Node Death Research Articles

Reinforcement learning-based dynamic routing using mobile sink for data collection in WSNs and IoT applications

Energy is one of the most critical resources for sensor devices that decides the network lifetime of the wireless sensor networks. In many circumstances, sensor devices consume more energy for data transmission, reception, and forwarding operations. The major challenge is to increase the network lifetime by implementing the latest research models to reduce the deployment and operational cost. Many existing methods address the application of the static sink with multi-hop routing. But most of them suffer from energy-hole issues and inefficient data collection due to the early death of sensor nodes. Most of the existing methods of learning require massive data with feature engineering which eventually increases the learning complexity. In order to avoid these issues, a robust reinforcement learning-based mobile sink model is proposed for dynamic routing with efficient data collection. In addition, the Q-Learning approach is implemented to induce automatic learning through the shortest route. Combining these strategies preserves network stability and efficiently improves the routing performance as well as the reward. The simulation results reveal that the proposed reinforcement learning-based mobile sink model extends the network lifetime, provides an improved learning time with more reward, and results in high efficiency when compared with existing methods.

Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol

A Flying Ad-hoc network constitutes many sensor nodes with limited processing speed and storage capacity as they institute a minor battery-driven device with a limited quantity of energy. One of the primary roles of the sensor node is to store and transmit the collected information to the base station (BS). Thus, the life span of the network is the main criterion for the efficient design of the FANETS Network, as sensor nodes always have limited resources. In this paper, we present a methodology of an energy-efficient clustering algorithm for collecting and transmitting data based on the Optimized Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. The selection of CH is grounded on the new optimized threshold function. In contrast, LEACH is a hierarchical routing protocol that randomly selects cluster head nodes in a loop and results in an increased cluster headcount, but also causes more rapid power consumption. Thus, we have to circumvent these limitations by improving the LEACH Protocol. Our proposed algorithm diminishes the energy usage for data transmission in the routing protocol, and the network’s lifetime is enhanced as it also maximizes the residual energy of nodes. The experimental results performed on MATLAB yield better performance than the existing LEACH and Centralized Low-Energy Adaptive Clustering Hierarchy Protocol in terms of energy efficiency per unit node and the packet delivery ratio with less energy utilization. In addition, the First Node Death (FND) is also meliorated when compared to the LEACH and LEACH-C protocols.

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.

An optimal communication in WSN enabled by fuzzy clustering and improved meta-heuristic model

PurposeWireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.Design/methodology/approachThis paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.FindingsThe proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.Originality/valueFor the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.

Novel Authentication and Secure Trust based RPL Routing in Mobile sink supported Internet of Things

ABSTRACT In the modern era, prevalence of the Internet of Things (IoT) devices that have de facto protocol as IPv6 routing protocol for low power and lossy networks (RPL). Yet, RPL protocol is vulnerable to many attacks such as rank attack, password spoofing and more. To this end, most of the works have focused their research on securing the RPL-based IoT network. However, still there exist downsides such as high energy consumption, lack of effective authentication and high packet losses. Motivated by these preceding defects, this paper proposes the Novel Authentication and Secure Trust-based RPL Routing in Mobile sink-supported Internet of Things (SecRPL-MS). At first, SecRPL-MS performs a registration process where all IoT nodes in the network register themselves in the security entity. In this work, the frequent death of IoT nodes is alleviated through deploying mobile sink in the network. If any grid member (GM) node wants to transmit their data to the grid head (GH) node, then it must undergo authentication process. Secure routing is adopted in RPL by utilising the sail fish optimisation algorithm. Each GM node encrypts its sensed data using the prince algorithm before transmitting it to the GH node. The moving points are selected for the mobile sink using the Quantum Inspired Neural Network (QINN) algorithm. This proposed SecRPL-MS performance is evaluated using the Network Simulator 3 (NS3) in terms of the Packet Delivery Ratio (%), Delay (ms), Energy Consumption (mJ), Key Generation Time (ms) and Malicious Node Detection Accuracy (%). The proposed SecRPL-Ms outperforms 23% of malicious node detection accuracy when compared to existing systems, which represent the proposed SecRPL-MS system providing high security by mitigating the following attacks such as rank attack, Sybil attack, blackhole attack and man in the middle attack.

An Implementation of a Multi-Hop Underwater Wireless Sensor Network using Bowtie Antenna

Water quality is a growing area of research, with more and more focus in the UK and globally on environmental issues and water quality. Current methods of monitoring environmental data such as air quality have continued to develop, spurred on by the growth of the Internet of Things. However, water quality monitoring mainly still depends on manual sample collection. This research presents the first implementation of a multi-hop underwater radio frequency sensor network using bowtie antennas combined with the 433 MHz frequency and a controlled flooding routing approach. The experimental work was conducted in the water reservoir and demonstrates the potential of multi-hop routing in underwater sensor networks to extend range to 19 meters as well as improvements on communication distances from 7 meters previously to 17 meters using radio frequency communications in an underwater environment. Simulated results show that the experimental platform used could enable the long-term deployment of an underwater wireless sensor network that used RF for periods of over a year with support for a 100 sensor node network broadcasting twice daily remaining active for 418 days or a 100 sensor node network broadcasting hourly remaining active for 406 days before any node deaths.

Energy-Efficient Cluster-Based Routing Protocol for WSN Based on Hybrid BSO–TLBO Optimization Model

Abstract The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method, which utilizes the brainstorm algorithm in order to adopt the ideal cluster head (CH) to reduce energy draining. Furthermore, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the modified teacher–learner optimized (MTLBO) algorithm with it. The modified BSO–MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inferred that our approach performs better than all the other approaches.

ETH‐LEACH: An energy enhanced threshold routing protocol for WSNs

SummaryMany wireless sensor‐based applications use LEACH as a preferred routing protocol owing to its unique features such as optimal sleeping time, minimum packet collisions, dynamic channel selection, and least power consumption. The traditional LEACH protocol wastes the transmission opportunities as it processes data only in an event occurring, leading to wastage of resources. To resolve this issue, this paper proposes a more robust Energy Enhanced Threshold Routing Protocol (ETH‐LEACH) for WSNs. The working of ETH‐LEACH is conceptualized in two parts. In the first part, TDMA is implemented to estimate the opportunistic paths to remove network overhead. Furthermore, in the second part, a threshold value is calculated for choosing the forwarder nodes. The proposed technique minimizes the energy usage of the sensor nodes and consequently enhances the network's lifetime by extending the duration of node death. The ETH‐LEACH protocol is contrasted with the different variants of LEACH to verify its effectiveness. The experimental results show that the proposed ETH‐LEACH protocol outperforms the traditional routing protocols. In this paper, the ETH‐LEACH protocol performs nearly 54.6% efficient than LEACH, 47.6% efficient than Q‐LEACH, 41.3% efficient than NR‐LEACH, 33.6% efficient than LEACH‐GA, and 29.7% efficient than LEACH‐POS in reducing the usage of energy in the overall simulation.

FFMCP: Feed-Forward Multi-Clustering Protocol Using Fuzzy Logic for Wireless Sensor Networks (WSNs)

The restriction on the battery life of sensors is a bottleneck for wireless sensor networks (WSNs). This paper proposes a new feed-forward multi-clustering protocol (FFMCP) to boost the network lifetime. The utilization of fuzzy logic helps to overcome the uncertainties in the value of input parameters. The proposed protocol selects the most suitable cluster heads (CHs) using the multi-clustering method. A multi-clustering technique is defined utilizing the node’s information of the previous round and a fuzzy inference system to decide the CHs. The sensor nodes spend energy due to non-uniform CH distribution and long-distance data transmission by member nodes. The main focus of the proposed protocol is to reduce the member node distance. Our proposal distributes CH nodes uniformly using unequal clustering. The simulation outcome reveals that the proposed algorithm(FFMCP) has better performance in terms of tenth node death (TND), half node death (HND), remaining energy after 800 rounds (E_800), and average energy spent per round (AVG_PR) as compared to standard clustering schemes in the past.

Delay-Sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware (DREAM) Routing Protocol for WSNs

A majority of Wireless Sensor Network (WSN) research at present is focussed on the problems of limited energy supply and its impact on network lifetime. Nevertheless, the plethora of applications conceivable with the help of WSNs often demand for MOO (Multi-Objective Optimization) formulations, where several design goals contend together for the best trade-off solution among them. Therefore, research investigators must also regard other miscellaneous issues in addition to energy efficiency for applicability of WSNs in practical scenarios like Internet of Things. DREAM (Delay-sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware) routing protocol is proposed in the present work, that ameliorates network lifetime (in terms of First Node Death and Last Node Death), throughput (in terms of number of packets sent to Base Station) and latency (average end-to-end delay in seconds) in the network along with enhancing the reliability (in terms of percentage packet loss) of delivered data. The proposed protocol also integrates mobility and heterogeneity of the nodes to cater to the needs of an application-independent general purpose WSN routing protocol, which can be used commercially. Comparative analysis with existing protocols establishes the superiority of the proposed protocol, which is capable of improving the network lifetime by about 3.54% and simultaneously lowering the delay by 35.5%, along with the amelioration of other parameters.

An Experimental Investigation of Premature Death of Sensor Node used in Io Tat Colder Environmental Conditions with Strategies to Mitigate the Same

The Sensor Node Is A Vital Component Of Iot Technologies. The Main Objective Of This Paper Is To Study Theimpactofthesensorfieldenvironmentonthelifetimeofthebatteryofthesensornode.Specifically, Temperature, One Of The Environmental Parameters, Is Chosen For The Experimental Studies Undertaken In This Work. The Lifetime And The Power Consumed By The Sensor Node Are Analyzed At Different Temperatures. The Reasons For The Early Death Of The Battery Of The Sensor Node At Lower Temperatures Are Identified. Some Strategies Are Proposed To Overcome The Same. Experimental Results Have Shown The Life Of The Sensor Node Is 18% More When The Proposed Techniques Are Used.

New Topology Control base on Ant Colony Algorithm in Optimization of Wireless Sensor Network

Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster, sorting all within header, and selecting an active one (more power header) for signal routing. While sensor cover topology demonstrates network monitoring capability, connection topology should remain as a requirement for the successful delivery of information including queries, data collected, and control messages. How to build an optimized coating topology while remaining efficient and low-cost connection is not well understood and needs further research. Power control and power management are two different types of topology controllers. Also in our study, we examine network lifetime, compared to other schemas time of death of the first node and the last node, and found that network lifetime was increased. Finally, a topology control method for extending network lifetime is presented.

Greedy Load Balancing Energy Efficient Routing Scheme for Wireless Sensor Networks

Background: Despite so many constraints, the limited battery power of the sensor nodes is the core issue in Wireless Sensor Networks. This compels how to extend the lifetime of the network as long as possible. One of the ways to solve the problem is to balance the relay traffic load to extend the lifetime. Objective: In this paper, a load balancing algorithm is suggested that selects the best possible relay node so that uniform consumption of the battery power of the sensor nodes can be ensured. Methods: After random deployment, sensor nodes collect information about their neighbors and their expected load. The selection of new next hop starts from maximum hop count. Next hop of the nodes having a single parent is set first. Remaining nodes select their next hop in the non-increasing order of their load. Results: Simulation results verify that packet delivery ratio for proposed work is up to 50% till 72% of total time duration and no nodes getting dead till 48% of total time duration, while for others, nodes start getting dead around 36% of total time duration. Also, it is proved that the solution obtained by proposed work can be at most 1.5 times imbalanced as compared to the optimal solution which implies our solution is quite near to the optimal one. Conclusion: Load balancing done in our work has shown more positive results in comparison to others in terms of network lifetime and first node death and which is also verified with F-test with α- value to be 0.05.

A Node Overhaul Scheme for Energy Efficient Clustering in Wireless Sensor Networks

Clustering of wireless sensor network nodes, a fundamental operation, is aimed at achieving load balancing and prolonged network lifetime. Low-energy adaptive clustering hierarchy protocol, the prominent standard, achieves these. An improved protocol, balance cluster formation, provides the additional advantage of equal size clusters, but at the cost of overlapping of clusters. This letter presents a node overhaul scheme that achieves load balancing and energy efficiency while also maintaining uniform size clusters without any overlapping. The proposed solution first forms initial clusters and later refurbishes the initial clusters based on a second best choice cluster head, wherever applicable. The results so obtained show a substantial improvement in network lifetime and node death rate as compared to other simulated methods.

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft- $k$ -means (IS- $k$ -means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of clustering by fast search and find of density peaks (CFSFDPs) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft $k$ -means clustering algorithm. Then, we utilize the flexibility of the soft- $k$ -means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multicluster heads is employed to balance the energy consumption within clusters. Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.

Cluster Formation using Fuzzy Logic in Wireless Sensor Networks

The biggest challenges faced by wireless sensor networks (WSNs) are the network lifetime and consumption of energy. To reduce the amount of energy used by WSNs, high quality clustering proves to be a crucial approach. There are multiple criteria that need to be evaluated depending on the cluster’s quality and incorporating all these criteria will prove to be cumbersome process, leading to high-quality clustering. Hence, in this paper we propose an algorithm that is used to produce high quality clusters. Cluster quality is set as the deciding criterion to determine the quality of the clusters thereby categorizing them as intra- and inter-clusters based on their distances to eliminate error rate. Using fuzzy logic, the optimal cluster head is chosen. Similarly, based on the maximum and minimum distance between the nodes, the maximum and minimum energy present in every cluster is determined. The major advantages of the proposed methodology are large-scale networks with large nodes count, better scalability, independence of key CHs, low error rate and high reliability. Using internal and external criteria, the validity of the clustering quality can be measured. Experimental simulation shows that the proposed methodology will be useful in improving the network lifetime and energy consumption. Hence the proposed node further enhances the death of the last node and first node when compared using other methodology.

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network. Keywords: Wireless sensor network; Differential Evolution; Low Energy Adaptive Clustering Hierarchy; HAS; modified HAS; simulated annealing algorithm

A hybrid approach for cluster head determination of unmanned aerial vehicle in flying ad-hoc networks

In recent years, flying ad hoc networks (FANETs) have witnessed a notable increase in its applications after the onset of the collaborations of the small unmanned aerial vehicles (UAVs). Because of its inherent characteristics, FANETs are used in diverse application ranging from the military to civil domain. Conversely, there are certain issues pertaining to the communication among the UAVs in view of the high mobility and limited battery resources available in the UAVs, resulting in their short lifetime. The paper is an attempt to address these issues plaguing to the short lifespan of the UAVs. In this paper, we propose a hybrid bio-inspired algorithm HGSOFA for optimizing cluster head (CH) selection in a FANETs. HGSOFA utilizes the hybrid implementation of glowworm swarm optimization (GSO) and firefly algorithm (FA). In this paper, we explain the step-by-step working of the HGSOFA and then performance is evaluated through rigorous simulations. Two separate network areas with varying node density is considered for conducting all the simulations. A robust experimental environment is developed using Taguchi and orthogonal methods. HGSOFA’s performance is tested against the conventional GSO and FA algorithms in respect of cluster building time, energy consumption and first node death. Comparable results have showcased the advantages of the HGSOFA as compared to other algorithms.

Energy balanced, delay aware multi-path routing using particle swarm optimisation in wireless sensor networks

World-wide use of wireless sensor networks (WSNs) has urged the need for energy-efficient, distributed routing algorithms. The interest of researchers from the past decade is in energy-efficient routing. In this paper, sequential quadratic programming (SQP) based multi-path routing formulation focusing on improving lifetime and delay is represented, namely EBDA-DEFL. This SQP based formulation is solved using the optimisation tool after that same formulation is solved using particle swarm optimisation (PSO). Also, a quota strategy for traffic load distribution is also introduced to mitigate the negative effects of multi-path routing. The proposed work is experimented and compared with existing algorithms to analyse its quality over previous work. Comparison has been done in terms of first node death, half node death, last node death, delay, and time consumed by Fminimax and PSO. Simulation results confirm the supremacy of proposed work over the existing ones.

Energy balanced, delay aware multi-path routing using particle swarm optimisation in wireless sensor networks

World-wide use of wireless sensor networks (WSNs) has urged the need for energy-efficient, distributed routing algorithms. The interest of researchers from the past decade is in energy-efficient routing. In this paper, sequential quadratic programming (SQP) based multi-path routing formulation focusing on improving lifetime and delay is represented, namely EBDA-DEFL. This SQP based formulation is solved using the optimisation tool after that same formulation is solved using particle swarm optimisation (PSO). Also, a quota strategy for traffic load distribution is also introduced to mitigate the negative effects of multi-path routing. The proposed work is experimented and compared with existing algorithms to analyse its quality over previous work. Comparison has been done in terms of first node death, half node death, last node death, delay, and time consumed by Fminimax and PSO. Simulation results confirm the supremacy of proposed work over the existing ones.