Energy Efficiency In Sensor Networks Research Articles

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

HDRA: A Haybird Data Reduction and Routing Algorithm

Presently, wireless sensor networks (WSNs) are emerging as a vibrant field of research due to various challenging aspects such as energy consumption, routing strategies, effectiveness, among others. Despite unresolved issues within WSNs, a substantial array of applications has already been developed. For any application design, a primary objective is to optimize the WSN in terms of its lifecycle and functionality. Recent studies on data reduction methods have shown that sensor nodes often transmit data directly (single hop) to the base station (BS). However, a significant concern is that most existing multi-hop routing protocols do not address data reduction before forwarding data to the BS. Consequently, this study introduces a Hybrid Data Reduction and Routing Algorithm (HDRA). The principal aim of HDRA is to prolong the lifespan of cluster-based WSNs. It strives to decrease the packet transmission by sensor nodes, especially when there's minimal change in sensor readings. The findings indicate that HDRA outperforms the LEACH protocol in terms of energy efficiency in sensor networks, irrespective of network type (T, H, or TH) or deployment scenarios (200x200m or 400x400m). Overall, the proposed algorithm enhances network performance by conserving energy and extending network lifespan.

An agent-based approach for energy-efficient sensor networks in logistics

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of ×21.7 and ×3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems.

Vibration Monitoring in the Compressed Domain With Energy-Efficient Sensor Networks

Vibration Monitoring in the Compressed Domain With Energy-Efficient Sensor Networks

Enhanced energy efficient sleep awake aware intelligent sensor network routing protocol for wireless sensor network

One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime.

Optimization of wireless sensor networks energy consumption by the clustering method based on the firefly algorithm

Wireless sensor networks (WSNs) contain an inordinate number of sensor nodes that are spatially distributed. The network is composed of entities that determine its lifetime. The WSN nodes are equipped with a battery whose autonomy is limited in duration. In this paper, different solutions are introduced to improve the overall energy consumption of the network in order to improve its lifetime. Contrary to many works considering the clustering algorithm as one potential candidate to improve the network's lifetime, this study has investigated the firefly algorithm optimization where an optimal cluster head is selected from a group of nodes. The set-up process of the cluster head is based on a set of conditions. To measure the performance of the proposed approach, the number of dead nodes and data packets received by the base station (BS) or sink node are considered. The results are tested on 100 nodes for 5000 transmission rounds, the amount of data transported is 20 million bits a little more than the other methods. It has been shown that the proposed solution outperformed the traditional low energy adaptive clustering hierarchy (LEACH), threshold sensitive energy efficient sensor network (TEEN), and developed distributed energy-efficient clustering (DEEC) approaches.

GREEN IOT FOR SUSTAINABLE SMART CITIES: INNOVATIONS AND CHALLENGES

This research paper explores "Green IoT for Smart Cities," the integration of green technologies and Internet of Things (IoT) applications to foster environmentally sustainable urban environments. The study reviews the state-of-the-art developments in energy-efficient sensor networks, smart waste management, intelligent transportation systems, and optimized energy consumption in buildings. Real-world implementations of Green IoT initiatives in smart city projects worldwide are analyzed, highlighting successes and challenges in achieving sustainability objectives. The paper outlines potential benefits, including reduced carbon footprint, optimized resource allocation, improved air quality, and enhanced quality of life for citizens, as well as economic implications for cities and businesses. Limitations such as data security, privacy concerns, scalability, and interoperability among IoT systems are identified, along with future research directions. Overall, this paper emphasizes the significance of integrating Green IoT technologies into smart city frameworks to address environmental challenges in urbanization, offering valuable insights for policymakers, researchers, and practitioners.

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.

Design and Validation of Lifetime Extension Low Latency MAC Protocol (LELLMAC) for Wireless Sensor Networks Using a Hybrid Algorithm

As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid algorithm to improve the energy efficiency of sensor networks with nodes that are regularly placed. Every node in this protocol has three operating modes, which are sleep mode, receive mode, and send mode. Every node enters a periodic sleep state in order to conserve energy, and after waking up, it waits for communication. During the sleep mode, the nodes turn off their radios in order to reduce the amount of energy they consume while not in use. As an added feature, this article offers a channel access mechanism in which the sensors can send data based on the Logical Link Decision (LLD) algorithm and receive data based on the adaptive reception method. It is meant to select acceptable intermediary nodes in order to identify the path from the source to the destination and to minimize data transmission delays among the nodes in the network scenario. Aside from that, both simulation and analytical findings are used to examine the activity of the suggested MAC, and the created models are evaluated depending on their performance. With regard to energy consumption, latency, throughput, and power efficiency, the result demonstrates that the suggested MAC protocol outperforms the corresponding set of rules. The extensive simulation and analytical analysis showed that the energy consumption of the proposed LELLMAC protocol is reduced by 22% and 76.9% the end-to-end latency is 84.7% and 87.4% percent shorter, and the throughput is 60.3% and 70.5% higher than the existing techniques when the number of node is 10 and 100 respectively.

Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.

Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks.

The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by means of wireless sensor networks (WSNs). Current vendor-level advancements enable algorithm-level flexibility to design protocols to concurrently collect multiple application data while enforcing the reduction of energy expenditure to gain commercial success in the industrial stage. In this paper, we propose a hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks. In our proposed model, homogeneous and heterogeneous nodes are deployed within specific regions. To reduce unnecessary data transmission, threshold-based conditions are presented to prevent unnecessary transmission when minor or no change is observed in the simulated and real-world applications. We further extend our proposed multi-hop model to achieve more network stability in dense and larger network areas. Our proposed model shows enhancement in terms of load balancing and end-to-end delay as compared to the other threshold-based energy-efficient routing protocols, such as the threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), and energy-efficient sensor network (TEEN).

Development of a modification of the method for constructing energy-efficient sensor networks using static and dynamic sensors

Due to the widespread use of sensors and sensor networks in the tasks of territory coverage, the relevant criteria are maximizing coverage and minimizing energy consumption. At the same time, the compliance of the network with these criteria is an urgent problem in the modern technological world. A modification of the method for constructing energy-efficient sensor networks is proposed by introducing an additional criterion for minimizing the number of sensors and limiting the number of sensors used, which allows reducing the energy consumption of sensor networks by 19 %. In the resulting optimization problem, the optimality criteria are the functions of minimizing the area of uncovered territory, the value of energy consumption, and the number of sensors. The optimum solution is formed by pairs of values of the coverage radius and the level of intersection of the coverage areas, which provide maximum coverage while minimizing energy consumption and the number of sensors used. To solve the problem, the parameter convolution method and the genetic algorithm were used. In the case of dynamic sensors, the problem is to find such a trajectory of the sensor that provides the maximum flyby of the territory with a minimum length. A grid algorithm is proposed to find the necessary trajectory. The presented algorithm consists in dividing the territory into nodes and estimating the value of the covered territory by the sensor in this node. After the formation of estimates, the search for a Hamiltonian path was used. The case of a multiply connected territory with the possibility of turning it into a simply connected one is considered. A scheme for finding the parameters of energy-efficient coverage of the territory using static and dynamic sensors is proposed.

Analysis of Energy Efficient Techniques of WSN

The wireless sensor networks is the decentralized type of network in which sensor devices sense information and pass it to base station. Due to dynamic topology of the network security, data aggregation and energy consumption are the major issue which affects network performance. In the past years many techniques are designed to improve lifetime of lifetime of sensor networks. In this review paper, energy efficient techniques of wireless sensor networks are reviewed in terms of certain parameters

ESSD: Energy Saving and Securing Data Algorithm for WSNs Security

The Wireless Sensor Networks (WSNs) are characterized by their widespread deployment due to low cost, but the WSNs are vulnerable to various types of attacks. To defend against the attacks, an effective security solution is required. However, the limits of these networks’ battery-based energy to the sensor are the most critical impediments to selecting cryptographic techniques. Consequently, finding a suitable algorithm that achieves the least energy consumption in data encryption and decryption and providing a highly protected system for data remains the fundamental problem. In this research, the main objective is to obtain data security during transmission by proposing a robust and low-power encryption algorithm, in addition, to examining security algorithms such as ECC and MD5 based on previous studies. In this research, the Energy Saving and Securing Data algorithm (ESSD) algorithm is introduced, which provides the Message Digest 5 (MD5) computation simplicity by modifying the Elliptic Curve Cryptography (ECC) under the primary condition of power consumption. These three algorithms, ECC, MD5, and ESSD, are applied to Low Energy Adaptive Clustering Hierarchy (LEACH) and Threshold-sensitive Energy Efficient Sensor Network Protocol (TEEN) hierarchical routing algorithms which are considered the most widely used in WSNs. The results of security methods under the LEACH protocol show that all nodes are dead at 456, 496, and 496, respectively, to ECC, MD5, and ESSD. The results of security methods under the TEEN protocol show that the test ends at 3743, 4815, and 4889, respectively, to ECC, MD5, and ESSD. Based on these results, the ESSD outperforms better in terms of increased security and less power consumption. In addition, it is advantageous when applied to TEEN protocol.

Linear Regression based Power Optimization of Wireless Sensor Network in Smart City

The modern smart cities are highly dependent on the performance of the Internet of Things (IoT) based energy-efficient sensor networks. Energy efficiency is a critical and indispensable issue for wireless sensor networks (WSNs). In the collection of sensor nodes, one node is selected to collect data and forward it to the base station. The modern base stations in smart cities are unmanned aerial vehicle (UAV) based. This paper presents a linear regression based model, where the initial residual energy and its corresponding transmission power are submitted to the proposed system, then it generates a prediction model since transmission power depends on the residual energy of the sensor node. Based on this model the transmission power of the sensor node can be calculated for data transmission as a higher residual node is best suitable for data transmission to the base station. As presented in the simulation result, the regression based model gives better performance for energy efficiency in WSN.

Energy efficient routing in wireless sensor network based on mobile sink guided by stochastic hill climbing

In Wireless Sensor Networks (WSNs), the reduction of energy consumption in the batteries of a sensor node is an important task. Sensor nodes of WSNs perform three significant functions such as data sensing, data transmitting and data relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for sensing and transmitting the data. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Network simulator 2 is used for experimentation purpose. This work also compares with the existing routing protocols like Energy-efficient Low Duty Cycle (ELDC), Threshold sensitive Energy Efficient sensor Network (TEEN) and Adaptive clustering protocol. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.

Energy Efficient Routing in Wireless Sensor Network Based on Composite Fuzzy Methods

Optimization of energy consumption in the batteries of a sensor node plays an essential role in wireless Sensor networks (WSNs). The longevity of sensor nodes depends on efficiency of energy utilization in batteries. Energy is consumed by sensor nodes in WSNs to perform three significant functions namely data sensing, transmitting and relaying. The battery energy in WSNs depletes mainly due to sampling rate and transmission rate. In the present work, the most important parameters affecting the longevity of network are indentified by modeling the energy consumption. The parameters are expressed as a fuzzy membership function of variables affecting the life time of network. Fuzzy logic is used at multiple levels to optimize the parameters. Network simulator-2 is used for experimentation purpose. The proposed work is also compared with the existing routing protocols like Enhanced Low Duty Cycle, Threshold Sensitive Energy Efficient Sensor Network and Distributed Energy Efficient Adaptive Clustering Protocol with Data Gathering. The proposed solution is found to be more energy efficient and hence ensures longer network lifetime.

Cluster Head Monitoring Nodes Secret Selection Protocol in Sensor Networks

In sensor networks, as the data collection point of nodes in the cluster, cluster head is easy to be the key target of attack. Once the cluster head node is controlled by the attacker, it will have a fatal impact on the security of the network. In order to solve the above problem, this paper proposes a cluster head monitoring nodes secret selection protocol. It uses secret selection algorithm to select the cluster head monitoring node from the neighbor nodes of the cluster head node. Thus, the attacker can not confirm the monitoring node, it results that the cluster head node can not be attacked. The protocol uses the spatio-temporal correlation between cluster head and cluster head monitoring nodes. Once it finds that cluster head node is suspected anomaly, it reports immediately to sink node. The analysis of protocol security shows that the protocol can meet the energy efficiency of sensor network and ensure the security of the system.

Optimization of the LEACH Protocol to Increase Stability on the Wireless Sensor Network

Wireless sensor networks (WSNs) are small devices that are run by batteries as minimal energy resources. The cluster method is one of the ways on WSN that is widely applied to the routing protocols to extend the network's lifespan. Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based protocol proven to increase energy efficiency in sensor networks. However, LEACH also has disadvantages such as a selection of cluster heads that don't consider residual energy at every node. It is the cause of failure in the election process and affects the stability of the network cluster. Period network stability is essential as reliable feedback from a network. Therefore, this study tries to investigate and solve stability problems in the LEACH algorithm. In this paper, the authors propose a new protocol that is Optimization-LEACH (O-LEACH), which is the development of the LEACH protocol. The O-LEACH protocol cluster-head selection process is developed based on the initial energy of each node, and a new beginning energy levels calculated from each node to every round. The test results show that the proposed O-LEACH protocol has a better stability period and network lifetime than the LEACH protocol.

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

SummarySensor node energy conservation is the primary design parameters in wireless sensor networks (WSNs). Energy efficiency in sensor networks directly prolongs the network lifetime. In the process of route discovery, each node cooperates to forward the data to the base station using multi‐hop routing. But, the nodes nearer to the base station are loaded more than the other nodes that lead to network portioning, packet loss and delay as a result nodes may completely loss its energy during the routing process. To rectify these issues, path establishment considers optimized substance particle selection, load distribution, and an efficient slot allocation scheme for data transmission between the sensor nodes in this paper. The selection of forwarders and conscious multi‐hop path is selected based on the route cost value that is derived directly by taking energy, node degree and distance as crucial metrics. Load distribution based slot allocation method ensures the balance of data traffic and residual energy of the node in areal‐time environment. The proposed LSAPSP simulation results show that our algorithm not only can balance the real‐time environment load and increase the network lifetime but also meet the needs of packet loss and delay.