Cluster-based Wireless Sensor Networks Research Articles

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

Predictive Model Techniques with Energy Efficiency for IoT-Based Data Transmission in Wireless Sensor Networks

Wireless sensor networks are limited by the vast majority of goods with limited resources. Power consumption, network longevity, throughput, routing, and network security are only a few of the research issues that have not yet been addressed in sensor networks based on the Internet of Things. Prior to becoming widely deployed, sensor networks built on the Internet of Things must overcome a variety of technological obstacles as well as general and specific hazards. In order to address the aforementioned problems, this research sought to improve rogue node detection, reduce packet latency/packet loss, increase throughput, and lengthen network lifetime. Wireless energy harvesting is suggested in the proposed three-layer cluster-based wireless sensor network routing protocol to extend the energy lifespan of the network. For the purpose of recognising and blacklisting risky sensor node behaviour, a three-tier clustering architecture with an integrated security mechanism is suggested. This clustering approach is cost-based, and the sink node selects the cluster and grid heads based on the cost function’s value. With its seemingly endless potential across a wide range of industries, including intelligent transportation, the Internet of Things (IoT) has gained prominence recently. To analyse the nodes and clustering strategies in IoT, the suggested method PSO is applied. A plethora of new services, programmes, electrical devices with integrated sensors, and protocols have been produced as a result of the Internet of Things’ explosive growth in popularity.

Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques

Energy consumption, secure connection of Sensor Nodes (SNs), and their performance analysis play a major role in Wireless Sensor Networks (WSNs) research. Increased case use of various Internet of Things (IoT) applications has led to the development of complicated networks. To address the threats and security issues faced in complicated WSNs, a novel combined feature selection method known as the Fast Correlation-based Feature Selection (FCBFS) with XG-Boost has been proposed for the NSL-KDD intrusion detection benchmark dataset. It helps in the selection of the best features in a cluster-based WSN before the classification step. Five popular machine learning-based classifiers, namely decision tree, random forest, Naïve Bayes, extra tree, and XG-Boost, are used for the development of a robust intrusion detection system in WSN and its IoT applications. The effectiveness and robustness of the developed ensemble methods have been checked using classification accuracy, precision, recall, and F-score. XG-Boost classifier along with the FCBFS process has performed best with a classic accuracy, precision, recall, and F-score up to 99.84%, 99.83%, 99.84%, and 99.82% on multiple runs. Upon comparison with the existing state-of-the-art work in this field, the proposed work has outperformed. Results show that, in contrast to the previous filter approaches, our proposed process can successfully minimize the number of features while maintaining a high classification precision and recognition rate. It further tends to lower the overall energy required by sensor nodes during attack detection, hence extending the network lifetime and usefulness to a sufficient time frame.

Residue Number System-Based Approach to Minimise Energy Consumption in Wireless Sensor Networks

This study harnesses the useful number properties of the residue number system (RNS) to minimise energy consumption in a wireless sensor network. In a traditional cluster-based wireless sensor network, large bit representations of aggregated packets are transmitted to the base station. However, large bit patterns of packets are slower compared to smaller bits. The proposed approach splits aggregated data into a pre-specified number of transmission channels using a moduli set. Cheap energy cost routes from the cluster heads are computed to deliver the chunked aggregated data to the base station. Forward and reverse converters are proposed to encode data into RNS and decode the RNS data that reaches the base station. MATLAB simulation is used to implement the proposed data splitting method and to evaluate network performance. The experimental results suggest that the proposed method is more effective at minimising transmission energy when compared with traditional approaches in which complete packets are transmitted.

A Formal Energy Consumption Analysis to Secure Cluster-Based WSN: A Case Study of Multi-Hop Clustering Algorithm Based on Spectral Classification Using Lightweight Blockchain.

Wireless Sensors Networks are integrating human daily life at a fast rate. Applications cover a wide range of fields, including home security, agriculture, climate change, fire prevention, and so on and so forth. If WSN were initially flat networks, hierarchical, or cluster-based networks have been introduced in order to achieve a better performance in terms of energy efficiency, topology management, delay minimization, load balancing, routing techniques, etc. As cluster-based algorithms proved to be efficient in terms of energy balancing, security has been of less importance in the field. Data shared by nodes in a WSN can be very sensitive depending on the field of application. Therefore, it is important to ensure security at various levels of WSN. This paper proposes a formal modeling of the energy consumed to secure communications in a cluster-based WSN in general. The concept is implemented using the Proof of Authentication (POAh) paradigm of blockchain and applied to a Multi-hop Clustering Algorithm based on spectral classification. The studied metrics are residual energy in the network, the number of alive nodes, first and last dead node.

Delay-aware relay node selection for cluster-based wireless sensor networks

Maximizing the network lifetime through balancing energy and lowering the delay is a primary design goal of wireless sensor networks (WSNs). In WSNs, the sensor nodes task is to acquire the data from the area or point of interest and transmit it to the sink node via relay nodes timely. Relay nodes must dissipate some energy to perform this task. So, the relay nodes and cluster heads face the challenge of energy balancing. Hence, choosing the optimal cluster heads and relay nodes is necessary to balance the energy among the sensor nodes. In this context, this paper presents a novel clustering algorithm to balance energy by considering different metrics of WSNs, including distance, energy, link lifetime, and delay characteristics—using a relay node, etc.In this paper, a Hybrid Heuristic Data Aggregation (HHDAP) Protocol is used by combining particle swarm optimization (PSO) and improved Ant colony optimization (ACO).Through this HHDAP algorithm, the relay nodes are chosen based on a sensor node's distance, energy, link lifetime, and delay metrics. This work is simulated using NS2, and the performance of proposed and existing works are evaluated. The results show the performance improvements of the proposed work over the existing algorithms in terms of different quality of service metrics.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task, the complex task should be decomposed into sub-computation tasks that run parallel in edge computing. Wireless Sensor Network (WSN) is a typical application of parallel computation. To achieve highly reliable parallel computation for wireless sensor network, the network's lifetime needs to be extended. Therefore, a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network. This paper proposes a task model and a cluster-based WSN model in edge computing. In our model, different tasks require different types of resources and different sensors provide different types of resources, so our model is heterogeneous, which makes the model more practical. Then we propose a task allocation algorithm that combines the Genetic Algorithm (GA) and the Ant Colony Optimization (ACO) algorithm. The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended. The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

The Internet of Things (IoT) makes it conceivable to communicate among distinctive things. The use of IoT in the farming industry is critical for increasing utility. Smart agricultural practices may boost crop yield while also creating more output with the same amount of input. The majority of farmers, however, are still unaware of the most recent technologies and procedures. In this study, a revolutionary wireless mobile robot based on the Internet of Things (IoT) is created and installed to perform a variety of outdoor tasks. The benefits of this work include more accurate and efficient data, as well as a reduction in manpower. This research has applications in agriculture, arrival, and water division. Keen agrarian frameworks have been built up in different parts of the world utilising the Internet of Things (IoT) and remote sensor systems. One of the branches that springs to intellect in this respect is exactness cultivating. Numerous analysts have made checking and robotization frameworks for different cultivating capacities. Information collection and transmission between IoT gadgets set in ranches will be basic utilising WSN. The Kalman Filter (KF) is used with expectation investigation within the proposed method to get high-quality information free of commotion and exchange it with cluster-based WSNs. The quality of information utilised for examination is progressed as a result of this strategy, and information transport overhead within the wireless sensor network application is decreased. A decision tree is used for forecast analytics decision making for trim surrender expectation, trim classification, soil classification, climate expectation, and trim malady expectation. IoT components integrated with IoT cloud are coordinates in proposed framework to supply keen arrangement for edit development observing to clients.

EECH/CF: An Energy-efficient Cluster Head Election and Cluster Formation Algorithms for WSNs

Background and Objective: Wireless sensor networks (WSN) consist of sensor nodes with a limited battery life and limited communication distance. The minimal energy expended by the sensor nodes and network can be achieved either by reducing the number of communications or by controlling the topology. Thus, energy consumption can be optimized by employing several techniques, such as clustering. Clustering allows the network to be divided into a set of clusters, each of which is managed by a cluster head. In a hierarchical cluster-based WSN, the cluster head receives the data from its sensor members, aggregates it and sends the result to the base station, which leads to an extra overload. So, the selection of appropriate cluster head plays a very important role in conserving the energy of the sensor nodes and extend the lifetime of the network. This paper introduces an energyefficient cluster head election and cluster formation algorithm for WSNs, called EECH/CF. To select cluster heads, our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Methods: Our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Results and Discussion: Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Conclusion: Results show that our clustering algorithms improve the lifetime of the network as we expected. Indeed, the delay before the death of the first node can be up to seven times longer with EECH/CF.

Ferry–Based Directional Forwarding Mechanism for Improved Network Life-Time in Cluster-Based Wireless Sensor Network

Considerable energy saving can be achieved with mobility-based wireless sensor networks (WSN's), where a mobile node (ferry) visits sensing nodes in a network to collect sensed data. However, the critical issues of such WSN's are limited networks lifetime and high data latency, these critical issues are due to the slow mobility and relatively long route distance for ferries to collect and forward data to the sink. Incorporating ferries in WSNs eliminates the need for multi-hop forwarding of data, and as a result, reduce energy consumption at sensing nodes. In this paper, we introduce the One Hop Cluster-Head Algorithm (OHCH), where a subset of ferries serve as cluster heads (CH), travel between nodes with short distance mobility, collect data originated from sources, and transfer it to the sink with minimum hop count possible, this approach can achieve more balance between network energy saving and data collection delay, also, it is an efficient design to combine between ferries and noise.

An algorithm for enhancing coverage and network lifetime in cluster-based Wireless Sensor Networks

Majority of wireless sensor networks (WSNs) clustering protocols in literature have focused on extending network lifetime and little attention has been paid to the coverage preservation as one of the QoS requirements along with network lifetime. In this paper, an algorithm is proposed to be integrated with clustering protocols to improve network lifetime as well as preserve network coverage in heterogeneous wireless sensor networks (HWSNs) where sensor nodes can have different sensing radii and energy attributes. The proposed algorithm works in proactive way to preserve network coverage and extend network lifetime by efficiently leveraging mobility to optimize the average coverage rate using only the nodes that are already deployed in the network. Simulations are conducted to validate the proposed algorithm by showing improvement in network lifetime and enhanced full coverage time with less energy consumption

Secure Routing Optimization in Hierarchical Cluster-Based Wireless Sensor Networks

Popularity of wireless sensor networks (WSNs) is increasing continuously in different domains of daily life, as they provide efficient method of collecting valuable data from the surroundings for use in different applications. Routing in WSNs is the vital functionality that allows the flow of information generated by sensor nodes to the base station, while considering the severe energy constraint and the limitations of computational and storage resources. Indeed, this functionality may be vulnerable and must be in itself secured, since conventional routing protocols in WSNs provide efficient routing techniques with low power consumption, but they do not take into account the possible attacks. As sensor nodes may be easily captured and compromised, the classical cryptographic solutions become insufficient to provide optimal routing security, especially, for cluster-based WSNs, where cluster heads can be still among the compromised nodes. In this work, we propose a hierarchical, robust and well-adapted intrusion detection system, named THIDS, which is intended to be integrated into the secure hierarchical cluster-based routing protocols. We have chosen the protocol RLEACH to be equipped with the proposed IDS. The results of simulation performed under NS2 simulator show that the resulting protocol ORLEACH is much more resistant to compromised nodes exercising the most dangerous attacks.

A Hybrid Routing Protocol Based on Naïve Bayes and Improved Particle Swarm Optimization Algorithms

Clustering of sensor nodes is a prominent method applied to wireless sensor networks (WSNs). In a cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes also have limited battery power. Therefore, energy efficiency in WSNs is crucial. The load on the sensor node and its distance from the base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSNs. In this paper, we propose a hybrid routing algorithm based on Naïve Bayes and improved particle swarm optimization algorithms (HRA-NP). The cluster heads (CHs) are selected according to the CH conditional probability, which is estimated by the Naïve Bayes classifier. After the selection of the CHs, the multi-hop routing algorithm is applied to the CHs. The best routing path from each CH to the BS is obtained from an improved particle swarm optimization (PSO) algorithm. Simulations were conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network, and the standard deviation of a load on the sensor node. It was observed that HRA-NP outperforms comparable algorithms, namely DUCF, ECRRS, and FC-RBAT, based on the evaluation factors.

Sensor device scheduling-based cuckoo algorithm for enhancing lifetime of cluster-based wireless sensor networks

Sensor device scheduling-based cuckoo algorithm for enhancing lifetime of cluster-based wireless sensor networks

Sensor device scheduling-based cuckoo algorithm for enhancing lifetime of cluster-based wireless sensor networks

Sensor device scheduling-based cuckoo algorithm for enhancing lifetime of cluster-based wireless sensor networks

Impact of Aggregation and Compression on Cluster-Based Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), a clustered architecture is generally used to reduce energy consumption irrespectively of the application of the system. We prove in this work that, a clustered network only reduces energy consumption if aggregation or compression functions are enabled. Further- more, a clustered network would consume much more energy if clustering techniques without the use of aggregation/compression (or a low aggregation coefficient) due to the extra consumption in the cluster formation phase. As an additional feature, a general energy consumption model based on the notion of energy units is developed that can be easily extrapolated to either theoretical or experimental values. Hence, the proposed analytical framework is valid for any commercial node or energy consumption model.

EEHCHR: Energy Efficient Hybrid Clustering and Hierarchical Routing for Wireless Sensor Networks

In cluster-based Wireless Sensor Network (WSN), the Cluster Heads (CHs) consume massive amount of energy due to uneven processing and routing of the information of their Cluster Members (CMs) to the Base Station (BS), and results in lower network lifetime e.g., REHR (Residual Energy based Hybrid Routing). Also, the centralized clustering algorithms introduce unnecessary consumption of energy e.g., FFTHR (Fitness Function based Two-Hop Routing) and UCRA-GSO (Uneven Clustering Routing Algorithm using Glowworm Swarm Optimization). Thus, for extending the network lifetime, we introduce a new clustering algorithm named as Energy Efficient Hybrid Clustering and Hierarchical Routing (EEHCHR) in WSN. Here, a new scheme of adaptive and hybrid clustering has been proposed for minimum usage of the node’s energy using the Euclidean distance parameter, Fuzzy C-Means (FCM) technique, location of BS, and residual energy of the nodes. Here, the clustering is performed only in a few rounds, and this results in reduction of energy consumption of the network. All the CHs are selected using the energy efficient fitness function, which works in an adaptive way with the residual energy of the nodes to improve the CH selection process. For efficient usage of the network’s energy, we have also given a hierarchical packet routing strategy by introducing the concept of DCH (Direct Cluster Head) and CCH (Central Cluster Head), which are selected by different fitness functions, and work as a relay for few other CHs. The simulation results of EEHCHR proved that it extends the network lifetime, coverage, and saves the network energy as compared to similar existing algorithms e.g., FCM, REHR, FFTHR, UCRA-GSO and CCA-GWO.

Enhanced shuffled frog leaping algorithm with improved local exploration and energy-biased load reduction phase for load balancing of gateways in WSNs

AbstractWireless sensor networks (WSNs) have grown widely due to their application in various domains, such as surveillance, healthcare, telecommunication, etc. In WSNs, there is a necessity to design energy-efficient algorithms for different purposes. Load balancing of gateways in cluster-based WSNs is necessary to maximize the lifetime of a network. Shuffled frog leaping algorithm (SFLA) is a popular heuristic algorithm that incorporates a deterministic approach. Performance of any heuristic algorithm depends on its exploration and exploitation capability. The main contribution of this article is an enhanced SFLA with improved local search capability. Three strategies are tested to enhance the local search capability of SFLA to improve the load balancing of gateways in WSNs. The first proposed approach is deterministic in which the participation of the global best solution in information exchange is increased. The next two variations reduces the deterministic approach in the local search component of SFLA by introducing probability-based selection of frogs for information exchange. All three strategies improved the success of local search. Second contribution of article is increased lifetime of gateways in WSNs with a novel energy-biased load reduction phase introduced after the information exchange step. The proposed algorithm is tested with 15 datasets of varying areas of deployment, number of sensors and number of gateways. Proposed ESFLA-RW variation shows significant improvement over other variations in terms of successful local explorations, best fitness values, average fitness values and convergence rate for all datasets. Obtained results of proposed ESFLA-RW are significantly better in terms of network energy consumption, load balancing, first gateway die and network life. The proposed variations are tested to check the effect of various algorithm-specific parameters namely frog population size, probability of information exchange and probability of energy-biased load reduction phase. Higher population size and probabilities give better solutions and convergence rate.

Malware propagation model for cluster-based wireless sensor networks using epidemiological theory.

Due to limited resources, wireless sensor network (WSN) nodes generally possess weak defense capabilities and are often the target of malware attacks. Attackers can capture or infect specific sensor nodes and propagate malware to other sensor nodes in WSNs through node communication. This can eventually infect an entire network system and even cause paralysis. Based on epidemiological theory, the present study proposes a malware propagation model suitable for cluster-based WSNs to analyze the propagation dynamic of malware. The model focuses on the data-transmission characteristics between different nodes in a cluster-based network and considers the actual application parameters of WSNs, such as node communication radius, node distributed density, and node death rate. In addition, an attack and defense game between malware and defending systems is also established, and the infection and recovery rates of malware propagation under the mixed strategy Nash equilibrium condition are given. In particular, the basic reproductive number, equilibrium point, and stability of the model are derived. These studies revealed that a basic reproductive number of less than 1 leads to eventual disappearance of malware, which provides significant insight into the design of defense strategies against malware threats. Numerical experiments were conducted to validate the theory proposed, and the influence of WSN parameters on malware propagation was examined.

CCIC-WSN: An Architecture for Single-Channel Cluster-Based Information-Centric Wireless Sensor Networks

The promising vision of information-centric networking (ICN) and of its realization, named data networking (NDN), has attracted extensive attention in recent years in the context of the Internet of Things (IoT) and wireless sensor networks (WSNs). However, a comprehensive NDN/ICN-based architectural design for WSNs, including specially tailored naming schemes and forwarding mechanisms, has yet to be explored. In this article, we present single-channel cluster-based information-centric WSN (CCIC-WSN), an NDN/ICN-based framework to fulfill the requirements of cluster-based WSNs, such as communication between child nodes and cluster heads (CHs), association of new child nodes with CHs, discovery of the namespace of newly associated nodes, and child node mobility. Through an extensive simulation study, we demonstrate that CCIC-WSN achieves 71%–90% lower energy consumption and 74%–96% lower data retrieval delays than recently proposed frameworks for NDN/ICN-based WSNs under various evaluation settings.