Longevity Of Wireless Sensor Networks Research Articles

An efficient neural network LEACH protocol to extended lifetime of wireless sensor networks

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions—representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol’s potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs.

Analytical and Empirical Insights into Wireless Sensor Network Longevity: The Role MAC Protocols and Adaptive Strategies

Wireless Sensor Networks (WSNs) have become essential in various fields, such as corporate industries, environmental monitoring, military defense, and healthcare, due to their ability to monitor and transmit data from remote locations. However, the battery-powered nature of WSN nodes necessitates a strong emphasis on energy conservation to extend network lifespan. This paper evaluates the impact of different Medium Access Control (MAC) protocols on WSN performance, focusing on energy efficiency and data transmission reliability. We explore various MAC protocols, including contention-based protocols like Carrier Sense Multiple Access (CSMA), schedule-based protocols like Time Division Multiple Access (TDMA), and hybrid protocols that combine both approaches. The methodology integrates analytical modelling, empirical data collection, and expert interviews to comprehensively assess the performance and longevity of WSNs under different MAC protocols. Analytical models were developed to simulate WSN behavior, considering factors such as node density, traffic load, and energy consumption. Empirical data from real-world deployments and experimental setups were analyzed to validate the models and provide practical insights. Expert interviews further highlighted the challenges and considerations in MAC protocol design and implementation. This paper underscores the importance of context-specific MAC protocol selection and the potential of adaptive strategies, including machine learning, to enhance WSN efficiency and reliability. These insights can guide the development of more sustainable and high-performing WSN applications.

An energy‐efficient method using PSO clustering and fuzzy routing to reduce energy consumption in IoT

SummaryIn “Internet of Things” (IoT) technology, all objects can connect to the Internet with a unique Internet address. The sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. These networks are subject to a variety of errors. Errors in wireless communication channels, network dynamics due to the mobility of nodes, and the potential for mistakes in mobile nodes are among the causes of errors in these networks. Despite the above failures, the development of routing algorithms in IoT‐based networks has become a significant challenge for researchers, so it is essential to provide routing algorithms to increase network lifespan and reduce energy consumption. In this study, a two‐dimensional network‐based architecture for the Internet of Things is presented. The proposed algorithm first organizes the nodes into logical cells and selects nodes in each cell as the cluster head. The cluster head is selected based on the binary Particle Swarm Optimization algorithm. Then, each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a fuzzy method to send the data to the base station. The proposed algorithm uses the remaining energy level parameter, cluster density, and location of each cell using fuzzy system capabilities and the particle swarm algorithm to try to route data with less energy consumption and increase network lifespan. The proposed method is simulated in ns2 software, and for a more accurate evaluation, we compare it with LEACH, Ant‐C, PSO, EABC, and SOA‐CH algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In comparison to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper’s study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

Optimization of Energy-Efficient Algorithms for Real-Time Data Administering in Wireless Sensor Networks for Precision Agriculture

Precision agriculture relies heavily on wireless sensor networks (WSNs) to monitor environmental conditions and manage resources efficiently. However, traditional WSN algorithms face significant challenges in energy management, resulting in frequent battery replacements, high maintenance costs, and reduced network reliability. Additionally, issues with high data latency and short network lifetimes hinder real-time decision-making and continuous monitoring. This study aims to address these challenges by developing energy-efficient algorithms that enhance the performance and longevity of WSNs in precision agriculture. The objectives include creating adaptive sampling, dynamic power management, and sleep scheduling algorithms to optimize energy consumption; improving data transmission efficiency and reducing latency through advanced data aggregation techniques; and extending network lifetime with balanced energy load distribution and efficient power usage strategies. The study introduces innovative algorithms that significantly reduce energy consumption and extend network lifetime, contributing to the field with adaptive and dynamic methodologies. Extensive simulations demonstrate that the proposed algorithms achieve a 30% reduction in energy consumption, a 20% decrease in data latency, and a 40% increase in network lifetime compared to traditional methods. Additionally, the packet delivery ratio improved by 10%, and computational overhead was reduced by 30%, highlighting the efficiency and reliability of the proposed solutions. These results are consistent with recent advancements in WSN optimization techniques, such as those utilizing cognitive radio and deep learning, validating the potential of the proposed algorithms for real-world application.

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage.

REDUCING ENERGY CONSUMPTION IN IOT BY A ROUTING WHALE OPTIMIZATION ALGORITHM

The Internet of Things is a new concept in the world of information and communication technology, in which for each being (whether it be a human, an animal or an object), the possibility of sending and receiving data through communication networks such as the Internet or Intranet is provided. Wireless sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. In this paper, a routing protocol has been proposed and stimulated which is based on the function of the whale optimization algorithm. Clustering is performed through a routing method which is based on energy level, collision reduction, distance between cluster head node and destination, and neighbor energy. Furthermore, the selection of the cluster head node is performed based on the maximum remaining energy, the least distance with other clusters, and energy consumption, where energy consumption for reaching the base station is minimized. By de-creasing the level of cluster head energy from the specified threshold value from among the nodes in the same cluster, a node with an energy level above the threshold would be selected as the new cluster head. Moreover, four conditions (i.e. the shortest route, the leading route, the least distance to the source node, and destination node) are applied for routing. The proposed method was compared to LEACH, EEUC, EECRP, BEAR and CCR algorithms, and the results indicated the superiority of the proposed method to other methods in terms of the number of dead nodes.

GLBR: A novel global load balancing routing scheme based on intelligent computing in partially disconnected wireless sensor networks

Load balancing is of great significance to extend the longevity of wireless sensor networks, due to the inherent imbalanced energy overhead in such networks. However, existing solutions cannot balance the load distribution in partially disconnected wireless sensor networks. For example, if a network is partitioned into several segments with different area sizes, some areas have much more traffic load than other areas. In this article, we propose a load-balanced routing scheme, which aims to balance energy consumption within each segment and among different segments. First, we adopt unequal transmission distances to build initial routing for intrasegment load balancing. Second, we adopt the genetic algorithm to build extra routing between different segments for intersegment load balancing. The unique character of our work is twofold. On one hand, we investigate partitioned wireless sensor networks where there are several isolated segments. On the other hand, we pursue load balancing from a global perspective rather than from a local one. Some simulations verify the effectiveness and the advantages of our scheme in terms of extra deployment cost, system longevity, and load balancing degree.

Enhancement of Energy Efficiency and Network Lifetime Using Modified MPCT Algorithm in Wireless Sensor Networks

Wireless sensor networks (WSN) allude to gathering of spatially fragmented and committed sensors for observing and documenting various physical and climatic variables like temperature, moistness and, so on. WSN is quickly growing its work in different fields like clinical, enterprises, climate following and so on. However, the sensor nodes have restricted battery life and substitution or re-energizing of these batteries in the sensor nodes is exceptionally troublesome for the most parts. Energy effectiveness is the significant worry in the remote sensor networks as it is significant for keeping up its activity. In this paper, clustering algorithms based on sensor module energy states to strengthen the network longevity of wireless sensor networks is proposed (i.e. modified MPCT algorithm) in which cluster head determination depends on the every cluster power centroid as well as power of the sensor nodes. Correspondence between cluster leader and sink module employ a parameter distance edge for lessening energy utilization. The outcome got shows a normal increment of 60% in network lifetime compared to Low energy adaptive protocol, Energy efficient midpoint initialization algorithm (EECPK-means), Park K-means algorithm and Mobility path selection protocol.

M/G/1 QUEUE-BASED REDUCTION OF POWER CONSUMPTION AND LATENCY IN WIRELESS SENSOR NETWORKS

Longevity of wireless sensor networks (WSN) is dependent on the optimal utilization of power supply. To make optimal utilization of power and increase the operational life of the network, we present two techniques with the goal of decreasing the consumption of power in a sensor node by incorporating queuing theory. We analyze the performance of wireless sensor networks that implement a M/G/1 queue with two different queuing policies. The analysis is done with respect to two important aspects: power consumption and latency delay. The results of the analysis illustrate the fact that the power consumed at a wireless sensor node can be reduced significantly by optimal selection of thresholds. We also compare the two policies in terms of power consumption and latency and find that the Min (N, T) policy is better equipped to not only reduce the power consumption but also reduces the latency delay caused due to the introduction of the queuing thresholds. The results indicate that the schemes studied can be implemented in practical scenarios as they are effective in reducing power consumption and increasing the operational life of a WSN.

A Robust Netrosophic Clustering Based Raven Routing Optimization to Alleviate Void Hole and Energy Depletion in IoT WSN

Wireless Sensor Networks (WSNs) find extensive utilisation in diverse domains, including but not limited to environmental monitoring, industrial control, and healthcare. Wireless Sensor Networks (WSNs) have a crucial function in gathering and relaying information from sensor nodes to a central node or sink in various applications. Wireless Sensor Networks (WSNs) encounter various obstacles that can have a significant impact on their performance and lifespan. These challenges include but are not limited to limited battery life, void holes, and energy depletion.
 Several routing algorithms have been suggested in scholarly works to tackle these concerns, such as clustering-based routing, data-centric routing, and location-based routing. Nonetheless, a majority of these algorithms possess constraints and may not be appropriate for every situation.
 The present study introduces a methodology called Rncrro (Robust Netrosophic Clustering Based Raven Routing Optimisation) that aims to mitigate the issues of void holes and energy depletion in Wireless Sensor Networks (WSNs). The proposed approach utilises Netrosophic Clustering and Raven Optimisation algorithms in tandem to enhance the efficacy and resilience of the routing mechanism.
 The significance of this study is rooted in its capacity to surmount the constraints of current routing algorithms and furnish a superior and dependable resolution for Wireless Sensor Networks (WSNs). The proposed methodology has the potential to enhance the performance and longevity of Wireless Sensor Networks (WSNs) by mitigating void holes and energy depletion. This, in turn, can result in more efficient and dependable data collection and transmission across diverse applications.

Sampling Sensor Nodes to Extend the Longevity of Wireless Sensor Networks

Background and Objective: Wireless Sensor Networks (WSN) are one of the most important elements in the Internet of Things (IoT) paradigm. It is envisaged that WSNs will seamlessly bridge the physical world with the Internet resulting in countless IoT applications in smart cities, wearable devices, smart grids, smart retails amongst others. It is necessary, however, to consider that sensing, processing and communicating large amounts of sensor data is an energy-demanding tasks. Recharging or replacing those battery-powered sensor nodes deployed in inaccessible locations is generally a tedious and time-consuming task. As a result, energy efficient approaches for WSN need to be devised in order to prolong the longevity of the network. Methods: In this paper, we present an approach that reduces energy consumption by controlling the sampling rate and the number of actively communicating nodes. The proposed approach applies compressive sensing to reduce the sampling rate and a statistical approach to decrease the sample size of sensor nodes. Results and Conclusion: The proposed approach is expected to significantly increase the lifetime of the network whilst maintaining the event detection accuracy.

Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility

The longevity of Wireless Sensor Networks (WSNs) is a crucial concern that significantly influences their applicability in a specific context. Most of the related literature focuses on communication protocols aiming to reduce the energy consumption which would eventually lead to longer network lifetimes. On the other hand, a limited number of studies concentrate on providing a unifying frame to investigate the integrated effect of the important WSN design decisions such as sensor places, activity schedules, data routes, trajectory of the mobile sink(s), along with the tactical level decisions including the data propagation protocols. However, a monolithic mathematical optimization model with a practically applicable, efficient, and accurate solution method is still missing. In this study, we first provide a mathematical model which integrates WSN design decisions on sensor places, activity schedules, data routes, trajectory of the mobile sink(s) and then present two heuristic methods for the solution of the model. We demonstrate the efficiency and accuracy of the heuristics on several randomly generated problem instances on the basis of extensive numerical experiments.

On the energy-delay trade-off in geographic forwarding in always-on wireless sensor networks: A multi-objective optimization problem

The design and development of multi-hop wireless sensor networks are guided by the specific requirements of their corresponding sensing applications. These requirements can be associated with certain well-defined qualitative and/or quantitative performance metrics, which are application-dependent. The main function of this type of network is to monitor a field of interest using the sensing capability of the sensors, collect the corresponding sensed data, and forward it to a data gathering point, also known as sink. Thus, the longevity of wireless sensor networks requires that the load of data forwarding be balanced among all the sensor nodes so they deplete their battery power (or energy) slowly and uniformly. However, some sensing applications are time-critical in nature. Hence, they should satisfy strict delay constraints so the sink can receive the sensed data originated from the sensors within a specified time bound. Thus, to account for all of these various sensing applications, appropriate data forwarding protocols should be designed to achieve some or all of the following three major goals, namely minimum energy consumption, uniform battery power depletion, and minimum delay. To this end, it is necessary to jointly consider these three goals by formulating a multi-objective optimization problem and solving it. In this paper, we propose a data forwarding protocol that trades off these three goals via slicing the communication range of the sensors into concentric circular bands. In particular, we discuss an approach, called weighted scale-uniform-unit sum, which is used by the source sensors to solve this multi-objective optimization problem. Our proposed data forwarding protocol, called Trade-off Energy with Delay (TED), makes use of our solution to this multi-objective optimization problem in order to find a “best” trade-off of minimum energy consumption, uniform battery power depletion, and minimum delay. Then, we present and discuss several numerical results to show the effectiveness of TED. Moreover, we show how to relax several widely used assumptions in order to enhance the practicality of our TED protocol, and extend it to real-world network scenarios. Finally, we evaluate the performance of TED through extensive simulations. We find that TED is near optimal with respect to the energy×delay metric. This simulation study is an essential step to gain more insight into TED before implementing it using a sensor test-bed.

A Load Balancing Multi-path Secure Routing Scheme for Wireless Sensor Networks

Lifetime optimization and security are two important design issues for WSNs with nonreplenishable energy resources. Routing protocols in wireless sensor networks (WSNs) are susceptible to a number of attacks depending on the nature of the protocol, its application, and the environment in which the protocol is intended to be used. Routing protocols that do not take the malicious attacks into account can not be easily temper proofed. The longevity of WSNs is a crucial concern that significantly influences their applicability in a specific context. In this paper, we present a Load Balancing Multi-path Secure Routing (LBMSR) protocol to address these two issues concurrently through balanced energy consumption and one-way hash key chain and symmetric key cryptography. LBMSR is designed with two configurable parameters, load balance control and security level. Load balance is used to avoid the problem of energy consuming imbalance and the formation of energy holes. Security level is designed to determine the probabilistic distribution of the random walking that provides routing security. Simulation results and security analyses show that the proposed algorithm can provide excellent balance between routing efficiency and energy consumption while preventing routing attacks.

Joint Sink Mobility and Routing to Maximize the Lifetime of Wireless Sensor Networks: The Case of Constrained Mobility

The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While communication protocols are striving to save energy by acting on sensor nodes, recent results show that network lifetime can be prolonged by further involving sink mobility. As most proposals give their evidence of lifetime improvement through either (small-scale) field tests or numerical simulations on rather arbitrary cases, a theoretical understanding of the reason for this improvement and the tractability of the joint optimization problem is still missing. In this paper, we build a framework for investigating the joint sink mobility and routing problem by constraining the sink to a finite number of locations. We formally prove the NP-hardness of the problem. We also investigate the induced subproblems. In particular, we develop an efficient primal-dual algorithm to solve the subproblem involving a single sink, then we generalize this algorithm to approximate the original problem involving multiple sinks. Finally, we apply the algorithm to a set of typical topological graphs; the results demonstrate the benefit of involving sink mobility, and they also suggest the desirable moving traces of a sink.