparse signal from incomplete linear measurements. We show that the sensing process in IoT system produces an equivalent integer CS using sparse graph codes at the sink node, for which one can devise a simple deterministic construction of a sparse measurement matrix and an efficient integer-valued signal recovery algorithm. We validated the determined measurement matrix, uniquely determined the signal coefficients, and performed an asymptotic analysis to examine the performance of the proposed approach, namely event detection with integer sum peeling (ISP), with the density evolution method. Simulation results show that the proposed ISP approach achieves a significantly higher performance compared to existing literature at various simulation scenario and match that of the theoretical results.

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.

Energy aware optimized dynamic routing mechanism in wireless sensor networks

A trade-off between energy efficiency and optimized routing is massively recommended for transmission efficiency enhancement in wireless sensor networks (WSNs). Therefore, in this paper, graph-based energy optimized dynamic routing (GEODR) mechanism is introduced to set up a balance between energy consumption minimization and throughput enhancement using a dynamic and optimized routing mechanism in WSNs. A clustering scheme is employed based on graph theory, and cluster boundaries are formed using distance vectors. Cluster head (CH) selection is performed based on residual energy, the distance between CHs, and the mobility of the sink node. Each cluster is scattered with multiple tiny nodes, and event monitoring is performed. A model for graph-based dynamic routing to transmit data packets, cluster and cluster boundary formation, and optimization of routing problems is discussed. The performance efficiency of the proposed GEODR mechanism is determined by taking 100 sensor nodes, and 20 nodes are selected as CHs in a sensor network, and several other network parameters are also considered. A massive improvement in energy is observed by using sink node mobility. Experimental results are obtained using the proposed GEODR mechanism in terms of data packet transmission, alive nodes, dead nodes, and residual energy and compared against classical routing mechanisms such as low energy adaptive clustering hierarchy (LEACH) and stable election protocol (SEP).

A survey on artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs

Despite the significant limits put on the resources of the sensor nodes, such as storage, processing power, communication range, and energy, the spectrum of applications for wireless sensor networks (WSNs) is expanding. This is the case even though WSNs have severe limitations. The primary goals of a WSN are to reduce the amount of energy used and the amount of time it takes to transmit data to the sink node. When a significant number of nodes are being installed, as is the situation with monitoring industrial pollutants, this becomes an extremely important point of discussion. We present an energy-efficient and resilient routing method for wireless sensor networks (WSNs) dubbed ELDC that is based on artificial neural networks. In this method, the network is trained on a massive data set that encompasses the vast majority of possible situations. This helps the network become more dependable and responsive to its surroundings. In addition to this, it makes use of a technique that is based on groups, with the understanding that the sizes of the groups might vary. This helps to extend the life of the network as a whole. An artificial neural network enables intelligent, efficient, and robust group organization by providing efficient threshold values for the selection of a group’s chief node and a cluster head based on the back propagation technique. These threshold values are provided by an artificial neural network. As a result, the method that we have suggested is very good at saving energy and is able to extend the life of sensor nodes.

MODEL OF A CHAOTIC ULTRA-WIDEBAND INFORMATION TRANSMISSION SYSTEM FOR WIRELESS SENSOR NETWORKS

Distributer measuring systems, consisting of interconnected wireless sensor nodes with autonomous supply, have plenty of applications in different areas of engineering end technology. Therefore, wireless sensor network design and optimization is a vital problem in informational technologies and computer engineering. Regardless of particular application and architecture, a wireless sensor network consists of several small sensor nodes somehow distributed in an area of inspection. Such a design ensures a distributed parameter measuring, according to which, each node performs preprocessing procedures for the data, which are to be aggregated by a so-called sink node and sent via other networks, e.g., wired or wireless local area network. For healthcare solutions, it is important to ensure a stable connection with the network and server back-end to provide a high-level analysis based on predicting methods of machine learning algorithms. This paper describes an application of an ultra-wideband communication model based on chaos synchronization as a low-power and efficient alternative solution for building wireless sensor networks. Thus, using chaotic signals as information carriers in wireless communication has several advantages, including a wide smooth spectrum, high information capability, cybersecurity, and low power consumption. The synchronization problem is one of the most vital tasks to be solved to design a chaos application for ultra-wide-band communication. Being well-studied for periodic signals, modern synchronization theory contains plenty of solutions for classical telecommunication and radio engineering systems, however, it is not developed enough for chaotic systems. Hence, a one-directional dissipative synchronization between two Chen systems is studied in the first section. The second section is devoted to the computer simulation of the model, described in the previous section. All the models built and the simulations performed have been done using MATLAB/Simulink software. The negative impact of channel noise and inequality of system parameters is considered. The possible way how to improve technical characteristics is also provided. Proposed models are to be used to design and develop low-cost wireless sensor networks for multi-channel healthcare solutions.

A practical method for connectivity and coverage reliability analysis for linear wireless sensor networks

Linear Wireless Sensor Networks (LWSNs) are the most suitable wireless sensor networks (WSNs) for monitoring the condition of infrastructures with linear typologies, e.g., railways, gas, or oil pipelines. Reliability of LWSNs is crucial for such applications due to their safety–critical or mission-critical nature. To this end, LWSNs of different designs need to be compared in terms of reliability to ascertain high-cost performance. Connectivity and coverage are fundamental metrics used to measure the reliability of LWSNs. However, to the best of our knowledge, there are no proper methods or tools to evaluate the reliability of LWSNs concurrently in terms of connectivity and coverage. In this paper, by reducing the size of system state space depending on the characteristics of LWSNs, we present a practical method for concurrent reliability analysis in terms of connectivity and coverage for LWSNs based on hybrid models of binary decision diagrams and divide-and-conquer schemes. The proposed method can be used to assess various LWSNs, e.g., networks with one or two sink nodes, as well as flat-based or cluster-based networks. Moreover, transmission and coverage ranges can be assigned to sensor nodes using this method. Case studies are presented to substantiate the viability of the proposed model, and some beneficial results are deduced, which can be considered to serve as basic principles for LWSN-design to ensure high-cost performance.

Cable Coupling Characteristics of Wireless Network Convergence Nodes by Transient Electromagnetic Fields in Intelligent Substations

Switching operations in intelligent substations generate a wide range of extremely strong and asymmetric transient spatial electromagnetic fields, and this complex transient electromagnetic field can have a coupling effect with the wireless network sink node equipment cables. We conducted a study on the networking method of wireless network equipment in smart substations and analyzed the types of sink node cables used. In the GIS room of the 110 kV GIS smart substation, the transient spatial electromagnetic field generated by switching operation and the transient induced current of the sink node cable under different operating conditions were tested and obtained, and the time domain and frequency domain comparative analysis was performed. A near-field coupling model of the asymmetric and non-uniform cable transient spatial electric field at the sink node is established, and the validity of the model is verified by comparing and analyzing the measurement and simulation results. The results of the study can provide a reference for transient electromagnetic disturbances in 110 kV GIS intelligent substations and contribute to improving the reliability of wireless network equipment operation.

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%.

Relay Selection for Over-the-Air Computation Achieving Both Long Lifetime and High Reliability.

In a general wireless sensor network, a sink node collects data from each node successively and then post-processes the data to obtain useful information. However, conventional methods have a scalability problem: the data collection/processing time increases with the number of nodes, and frequent transmission collisions degrade spectrum efficiency. If only statistical values of the data are needed, using over-the-air computation (AirComp) can efficiently perform data collection and computation. However, AirComp also has its problems: when the channel gain of a node is too low, (i) the transmission power of that node will be high, decreasing the lifetime of that node and the entire network, and (ii) sometimes, the computation error still occurs even though the maximal transmission power is used. To jointly solve these two problems, in this paper we investigate the relay communication for AirComp and study a relay selection protocol. The basic method selects an ordinary node with a good channel condition as a relay node, considering both computation error and power consumption. This method is further enhanced to explicitly consider network lifetime in relay selection. Extensive simulation evaluations confirm that the proposed method helps to prolong the lifetime of the entire network and reduce computation errors as well.

Age Prediction for Energy-Aware Communication in WSN Using Hybrid Optimization-Enabled Deep Belief Network

To perceive the data utilizing sensor nodes, wireless sensor network (WSN) consists of several nodes connected to a wireless channel. However, the sink node, also known as a base station (BS), provides power to the WSN and acts as an access node for a number of the network’s sensor devices. Weather monitoring, field surveillance, and the collection of meteorological data are just a few of the various uses for WSN. The energy of each node directly affects how long a wireless network will last. So, to increase the lifespan of WSN, effective routing is required. Using the suggested Taylor sea lion optimization-based deep belief network (TSLnO-based DBN), the ultimate purpose of this research is to build a method for energy-aware communication in WSN. In the setup stage, cluster head (CH) is chosen using a hybrid optimization technique called ant lion whale optimization (ALWO), which is created by fusing the whale optimization algorithm (WOA) and ant lion optimizer (ALO). It is important to note that CH’s selection criteria are solely based on fitness factors such as energy and distance. The second phase, known as the steady state step, is when the updating of energy and trust takes place. In the prediction phase, the network classifier is trained using a newly created optimization method called TSLnO, and the age of neighbor nodes is predicted by estimating the energy of neighbors using DBN. By combining the Taylor Series and the sea lion optimization (SLnO) method, the proposed TSLnO is produced. The communication/route discovery phase, which occurs in the fourth phase, is where the path through nearby nodes is chosen. The maintenance phase of the route is the fifth phase.

Data Acquisition Control for UAV-Enabled Wireless Rechargeable Sensor Networks

In the realm of Internet of Things (IoT), wireless sensor networks (WSNs) have been the subject of ongoing research into the use of energy harvesting to capture ambient energy, and wireless power transfer (WPT) via a mobile charger to overcome the energy limitations of sensors. Moreover, to mitigate energy imbalance and reduce the number of hops, strategies have been developed to leverage cars or unmanned aerial vehicles (UAVs) as mobile sinks. The primary objective of this work is to increase network lifetime by reducing energy consumption of hotspot nodes and increasing the amount of data acquired from all sensors in an environment that combines the methods mentioned above.To achieve this objective, the proposed method involves developing multiple minimum depth trees (MDTs) for all nodes, considering the energy of the UAV and sensor nodes. Parent nodes prevent their own energy depletion and ensure data transmission without imbalance by adaptively controlling the data sensed at the nodes and their child nodes. Consequently, the energy depletion of nodes in hotspots is prevented, more sensory data is acquired, and balanced data collection from all nodes is achieved. Simulation results demonstrate that the proposed scheme outperforms other state-of-the-art methods in terms of reduced energy depletion, increased network connectivity, and the amount of data collected at the sink node. This scheme will be applied to applications that collect environmental data outdoors, such as climate measurement, to collect data uniformly and increase the lifespan of the network, thereby reducing network maintenance costs while collecting data effectively.