Traditional Wireless Sensor Networks Research Articles

Manoeuvre Target Tracking in Wireless Sensor Networks Using Convolutional Bi-Directional Long Short-Term Memory Neural Networks and Extended Kalman Filtering.

Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios.

Latency and Residual Energy Analysis of MIMO Heterogeneous Wireless Sensor Networks

Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized application of MIMO technology within WSNs. This approach operates effectively, especially in challenging and resource-constrained environments. By facilitating collaboration among sensor nodes, Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments. Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring, industrial automation, and healthcare applications. This research paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO using Network Simulator NS2.35 for analysis of End to End Delay for packet transmission and Residual energy of nodes. The research work shows application of MIMO in Wireless Sensor Networks with considerable improvements in Quality of Service parameters which is achieved through Spatial Multiplexing and Diversity Gain. MIMO enables multiple spatial streams, allowing several data streams to be transmitted simultaneously on the same channel. This increases the overall throughput as multiple sensors can transmit their data concurrently without interference. MIMO systems also provide diversity gain by transmitting multiple copies of the same data over different antennas which helps in mitigating the effects of fading and interference, resulting in a more reliable and higher-throughput communication link as compared to a SISO channel. Another advantage of employing MIMO in WSN is reduction in End-to-End delays in data transmission. Last but not the least, MIMO can be configured to optimize the power consumption of individual sensors by adjusting the number of antennas used and transmission power levels based on channel conditions. Hence, MIMO can help to extend the network's lifetime by conserving energy in resource-constrained sensor nodes by preservation of Residual Energy.

Delay-sensitive energy-efficient routing scheme for the Wireless Sensor Network with path-constrained mobile sink

The use of mobile sinks (MSs) can address several challenges for traditional wireless sensor networks (WSNs), such as energy holes, meanwhile it also introduces some new issues. Especially, for the path constraint MS, the fixed trajectory and uncontrollable moving speed results in significant delay and even buffer overflows. Existing research mainly focused on delay-tolerant applications which are unsuitable for delay-sensitive data or adopted the greedy policy to collect data which is inefficient in energy. To minimize the energy consumption and guarantee delay requirement simultaneously, we propose a delay-sensitive and energy-efficient routing scheme. Firstly, to obtain the MS position, we propose a K-nearest neighbor-based prediction model, which can reduce the energy and delay overhead of the location update. Based on the prediction result, an efficient cluster routing scheme is designed. To ensure both search speed and the optimality of the cluster head (CH), we improve the Reed-Deer algorithm (RDA) as IRDA, and use the IRDA to find out CHs. To remove the redundancy of the CH, a removal method is proposed. After constructing clusters, a method is used to determine the next-hop of the CH by using the predication position of the MS, the received timestamp and the delay requirement of the data. A fault tolerance mechanism is supplied to deal the predication error and other unexpected situations. By applying IRDA and the prediction model, the proposed routing scheme avoids the frequent construction of new routes, reduces the hops of the end-to-end path, minimizes network energy consumption, and ensures timely data transmission. The comparison results indicates that the proposed protocol can effectively improve the network performance while guarantee delay requirement. Especially in realistic scenario, the proposed algorithm can effectively reduce energy consumption and the end-to-end delay and improve the packet delivery ratio.

WOAD3QN-RP: An intelligent routing protocol in wireless sensor networks — A swarm intelligence and deep reinforcement learning based approach

Wireless Sensor Networks (WSN) are a crucial part of the Internet of Things (IoT), and research on WSN routing protocols has always been a hot topic in academia. However, traditional WSN routing protocols have limited utilization of available information during the routing decision process, leading to challenges such as insufficient adaptability to network topology changes, high communication delays, and short network lifetimes. To address these issues, this paper proposes an innovative intelligent routing algorithm WOAD3QN-RP, which cleverly integrates swarm intelligence algorithms and deep reinforcement learning. The WOAD3QN-RP not only effectively reduces delay but also balances energy consumption and flexibly adapts to changes in network topology, while simultaneously determining the optimal multi-hop path, effectively extending the lifetime of the network. Firstly, the WOAD3QN-RP algorithm employs the Whale Optimization Algorithm (WOA) to determine the optimal cluster heads (CHs). In the process of selecting CHs, the algorithm comprehensively considers key factors such as the residual energy of nodes, node distance, and communication delay, thereby significantly improving the accuracy and efficiency of CH selection, which contributes to better energy distribution and performance of the network. Secondly, in terms of multi-hop path selection, WOAD3QN-RP uses a dueling double deep Q-network (D3QN) to determine the optimal multi-hop path. Through utilizing neural networks to interact with the environment, intelligent agents are trained to learn routing policies to adapt to dynamic changes in the network topology and ensure the balance between energy consumption and multi-hop routing performance. Experimental results show that WOAD3QN-RP exhibits significant advantages over existing routing protocols in terms of network lifetime, energy efficiency, and communication delay.

Blockchain Based Wireless Sensor Networks for Detecting Nodes

Abstract The versatility of Wireless Sensor Networks (WSNs) and other unique qualities inspired by real-time collaboration between the sensor nodes make them widely used for a range of applications in monitoring and surveillance. As solutions for malicious node identification involve a single, centralized decision-making process, security in WSNs is becoming more and more important. Errors are difficult to prevent with this paradigm, and repeatability and traceability are problems. Therefore, the detecting ability and integrity of the developed detection method cannot be guaranteed by malicious node identification technology in ordinary WSNs. This article provides insights into this innovative idea as well as a thorough investigation of the use of blockchain technology to WSNs (Blockchain-Based WSN), including a full analysis of a blockchain-based method for identifying rogue nodes. The Blockchain-Based WSN architecture, business-specific applications, and use are all included in this study. The Blockchain-Based WSN architecture model for tracking malicious nodes and the smart contract features of malicious node detection is both included in the explanation of harmful node identification based on Blockchain-Based WSN in this paper. The second part of this study investigates the use of blockchain technology to the management of WSN data, which necessitates online information aggregation and may also require auditing, event logging, and storage for information analysis and offline query processing. The traditional WSN data management approaches are discussed first in this study, followed by the blockchain-based WSN data management choices. The benefaction of blockchain to WSN security management is also included in this survey. Before discussing blockchain-based WSN security management solutions, such as those that provide access control, protect data integrity, ensure anonymity, and prolong the life of WSN nodes, it first looks at the security vulnerabilities of centralized WSN models.

Performance Analysis of MIMO Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSN) are widely used in remote applications related to defence and healthcare. A network with nodes having different capabilities like sensing, various computational capabilities, power-efficient communication, and a varied sensing range is called a heterogeneous wireless sensor network. Heterogeneous wireless sensor networks using MIMO wireless channels are more useful for energy-efficient multi-channel communication. MIMO applications in wireless sensor networks have the potential to enhance throughput, reduce End-to-End Delay, improve packet delivery ratios, and conserve energy in wireless sensor networks. Its implementation needs to be carefully considered in light of the specific deployment conditions and resource constraints of the network, considering proper antenna design, synchronisation mechanisms, and energy-efficient algorithms. This paper presents a comparative performance analysis of MIMO wireless sensor networks and traditional wireless sensor networks without MIMO for various Quality of Service parameters like Packet Delivery Ratio, End to End Delay, Throughput and Residual energy. The research work shows that the application of MIMO in Wireless Sensor Networks enables sensor nodes to collaborate effectively, leading to improved reliability and coverage, and also increases the network's lifetime by conserving energy in resource-constrained sensor nodes through the preservation of Residual Energy.

Construction of rehabilitation training simulation model based on energy monitoring and transmission by wireless sensor networks

In today's world, with the advent of information technology, the speed of social development has also been greatly improved, and the national demand is also increasing. Among them, national health issues have always been the focus of countries. In addition to basic medical testing, the rehabilitation training for special groups in the medical industry is also gradually becoming more mature. In traditional wireless sensor networks, the energy consumption of nodes is often high, because each node needs to continuously send and receive data, leading to energy waste. In addition, many nodes remain active even when data transmission is not required, further increasing energy consumption. The coverage range of traditional wireless sensor networks is influenced by the number and distribution of nodes, often resulting in coverage vulnerabilities and blind spots. This makes it impossible for the network to fully cover the target area, reducing the reliability and accuracy of monitoring. Based on the above situation, this paper studies the construction of rehabilitation training simulation model by using wireless sensor network monitoring coverage and transmission technology on the basis of energy acquisition. Firstly, it discusses the development trend of rehabilitation training technology in various countries and the social status of rehabilitation training. Through the use of wireless sensor network monitors to monitor the target data collection and model construction. The target coverage and transmission speed are optimized by combining reinforcement learning algorithm SARSA and multilayer feedforward BP neural network. The sensor coverage and transmission of monitoring targets are also studied. The results show that the optimized wireless sensor system has a good effect in rehabilitation training.

Application and performance of a Low Power Wide Area Sensor Network for distributed remote hydrological measurements

Communication distances of wireless sensor networks (WSNs) are greatly limited in settings where vegetation coverage is moderate or dense, and power consumption can be an issue in remote environmental settings. A newer innovative technology called “Low Power Wide Area Sensor Networks” (LPWAN) is capable of greater communication distances while consuming less power than traditional WSNs. This research evaluates the design and in-field performance of a LPWAN configuration in headwater catchments to measure environmental variables. The performance of the Beta LPWAN deployment indicate reduced signal strength in topographic valleys, but better actual than modelled data transmission performance. System performance during extreme cold temperatures (below – 15 ºC) resulted in increased sensor down time. The configuration of antennae combinations provides the greatest improvement in signal strength and system performance. This technology facilitates remote collection of physically-based, spatially-distributed information within regions with limited accessibility, ultimately advancing data collection capabilities into areas that are not feasible to visit regularly.

E2DNE: Energy Efficient Dynamic Network Embedding in Virtualized Wireless Sensor Networks

Efficient utilization of resources is an important challenge in traditional non-virtualized Wireless Sensor Networks (WSNs), as the applications are embedded in the sensors, precluding the sensor nodes from being re-used by other applications. Inefficient utilization of resources results in high deployment and maintenance costs. Virtualization is a promising technology that allows multiple sensing tasks from diverse applications to be executed on the same deployed WSNs concurrently. However, given that the WSN sensors are constrained with limited energy, the allocations of physical and virtual resources to the applications in an efficient manner becomes a challenge, especially for mission-critical, delay-sensitive applications. In this paper, we address the problem of virtual network embedding in virtualized WSNs, aiming at minimizing the overall energy consumption while considering the end-to-end latency and bandwidth consumption as the service level agreement (SLA) constraints. We propose our Dynamic Network Embedding (DNE) heuristic for large-scale problem instants. The results reveal that our proposed heuristic leads to close-to-optimal solutions with satisfactory execution time. Our results indicate that the proposed heuristic achieves up to an 18% optimality gap in energy consumption in the small-scale scenario, and outperforms the existing benchmark by up to 58% in the large-scale scenario.

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are crucial in various fields, including monitoring the environment, surveillance, and healthcare. They rely on localization services for accurate data transfer and optimal network performance. Traditional WSN techniques struggle to adapt to dynamic environmental changes beyond the intended task scope. A synergy between Software-Defined Networking (SDN) and WSN has been suggested to address this issue. This research paper presents proposed approach for machine learning-based localization in Software Defined Wireless Sensor Networks (SDWSNs) using Particle Swarm Optimization (PSO) technique and the Weighted Dijkstra algorithm. PSO technique is used for clustering, the weighted Dijkstra algorithm (WDA) for finding the shortest path and sending data packets, and machine learning algorithms like AdaBoost and Naïve Bayes for data classification. The effectiveness of the proposed approach is measured using energy consumption, throughput, network lifespan, and packet delivery ratio, outperforming existing models like OEERP, LEACH, DRINA, and BCDCA. The machine learning algorithms' performance is also evaluated, with Naïve Bayes achieving the highest accuracy of 78.24% and AdaBoost 98.90%.

Discrete grey wolf optimization algorithm for solving k-coverage problem in directional sensor networks with network lifetime maximization viewpoint

Unlike the traditional wireless sensor networks (WSNs) which apply omni-directional sensors, the directional sensor networks (DSNs) utilize directional sensor nodes. Each directional sensor in DSN is a self-configured in one direction and one coverage range at a same time. In critical industries, the target coverage problem in which every target should be observed by k different sensors increases the reliability and leads the fault tolerant observation. In this case, the huge amount of energy is consumed in comparison with one coverage problem. Since the business continuity in such industries strongly depends on the network lifetime, the energy management and lifetime maximization of such resource-limited networks are still of the most important challenges. To address the issue, this paper formulates the k-coverage challenge to a discrete optimization problem with the network lifespan expansion viewpoint which is an NP-Hard problem. To solve this combinatorial problem, a discrete grey wolf optimization algorithm (D-GWA) is presented. This D-GWA proposes abundant novel exploration and exploitation procedures which permutes discrete search space efficiently. In this regard, the new fitness function is also defined which steers solutions in the directions to engage sensors uniformly in different rounds. It potentially improves network lifetime because of uniform battery usage. During the course of optimization, it utilizes temperature concept of simulated annealing algorithm to running away from local trap. To verify the proposed algorithm in solving k-coverage problem in DSNs, twelve scenarios are conducted. The simulation results experimentally prove that the totally dominance of the proposed algorithm against state-of-the-arts HTOH, LA-based, GWA, and hybrid PSO-GA approaches respectively are the amount of 31.37%, 20.97%, 14.12%, and 7.93% improvement in term of network lifetime expansion. Furthermore, the behavior of D-GWA shows the high potential of scalability in the larger search space.

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

Characterization of Low-Power Wireless Links in UAV-Assisted Wireless-Sensor Network

The introduction of unmanned aerial vehicle (UAV) makes low-power links face many new challenges in UAV-assisted wireless-sensor network (WSN). In this article, the spatial characteristics of these low-power links are analyzed comprehensively, and the applicability of traditional link quality metrics and models is evaluated. In particular, the differences between link characteristics of UAV-assisted WSN and those of traditional WSN are discussed, and possible reasons for these differences are analyzed. To achieve this goal, two UAVs working on different bands are used and three different flight modes are designed. Experimental results show that link characteristics of UAV-assisted WSN become more complicated due to the 3-D movement of UAV. Link quality fluctuates significantly when the altitude or horizontal distance changes, and it is difficult to demarcate a clear bound between the connected and transitional region. Traditional models for spatial characteristic are only applicable when there is no UAV communication interference and the direction between the air node and ground node remains unchanged. Meanwhile, existing models between physical-layer metrics and packet reception ratio are only applicable when there is no UAV communication interference. For links with the same quality, stability of UAV-assisted WSN is not significantly different from that of traditional WSN. Finally, there is more obvious overall asymmetry between uplinks and downlinks in UAV-assisted WSN, and the quality of downlinks is significantly better than that of uplinks. The discovery of these new features would have great significance on the design of UAV-assisted WSN.

Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks (WSNs) greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery. The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN. However, existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space. Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN, we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage. The Confident Information Coverage (CIC) model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage (CICMTP) problem to minimize the deployed sensor nodes. As the CICMTP is NP-hard, we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC (LGTA-CIC) and Overall Greedy Search Algorithm based on CIC (OGSA-CIC). The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate. Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP, TPNP and EENP algorithms.

Opportunistic coding across on‐route dominating nodes in a flow‐oriented SDN enabled WSN

SummaryWith the aid of network coding, numerous packets can be mixed at intermediary nodes if any encoding opportunity exists. However, it is challenging to choose intermediate relay nodes of probable opportunistic coding in distributed wireless networks. In this work, a wireless sensor network (WSN) is employed with a software defined network (SDN) architecture along with a connected dominating set (CDS)‐based routing technique. The SDN offers a centrally managed architecture that lowers energy usage and network traffic by reducing the controlling and managing overheads in sensory networks. In addition, the CDS‐based routing and the potential for opportunistic coding at dominant nodes are coupled to enhance network performance. The proposed CDS‐based flow‐oriented coding‐aware energy‐efficient routing (CFCER) is contrasted with existing traditional wireless sensor networks and software‐defined sensor networks in terms of throughput, packet delay, and energy usage. CFCER increases network throughput by 15% more than SDN‐based WSN and by about 25% more than traditional WSN. However, compared to standard WSN, a significant improvement was made in packet delay and energy conservation. Over SDN‐WSN, packet delay is reduced by about 18%, and the energy consumption is cut by 10%.

Autonomous Traffic-Aware Scheduling for Industrial Wireless Sensor-Actuator Networks

Recent years have witnessed rapid adoption of low-power Wireless Sensor-Actuator Networks (WSANs) in process industries. To meet the critical demand for reliable and real-time communication in harsh industrial environments, the industrial WSAN standards make a set of specific design choices, such as employing the Time-Slotted Channel Hopping (TSCH) technique. Such design choices distinguish industrial WSANs from traditional Wireless Sensor Networks, which were designed for best-effort services. Recently, there has been increasing interest in developing new methods to enable autonomous transmission scheduling for industrial WSANs that run TSCH and the Routing Protocol for Low-Power and Lossy Networks (RPL). Our study shows that the current approaches fail to consider the traffic loads of different devices when assigning time slots and channels, which significantly compromises network performance when facing high data rates. In this article, we introduce a novel Autonomous Traffic-Aware transmission scheduling method for industrial WSANs. The device that runs ATRIA can detect its traffic load based on its local routing information and then schedule its transmissions accordingly without the need to exchange information with neighboring devices. Experimental results show that ATRIA provides significantly higher end-to-end network reliability and lower end-to-end latency without introducing additional overhead compared with a state-of-the-art baseline.

Towards an Efficient Fog-Based Forest Fire Management Architecture

Forest fires cause significant human losses and natural damage and threaten the biodiversity on our planet. Several researchers have studied the potential of wireless sensor networks (WSNs) to manage a forested area and have proven the real potential of this paradigm. Such systems generate a large amount of heterogeneous data in a short time, which may lead to network congestion and delay in data transmission. Processing such data in a short time frame is challenging for a traditional WSN. This article presents a multi-layered architecture for wildfire prevention, detection, and intervention based on WSN, IoT, cloud, and fog computing technologies. The proposed architecture is validated using the iFogSim simulation tool. Two models are compared, cloud-only and fog-cloud-based scenarios. The experimental results show that the cloud-fog model minimizes latency and significantly reduces bandwidth consumption compared to the cloud-only model. To bring fast fire extinguishing ability to the system, the authors recommend the employment of drones equipped with fire extinguishing balls.

Energy-efficient adaptive sensing for Cognitive Radio Sensor Network in the presence of Primary User Emulation Attack

A Cognitive Radio Sensor Network (CRSN) is a promising technology incorporating cognitive radio in traditional wireless sensor networks. In general, Primary User Emulation Attack (PUEA) is becoming the major bottleneck for implementing CRSN in reality. In CRSN-PUEA, malicious users are also equipped with a Spectrum Sensing capability, the so-called smart PEUA, which further complicates the spectrum access of Cognitive Users (CU). In this paper, we propose a novel Energy-Efficient Adaptive Sensing (EEAS) technique for CRSN to overcome the spectrum access problem in the presence of PUEA. The proposed EEAS enhances the sensing accuracy by differentiating the PUEA and Primary User signals that, reduce the sensing error probability and enhance the underlying system model’s throughput and energy efficiency. The numerical results show that the proposed EEAS technique has improved the throughput and energy efficiency by about 12% and 44%, respectively, compared to the state-of-the-art works in the field of PUEA-CRSN. In addition, it is observed that the sensing error probability is reduced by 22%. Furthermore, a Software Defined Radio (SDR) based hardware system setup has been developed to verify the functionality of the proposed system model.

BRATRA: Balanced Routing Algorithm With Transmission Range Adjustment for Energy Efficiency and Utilization Balance in WSNs

In traditional wireless sensor networks (WSNs), packets are mainly transmitted in a multihop routing manner. The multihop transmission, however, leads to a hotspot problem in the sink connectivity area (SCA), and the overall network efficiency is reduced due to the quick battery power exhaustion of nodes in that area. This article proposes a novel balanced routing algorithm with transmission range adjustment (BRATRA) to address the network efficiency problem, including the energy efficiency and utilization issues. First, a balanced routing strategy is designed to deal with the SCA load imbalance problem. With the shortest balanced path, the amounts of forwarding packets for the nodes in the SCA and all the other intralayers become more even. From the perspective of power equilibrium in each routing path, each node then determines its accurate transmission radius according to the derived formula and performs power control to realize the even power utilization between interlayers, thereby prolonging the overall network lifetime. Performance evaluation validates that the proposed BRATRA strategy can achieve efficient power utilization in each intralayer and double the network lifetime as compared to the Dijkstra routing strategy. Additionally, it yields better power utilization fairness among nodes, and on average only 5% of battery power is unused for all network nodes, resulting in a network lifespan ten times larger than that using a conventional strategy.

Design of High-Performance Intelligent WSN based-IoT using Time Synchronized Channel Hopping and Spatial Correlation Model

Wireless Sensor Network (WSN) is one of the most significant contributors to the Internet of Things (IoT), and it plays a significant role in the lives of individuals. There are three main problems in the design of traditional WSN based-IoT. First problem about data; the WSN transmits a huge volume of data to the IoT for processing. The second problem is the energy; since sensor nodes rely on their limited battery, conserving energy is crucial, and the third problem about efficiency of transmission. This paper presents new WSN based IoT framework that integrate important techniques to solve these problems; To increase the effectiveness of data processing and storing, the intelligent Adaptive Boosting stochastic algorithm is applied. IEEE 802.15.4e time slotted channel hopping (TSCH) protocol is used because it has the benefits such as collision-free transmission and multi-hop transmission. Data reduction at the Gateway (GW) level of the network is achieved through spatial correlation between sensors with the goal of conserving energy. Principle idea of this new framework is to identify the advantages of integrating the important techniques; intelligent Adaptive Boosting Stochastic diffusion search algorithm, TSCH, and Special correlation model. As a result, the proposed framework can thereby satisfy the need for a long battery life of low-rate applications and at the same time, the need for high throughput for high-rate uses also for testing it in achieved efficient classification of data, the important performance measures are used.