Dense Node Deployment Research Articles

Development of Fused Recurrent Neural Network‐Gated Recurrent Unit with Improved Sandpiper Optimization‐based Intelligent Node Localization Framework in WSN

SummaryEmploying mobile nodes or Sensor Nodes (SN) in the Wireless Sensor Networks (WSNs) is a critical task within the coverage area. However, these techniques produce huge errors in the evaluated distance between source nodes and unknown nodes, which increases the average NL error of the unknown node. The usage of a Global Positioning System (GPS) for all the SNs needs high cost for implementation in large‐scale WSNs. The designing of NL in WSN with less energy requirements and less cost is very significant. Enhanced NL is performed with deep learning techniques together with the heuristic strategy for performance enhancement. The developed localization model is mainly utilized for providing fast, scalable, and easily implementable results over heterogeneous and homogeneous networks. Moreover, it is applicable for sparse and dense deployment of nodes. Here, a hybridized deep structured approach is employed by integrating the Recurrent Neural Network (RNN) with a Gated Recurrent Unit (GRU) for calculating the distance among the source nodes to facilitate determining the optimal location for the unidentified node by analyzing the node information present in the received packets. Here, the optimization of constraints executed in the deep learning techniques with the Modified Exploration‐based Sandpiper Optimization Algorithm (ME‐SOA), and also it is utilized to find the optimal location of unknown nodes based on computed distance from the received packets that minimize the average localization error. The experimental results are validated and show the effectiveness of the proposed algorithm with a reduced error rate than other algorithms.

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

An Enhanced Spatial Correlation Framework for Heterogenous Wireless Sensor Networks

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

Optimizing Age of Information in Random-Access Poisson Networks

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks with a large number of nodes, severe interference may incur that leads to Age-of-Information (AoI) degradation. It is, therefore, important to study how to optimize the AoI performance. This article focuses on the AoI minimization in random-access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on which the optimal peak AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. The analysis shows that when the channel access probability (resp., the packet arrival rate) is given, the optimal packet arrival rate (resp., the optimal channel access probability) is equal to one when nodes are sparsely deployed, and decreases as the node deployment density increases. With a joint tuning of these two system parameters, the optimal channel access probability always equals one. Moreover, with the sole tuning of the channel access probability, the optimal peak AoI is improved with a smaller packet arrival rate only when the node deployment density is high. In contrast, a higher channel access probability always improves peak AoI performance when the packet arrival rate is solely tuned. The analysis in this article sheds important light on freshness-aware design for large-scale networks.

DIEER: Delay-Intolerant Energy-Efficient Routing with Sink Mobility in Underwater Wireless Sensor Networks.

Underwater Wireless Sensor Networks (UWSNs) are an enabling technology for many applications in commercial, military, and scientific domains. In some emergency response applications of UWSN, data dissemination is more important, therefore these applications are handled differently as compared to energy-focused approaches, which is only possible when propagation delay is minimized and packet delivery at surface sinks is assured. Packet delivery underwater is a serious concern because of harsh underwater environments and the dense deployment of nodes, which causes collisions and packet loss. Resultantly, re-transmission causes energy loss and increases end-to-end delay (). In this work, we devise a framework for the joint optimization of sink mobility, hold and forward mechanisms, adoptive depth threshold () and data aggregation with pattern matching for reducing nodal propagation delay, maximizing throughput, improving network lifetime, and minimizing energy consumption. To evaluate our technique, we simulate the three-dimensional (3-D) underwater network environment with mobile sink and dense deployments of sensor nodes with varying communication radii. We carry out scalability analysis of the proposed framework in terms of network lifetime, throughput, and packet drop. We also compare our framework to existing techniques, i.e., Mobicast and iAMCTD protocols. We note that adapting varying based on node density in a range of network deployment scenarios results in a reduced number of re-transmissions, good energy conservation, and enhanced throughput. Furthermore, results from extensive simulations show that our proposed framework achieves better performance over existing approaches for real-time delay-intolerant applications.

Impact of Sparse and Dense Deployment of Nodes Under Different Propagation Models in Manets

Mobile Ad-hoc Network (MANET) is the most emerging and fast-expanding technology in the last two decades. One of the major issues and challenging areas in MANET is the process of routing due to dynamic topologies and high mobility of mobile nodes. The efficiency and accuracy of a protocol depend on many parameters in these networks. In addition to other parameters node velocity and propagation models are among them. Calculating signal strength at the receiver is the responsibility of a propagation model while the mobility of nodes is responsible for the topology of the network. A huge amount of loss in performance is occurred due to the variation of signal strength at the receiver and obstacles between transmissions. In this paper,it has been analyzed to check the impact of different propagation models on the performance of Optimized Link State Routing (OLSR) in Sparse and Dense scenarios in MANET. The simulation has been carried out in NS-2 by using performance metrics as average packet drop average latency and average Throughput. The results predicted that propagation models and mobility have a strong impact on the performance of OLSR in considered scenarios.

Energy-Efficient Over-the-Air Computation Scheme for Densely Deployed IoT Networks

In this article, we propose a spatial sampling approach to reduce energy consumption for over-the-air (function) computation (AirComp) scheme by utilizing the cross-correlations among sensor readings. Since the conventional AirComp scheme leads to a reduction in total transmission time and latency thanks to the joint communication and computation processes, it is especially well-suited to Internet of Things (IoT) monitoring systems. AirComp lets simultaneous transmissions of all nodes by exploiting the superposition property of wireless channel; however, it does not overcome the high energy consumption paradigm, which is a fundamental problem of densely deployed IoT monitoring systems. We present a minimum mean square error (MMSE) estimation scheme while a small number of observations are available for densely deployed networks. The proposed MMSE estimator provides a significant mean squared error improvement with reducing energy consumption compared to the conventional estimator. Since the network lifetime of IoT monitoring systems can be almost doubled, the proposed estimator provides flexibility for the dense deployment of nodes. The simulation results verify the theoretical expressions.

RETRACTED ARTICLE: An Energy Efficient Framework for Densely Distributed WSNs IoT Devices Based on Tree Based Robust Cluster Head

There is a developing effect of WSNs (wireless Sensor Networks) on genuine applications. Various plans have been proposed for gathering information on multipath routing, tree, clustering and cluster trees. Existing schemes can’t give an ensured dependable system to versatility, movement, and end-to-end association, separately. Such kind of problems to be moderate, the proposed scheme considers a densely distributed WSN system model related to Internet-of-Things (IoT) and tree based cluster formation depending upon sensor node deployment density. For each tree based cluster having one cluster head node to attain energy efficient data gathering, a reinforcement learning based fuzzy inference system (RL-FIS) will applied to determine the data gathering node for every cluster present in the densely distributed WSNs based on three metrics: neighbourhood overlap, bipartivity index and algebraic connectivity. We compare our proposed scheme with the other schemes. Simulation results indicate that our proposed scheme outperform the other schemes in overall energy consumption saving and prolong the lifetime of the network.

Channel assignment based coding mechanism for reliable transmission for smart cities

The broadband wireless network provides an efficient solution for both data reliability and network congestion to meet the demand of the increasing number of mobile users. However, due to dense deployment of nodes, interference and data dissemination problems occur which result in unreliable packet delivery during data transmission in smart cities. All this creates problem in efficient data transmission. In order to solve this problem, in this work, we propose a novel Code-Encoding and Decoding (C-ED) mechanism for IEEE 802.16 networks that helps to mitigate the problem of data dissemination by using minimal number of channels. The proposed C-ED mechanism provides a reliable packet delivery during multicast transmission. As the smart cities are connected with complex networks, our scheme utilizes the existing channel assignment strategies to assign a minimal number of channels to the network and transmit the data using the proposed C-ED mechanism. To illustrate the effectiveness of the proposed C-ED mechanism, it is evaluated by combining it with the existing channel assignment strategies i.e., clique partitioning and random. The simulation results show that the C-ED mechanism with clique partitioning provides improved quality of service than random channel assignment. The C-ED mechanism is compared with Luby Transform (LT) codes which result in reduced transmission delay and improved throughput for a complex network.

Survey on state scheduling-based topology control in unattended wireless sensor networks

Wireless sensor networks comprise nodes with limited power supply. The dense deployment of nodes on a terrain for monitoring the environment causes contention in wireless channels that leads to interference and high energy consumption. The topology of the network changes frequently because of failure of nodes, addition of new nodes, and channel fading. Topology control is an important technique for reducing energy consumption, interference, and maintaining connectivity in the network. This paper provides a survey on state scheduling-based topology control techniques for unattended wireless sensor networks. Topology control algorithms for both flat and hierarchical networks are discussed. The algorithms are further categorized according to the key parameters used for state scheduling. The advantages, disadvantages, and results of the algorithms are tabulated. In addition, we highlight future research directions in designing state scheduling-based topology control algorithms.

Energy Based Efficiency Evaluation of Tree-Based Routing Protocols for Wireless Sensor Networks (WSNs)

A self-heal and self-organizing device deployed in a region of interest typically consists of low cost, low power and multi-functional miniature sensor nodes from a few hundreds to many thousands, described as the Wireless Sensor Network (WSN). The designing of a routing protocol for Wireless Sensor Network is very challenging due to a number of factors such as application specific, dense node deployment, frequent topology change, no global identification, unreliable sensor node, fault tolerant, server energy, computation and storage constraints. In the recent couple of years to prolong the network lifetime of tiny sensor node, different types of routing mechanisms have been proposed by various researchers. However, their performance superior or poorer cannot be examined without any doubt by traditional techniques due to their altered behavior in different scenarios. The aim of this research is to find the energy consumption profile over various metrics among energy efficient tree based routing protocols in Wireless Sensor Networks (WSNs). The present research work can be considered as to guide the protocol designing, application-specific protocol selection and modification of the existing energy efficient routing protocols in WSNs. Simulations among Iman ALMomani et al Protocol and an Energy Efficient Data Collection Protocol EEDCP-TB confirms their results in term of Overhead, Delay, Packet Delivery Ratio and Congestion over Energy consumption and Network lifetime.

Unequal Density-based Node Deployment and Clustering Routing Protocol in Wireless Sensor Networks

In order to solve the problem of the hot spots in clustering routing protocols, this paper proposes an unequal density-based node deployment and clustering routing protocol (UDNDC). It has following characteristics. (1) In clusters, member nodes transmit data directly to cluster head, and cluster head can fuse all signals into a single signal. But data from different clusters cannot be fused, and multi-hop forwarding is used among clusters to save energy. (2) In order to balance the energy depletion of nodes, the proposed protocol uses an unequal density-based node deployment strategy and analyzes the optimal node deployment density in different network regions in theory according to energy dissipation model of nodes. (3) It is distributed clustering protocol and doesn’t need overall information during cluster setup phase. (4) Cluster heads are chosen out by their residual energy and rotated periodically. (5) It can ensure quantity stability of cluster heads in each round and fairly even cluster head distribution across the network. Simulation results show that UDNDC can effectively solve the problem of the hot spots and obviously prolong the network lifetime.

Research on key nodes of wireless sensor network based on complex network theory

On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.

Mobile Scheduling for Spatiotemporal Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.

Energy-Efficient Beaconless Geographic Routing in Wireless Sensor Networks

Geographic routing is an attractive localized routing scheme for wireless sensor networks (WSNs) due to its desirable scalability and efficiency. Maintaining neighborhood information for packet forwarding can achieve a high efficiency in geographic routing, but may not be appropriate for WSNs in highly dynamic scenarios where network topology changes frequently due to nodes mobility and availability. We propose a novel online routing scheme, called Energy-efficient Beaconless Geographic Routing (EBGR), which can provide loop-free, fully stateless, energy-efficient sensor-to-sink routing at a low communication overhead without the help of prior neighborhood knowledge. In EBGR, each node first calculates its ideal next-hop relay position on the straight line toward the sink based on the energy-optimal forwarding distance, and each forwarder selects the neighbor closest to its ideal next-hop relay position as the next-hop relay using the Request-To-Send/Clear-To-Send (RTS/CTS) handshaking mechanism. We establish the lower and upper bounds on hop count and the upper bound on energy consumption under EBGR for sensor-to-sink routing, assuming no packet loss and no failures in greedy forwarding. Moreover, we demonstrate that the expected total energy consumption along a route toward the sink under EBGR approaches to the lower bound with the increase of node deployment density. We also extend EBGR to lossy sensor networks to provide energy-efficient routing in the presence of unreliable communication links. Simulation results show that our scheme significantly outperforms existing protocols in wireless sensor networks with highly dynamic network topologies.

Energy distribution‐aware clustering algorithm for dense wireless sensor networks

AbstractWe consider the challenge of organizing densely deployed sensor nodes into the form of clusters, using the distribution of network residual energy (NRE), which is defined as the sum of node residual energy. Irrespective of network topology, the distribution of NRE is proven to approach Gaussian in dense node deployment. A decentralized clustering algorithm is present, using timers and a recursively updated probability to select nodes with more residual energy to become Cluster Head (CH) nodes and organize other nodes in the form of clusters over slotted time intervals. Embracing the dense node deployment, each node initializes its probability of becoming a CH node using the distribution of NRE defined in its neighborhood area. Each of the selected CH nodes resides in the center of its cluster area, which has a radius that can be arbitrarily chosen. The performances of the new clustering algorithm are analyzed and then validated via extensive simulations, taking into account variable cluster radius and variable network density. The new clustering algorithm significantly prolongs the network lifetime, in comparison to several representative and competing clustering algorithms reported in the literature. Copyright © 2010 John Wiley & Sons, Ltd.

Triangle-based key management scheme for wireless sensor networks

For security services in wireless sensor networks, key management is a fundamental building block. In this article, we propose a triangle-based key predistribution approach and show that it can improve the effectiveness of key management in wireless sensor networks. This is achieved by using the bivariate polynomial in a triangle deployment system based on deployment information about expected locations of the sensor nodes. The analysis indicates that this scheme can achieve higher probability of both direct key establishment and indirect key establishment. On the other hand, the security analysis shows that its security against node capture would increase with a decrease of the sensor node deployment density and size of the deployment model and an increase of the polynomial degree.

Cognitive radio sensor networks

Dynamic spectrum access stands as a promising and spectrum-efficient communication approach for resource-constrained multihop wireless sensor networks due to their event-driven communication nature, which generally yields bursty traffic depending on the event characteristics. In addition, opportunistic spectrum access may also help realize the deployment of multiple overlaid sensor networks, and eliminate collision and excessive contention delay incurred by dense node deployment. Incorporating cognitive radio capability in sensor networks yields a new sensor networking paradigm (i.e., cognitive radio sensor networks). In this article the main design principles, potential advantages, application areas, and network architectures of CRSNs are introduced. The existing communication protocols and algorithms devised for cognitive radio networks and WSNs are discussed along with the open research avenues for the realization of CRSNs.

Building efficient wireless sensor networks with low-level naming

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational testbed.