Energy Hole Problem Research Articles

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

A Mobility Based Approach to Strengthen the Network Lifetime of Wireless Sensor Networks in 3D Region

Background: In this era of emerging technologies, Mobile Wireless Sensor Network (MWSN) has emerged as a powerful tool for many applications. Applications such as battlefield and traffic surveillance, agriculture and environment monitoring, smart homes and smart cities require a specific protocol to fulfill a specific purpose. WSN is composed of numerous tiny Sensor Nodes (SNs) along with one or more sinks, where sinks have unlimited sources of energy and SNs are battery- operated. SN tasks are to sense the data and transmit it to sink through the formation of dynamic topology. The SNs nearer to the sink rapidly exhaust their energy due to the heavy burden. Due to this, SNs became dead affecting the performance of the network lifespan. To overcome this problem, the concept of MWSN has been proposed. In MWSN, the sink can move from one location to another, and collect data from SNs. With the help of MWSN, the problem of energy holes can be resolved. An energy hole is a problem in which nodes are alive but they are not able to send the data due to low energy left. To overcome this problem, MWSN plays an important role. MSWN can move around the region and collect the data from SNs. Methods: In this work, we have proposed a Mobile Sink (MS) that can move on fixed or random locations for data collection from SNs. The comparative analysis of various MS strategies such as MS on boundaries, 4 sojourn locations in the region, random position in the region and fixed path to collect the data has been done. Results: SNs become dead in 2246 rounds in static approach. In the MS boundary approach, all SNs are dead in 2593 rounds. In the sojourn location, it lasts up to 4827. But in MS random and fixed location approaches, all SNs are dead in 11568 and 11513 rounds, respectively. Conclusion: The simulation results depict that the MS strategies having fixed or random positions in the region enhanced the network lifetime 4 to 5 times more than the static sink.

An optimal multipath routing protocol using hybrid gravitational search particle swarm optimization for secure communication

SummaryCommunicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%.

A Genetic-Algorithm-Based Dynamic Transmission of Data for Communicable Disease in IoMT Environment

Recent advancements in the field of the Internet of medical things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA) based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this paper. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

On the Best Fitness Function for the WSN Lifetime Maximization: A Solution Based on a Modified Salp Swarm Algorithm for Centralized Clustering and Routing

Wireless sensor networks (WSNs) have become increasingly important in recent years due to their ability to monitor and collect data in various environments. However, WSNs are often limited by their energy resources, making energy efficiency a critical concern in designing WSNs. Clustering and multi-hop routing are effective methods for maximizing the lifetime of WSNs. The energy efficient clustering and routing problem in WSNs can be formulated as a multi-dimensional knapsack problem (d-kp). This formulation can generalize all clustering and routing protocols in regard to energy efficiency. In this paper, we investigate the hardness of the energy-efficient clustering and routing problem in WSNs based on the d-kp formulation and introduce a modified Salp Swarm Algorithm (SSA) as a method to solve this problem. SSA is a metaheuristic algorithm inspired by the behavior of salps in the ocean. Moreover, we prove that it is impossible to design a polynomial time fitness function to maximize a long-term metric such as FND (time of first node death), which is a commonly used metric for network lifetime. Our results have implications for the design of energy-efficient WSNs and call for the development of more efficient algorithms that can handle the complexity of this problem. furthermore, we evaluate our proposed algorithm using simulation and compare it to existing algorithms. Our results show that using SSA with a carefully designed fitness function based on the theoretical findings and a unique one-to-one routing protocol to mitigate the energy hole problem, outperforms many of the existing algorithms in terms of energy efficiency and network lifetime.

Energy aware optimal routing model for wireless multimedia sensor networks using modified Voronoi assisted prioritized double deep Q‐learning

SummaryThe energy hole problem is critical in Wireless Multimedia Sensor Network (WMSN), wherein the nodes closer to the sink node will expire sooner than the outer sub‐regions. Because they transmit their packet and forward the outer sub‐regions packet to the sink. Hence, a hole arises near the sink node after a very short time. Thus, an energy‐aware optimal routing model is introduced in this research using Modified Voronoi‐assisted prioritized double deep Q‐learning (PDDQL). Initially, the sensor nodes are deployed using the Voronoi cell structure that splits the entire region of interest into different Voronoi polygons. However, the Voronoi diagram will not always predict an efficient route because of the nodes at a Voronoi edge or vertex outside the transmission range. To tackle this issue, a new Voronoi diagram is proposed that depends on the maximum horizontal transmission range to remove the portion of Voronoi edges uncovered by the sensor node. After the deployment, a PDDQL algorithm is used to select the Relay Nodes based on their remaining energy. The proposed PDDQL determines an alternate route to transmit the packet if an energy hole problem has occurred in WMSN. The analysis of a proposed routing protocol based on the assessment measures like packet delivery ratio, average residual energy, number of alive nodes, and average end‐to‐end delay accomplished the value of 198.33 (ms), 2.05 (J), 100, and 0.85, respectively.

Dynamic layered routing protocols based on BP-NN for underwater acoustic sensor networks

Underwater acoustic sensor network (UASN) effectively improves the reliability of underwater long distance transmission through multi-hop transmission between multiple nodes. However, the limited energy of underwater acoustic nodes and large end-to-end transmission delay lead to uneven load and short lifetime of UASN. In this paper, we propose a dynamic layered routing protocol based on back propagation neural network (BP-NN), named as BPDLR, to address the above issues. In the proposed scheme, considering such factors as node topology, residual energy and communication environments, the UASN is vertically divided into the top layer, the middle layer and the bottom layer. The routing designs in each layer are different, and each of them is predicted by BP-NN training model for routing. In addition, we compare the proposed BPDLR protocol with the existing schemes such as static layered routing (SLR) protocol and depth-based routing (DBR) protocol in terms of network node survival, average end-to-end delay and packet loss rate. The simulation results show that, the proposed BPDLR protocol effectively alleviates the energy hole problem of UASN and extends the network lifetime.

Data Collection in WSNs using a Probability-Based Rendezvous Points Selection Algorithm

Wireless Sensor Networks are becoming more prevalent in various industries, including military operations and distant environmental monitoring. This is important because sensors are getting smarter, smaller, and less expensive. The energy hole problem in the WSN has been a major focus of recent research. The mobile sink is an efficient solution for the energy hole problem in a wireless sensor network. A mobile sink gets data from sensors by moving around the network often to avoid problems with hotspots or energy holes. It gets data from network nodes by traveling regularly and visiting a group of nodes known as rendezvous points (RPs). This research will present a probability-based RP selection (PRPS) technique for data collection in wireless sensor networks. To begin, a directed spanning tree is used to construct a tree that eliminates duplication in the data forwarding path. The proposed method is employed to compute the likelihood of RPs. Finally, using the shortest path technique, a mobile sink is constructed between these locations. The path provided is the best path that connects all of the RPs. The proposed approach improves the previous solutions by choosing the nodes with the most data packets as RPs. As a result, it extends network lifetime by lowering energy consumption and addressing the energy hole problem.

Minimizing Energy Hole Problem Comparisons in Some Hierarchical WSN Routing Protocols

This paper study the efficient use of energy as a challenging task of designing these protocols. The energy holes are created due to non-uniform node distribution in the network when quickly drain the energy in a few nodes. It is studies removing energy holes by using sleep and awake mechanism for sensor nodes to save energy. This approach finds the maximum distance nodes to calculate the maximum energy for data. It intends to enumerate the dead nodes transmission, alive nodes, throughput, overhead and packet delivery ratio consumed by the entire network and affected by the network area changes. lastly, a brief performance analysis of Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy-Efficient Clustering (DEEC), Threshold sensitive Energy Efficient sensor Network protocol (TEEN) and Stable Election Protocol (SEP) is carried out considering metrics of the previous characteristics of wireless sensor network.

Cluster Head Selection for the Internet of Things Using a Sandpiper Optimization Algorithm (SOA)

In recent years, our life has become broader and faster by adapting to the Internet of Things (IoT). In IoT, the devices distributed globally that are connected to the Internet improve productivity in various sectors. The network plays an important role for transferring data to the sink node by collecting from all other nodes in IoT. The IoT requires energy saving since it is connected to resource‐constrained devices. Energy preservation is a difficult challenge to improve network lifetime in IoT. Clustering is one of the key techniques to extend the network’s life. In that, cluster head selection is one of the promising techniques to extend the lifespan of the IoT network. Many researchers proposed various cluster head (CH) selection techniques in IoT. However, inappropriate CH selection quickly degrades a network battery and creates an energy‐hole problem in the network. This paper proposes a novel sandpiper optimization algorithm (SOA) to select CH among the networks. Later, the cluster is formed by using the Euclidean distance. The proposed SOA’s accomplishments are compared to fitness value‐based improved grey wolf optimization (FIGWO), particle swarm optimization (PSO), artificial bee colony‐SD (ABC‐SD), and improved artificial bee colony (IABC). The proposed SOA extends the network lifespan by 3‐18% and increases the throughput by 6‐10%. Thus, the proposed SOA increases the network lifetime and throughput and decreases the energy consumption among the nodes in the network.

An energy fault and consumption optimization strategy in wireless sensor networks with edge computing

With the operation of wireless sensor networks (WSNs), energy-constrained sensor nodes and inadequate energy constraint methods are becoming obsolete, as they reduce the efficiency of data transmission. The appearance of edge computing (EC) and causality graph provide a new opportunity for energy fault analysis and assessment in WSNs. As a result, by selecting a reliable data transmission path and averaging the energy consumption, we present an energy fault and consumption optimization (EFCO) algorithm for solving the energy hole and consumption balance (EHCB) problem in wireless sensor networks with edge computing (ECWSNs). Specifically, we first build a novel four-layer network architecture by using edge computing technology and causality graph theory. Then, the energy fault cost (EFC) in ECWSNs is formulated as an optimization problem that is constrained by the energy allocation of relay nodes. Furthermore, we propose a causal reasoning algorithm to deduce the single-value fault status probability of relay nodes. Finally, we utilize version 2 of a network simulator (NS-2) to evaluate the fault derivation and energy allocation efficiency of the EFCO algorithm in ECWSNs.

Multiwinner Voting for Energy-Efficient Mobile Sink Rendezvous Selection in Wireless Sensor Network

Recent studies have demonstrated the advantage of applying mobile sink to prevent the energy-hole problem and prolong network lifetime in wireless sensor network. However, most researches treat the touring length constraint simply as the termination indicator of rendezvous point selection, which leads to a suboptimal solution. In this paper, we notice that the optimal set of rendezvous points is unknown but deterministic and propose to elect the set of rendezvous points directly with the multiwinner voting-based method instead of step-by-step selection. A weighted heuristic voter generation method is introduced to choose the representative voters, and a scoring rule is also well designed to obtain a satisfying solution. We also employ an iterative schema for the voting score update to refine the solution. We have conducted extensive experiments, and the results show that the proposed method can effectively prolong the network lifetime and achieve the competitive performance with other SOTA methods. Compared to the methods based on step-by-step selection, the proposed method increases the network lifetime by 23.2% and 10.5% on average under the balanced-distribution and unbalanced-distribution scenarios, respectively.

An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNs

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

Load balanced cluster formation to avoid energy hole problem in WSN using fuzzy rule-based system

Load balanced cluster formation to avoid energy hole problem in WSN using fuzzy rule-based system

An Energy Balance-Oriented Clustering Routing Protocol for Cognitive Radio Sensor Networks

Uneven clustering is an effective solution to solve the energy hole problem in multihop cognitive radio sensor networks (CRSNs). However, current literature leaves control overhead and multihop intercluster routing out of consideration and adopts constant weight coefficients for cluster radius calculation instead of reasonably setting their values according to specific network configurations. To solve the above problems, a distributed energy balance-oriented uneven clustering routing protocol (EBUCRP) is proposed in this article. First, by fully considering the energy consumption of control information exchange and data transmission, EBUCRP theoretically derives the optimal cluster radius of each unit within which distributed clusters are formed. Second, communication capability-based selection criteria are defined from joint energy and spectral efficiency perspectives to help select high-quality cluster heads and next-hop relays. In this case, the monitoring information can be transferred to the sink through efficient intracluster data aggregation and intercluster multihop forwarding. Third, EBUCRP strictly restricts the energy consumption of control information exchange during network operation. Simulation results show that EBUCRP gains obvious advantages over competing protocols in enhancing the lifetime and supervision capability of both single-hop and multihop CRSNs.

Virtual Infrastructure based Routing Algorithm for IoT enabled Wireless Sensor Networks with Mobile Gateway

The IoT technology is seeking the attention of industry and researcher day by day due to its large number of applications and is off monitoring and controlling the object from the remote location. The very basic and important element of IoT is wireless sensor network where sensor nodes are attached with the object and generates the sensory data related to the object an actuator can provide the movement to the object. To make it a success reliable and efficient communication model is the main requirement. To fulfil the objective related to IoT application success in this paper a communication protocol called virtual infrastructure based routing algorithm is proposed. In this proposal a virtual cross region structure is formed in the central of the sensor network, which is called meeting region. In this meeting region the sensor nodes send their generated data and the gateway node receives those data. The problem of energy hole, the mobile gateway node is considered in this proposal. The proposed algorithm is implemented using the standard wireless sensor network simulator tool and compared the performance of the proposed algorithm with the existing algorithm based on some standard performance metrics. The simulation outcome exhibits that the proposed algorithm outperformed the existing algorithms in different performance metrics.

Virtual Infrastructure based Routing Algorithm for IoT enabled Wireless Sensor Networks with Mobile Gateway

The IoT technology is seeking the attention of industry and researcher day by day due to its large number of applications and is off monitoring and controlling the object from the remote location. The very basic and important element of IoT is wireless sensor network where sensor nodes are attached with the object and generates the sensory data related to the object an actuator can provide the movement to the object. To make it a success reliable and efficient communication model is the main requirement. To fulfil the objective related to IoT application success in this paper a communication protocol called virtual infrastructure based routing algorithm is proposed. In this proposal a virtual cross region structure is formed in the central of the sensor network, which is called meeting region. In this meeting region the sensor nodes send their generated data and the gateway node receives those data. The problem of energy hole, the mobile gateway node is considered in this proposal. The proposed algorithm is implemented using the standard wireless sensor network simulator tool and compared the performance of the proposed algorithm with the existing algorithm based on some standard performance metrics. The simulation outcome exhibits that the proposed algorithm outperformed the existing algorithms in different performance metrics.

A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks

Aiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumption. Through effective transformation and theoretical analysis, cluster radius of each ring is derived by minimizing the network energy consumption and balancing the residual energy among nodes in different rings. Distributed cluster heads (CHs) selection and cluster formation are carried out within this range to control the cluster size and the corresponding energy cost. Expected times for being CHs metric is defined to measure nodes’ energy and spectral potential and help select powerful CHs. Simulation results show that ECMUC protocol is superior to most clustering protocols designed for CRSNs in terms of network surveillance capability and network lifetime, and it is also demonstrated that taking control overhead into consideration is beneficial for improving the network performance.

An energy-balanced head nodes selection scheme for underwater mobile sensor networks

Underwater sensor networks system is a promising technology for smart ocean monitoring applications. To address the imbalance problem of network overhead and limited energy of underwater sensor nodes, this paper presents a CH–SH Selection and Sink Path-planning (CSSP) scheme to collect vast amount of data from heterogeneous sensor nodes. The scheme firstly establishes a three-layer network structure model for underwater mobile sensor networks (UMSN) based on multi-mode communication mechanism and provides energy-balanced head nodes (cluster head and sub-cluster head nodes) selection algorithm based on particle swarm iterative optimization. Then, the optimal global-path-plan of mobile sink is proposed to visit all the head nodes to collect data, reduce the multi-hop underwater acoustic transmission distance of relay nodes, and avoid the energy hole problem. Lastly, the upper-layer multi-hop network of UMSN is designed to remotely control local-path-plan over mobile sink, in order to implement joint planning of global and local paths of mobile sink. Simulation results verified that the proposed CSSP scheme outperformed 11%, 16% and 22% over three typical protocols in terms of nodes energy consumption, CSSP was 9%, 12% and 19% lower than three typical protocols in terms of SD of energy consumption, packet delivery ratio of CSSP was 8%, 10% and 12% higher than three typical protocols. The scheme could significantly balance energy and reduce packet loss rate.

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery.

In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.