Significant Improvement In Network Lifetime Research Articles

Hybrid fuzzy logic and gravitational search algorithm based routing for wireless sensor networks

Over the recent years, the wireless sensor network (WSN) has garnered significant attention from both researchers and the general populace. Its application in diverse environmental scenarios, including weather monitoring, temperature regulation, humidity tracking, and military surveillance, extends beyond conventional boundaries. WSNs consist of numerous nodes, each functioning as a sensor with the primary responsibility of data sensing. These nodes operate under constraints such as power, energy, efficiency, and deployment considerations. Moreover, the power and other resources cannot be replaced and renewed therefore prolonging the network lifetime has become the main aspect of WSNs. Energy aware routing play’s important role to ensure the efficient data transmission with minimal power consumption and ensures the prolonged network lifetime. In this work, we focus on optimizing the routing process therefore we present a hybrid model which uses fuzzy logic for path identification and gravitational search optimization (GSA) for efficient path selection. The fuzzy logic considers energy consumption, residual energy, distance and delay parameters to identify the most suitable path for data transmission. The experimental analysis shows a significant improvement in network lifetime, delay an aspect delivery.

Energy efficient routing protocol for enhancing the network lifetime in wireless sensor network

Wireless sensor networks (WSNs) confront significant challenges related to battery capacity, as sensor nodes operate on limited energy resources. To address this issue, low energy adaptive clustering hierarchy (LEACH) protocol is commonly employed for power management in WSNs. LEACH is commonly used for power management. Here, sensing region is divided into clusters and sectors, placing a gateway node at the center to minimize energy consumption during data transmission. It employs one-hop, two-hop, or three-hop pathways based on node proximity to the base station (BS) to optimize energy usage. Network performance is assessed using rounds, throughput, and energy usage. MATLAB simulations compare the proposed approach with dual layer LEACH (DL-LEACH) and LEACH, showing significant improvements in network lifetime. The proposed scheme outperforms LEACH by 515% and 347% for 20% and 50% node depletion, respectively. Compared to DL-LEACH, it extends network lifetime by 27% and 59% under similar scenarios. Sectoring, clustering, and multi-hop communication reduce energy consumption, enhancing network lifetime and addressing WSN challenges effectively.

Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN.

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

A Hybrid Optimization Algorithm on Cluster Head Selection to Extend Network Lifetime in WSN

Wireless Sensor Networks (WSNs) comprise of a number of sensor nodes that are capable of sensing and aggregating the data from the monitoring environment. However, the process of recharging the limited energy sensor node batteries are highly difficult during adverse situations. This limitation of sensor nodes greatly crumbles the network lifetime to a maximum degree and degrades the level of reliable data dissemination. In this paper, a Novel Individual Updating Strategies-based Hybrid Elephant Herding Optimization Algorithm (NIUS-HEHOA) is planned for facilitating energy balanced cluster head selection for the objective of extending the network lifetime. It included energy-aware optimization process during the clustering schemes, since it is considered as a solution to the significant NP complete optimization problem. It is propounded as a swarm intelligent algorithms are identified to be the most applicable candidate for energy optimization that leads to significant improvement in network lifetime. It is contributed to maintain the deviation between exploitation and exploration such that least potential sensor nodes are prevented from being chosen as cluster heads. The simulation experiments confirmed that the proposed NIUSHEHOA scheme is better than the benchmarked schemes in terms of alive nodes, dead nodes, residual energy, network lifetime and throughput.

Enhancing network lifetime with an improved MOD- LEACH

<p class="Abstracttext"><span>Wireless sensor network will be the most dominating field in future era. There are certain issues which wireless sensor network suffers from. The main concern with wireless sensor network is limited energy which directly impact on network lifetime. In this paper we modify the cluster selection procedure of MODLEACH. MODLEACH protocol use threshold value for selecting cluster head. Once a cluster head is selected, it retains its position until it bypasses the threshold limit. In Basic LEACH, it does not use any threshold value but it randomly selects cluster head from the available nodes. We combine the probabilistic nature of LEACH to select the cluster head and threshold base selection of cluster head of MODLEACH. We also apply proposed modification in EAMMH protocol. Our main focus is on the enhancement of network lifetime, and we got significant improvement in network lifetime.</span></p>

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks.

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning

Generally, mobile wireless sensor network (MWSN) senses the sensitive and typical kind of events in various application areas along with the frequent mobility of sensor nodes as compared to traditional wireless sensor network. Due to mobility feature, MWSN extended the working of WSN. To design MWSN, certain key factors like energy efficiency, mobility, data routing, localization and charging strategy are involved. Mobile sensor nodes consume extra amount of energy due to mobility along with sensing and data routing task which exhaust network lifetime rapidly. In this research study, authors have proposed two wireless chargers (fixed and mobile) for sensor nodes. The main difference between fixed and mobile wireless charger is: fixed wireless charger invites mobile sensor nodes for wireless charging at base station whereas mobile wireless charger travels to the position of mobile sensor nodes for wireless charging. Both chargers fulfilled the charging request of sensor nodes efficiently. This is the first research study to propose such type of charging strategy for mobile wireless sensor network (MWSN) as per the available literature. The RL (reinforcement learning) technique is used here for optimization purpose of charging cost. These wireless chargers simulated in MATLAB enviorment and results showed that there is a significant improvement in network lifetime due to these charging strategies when compared to the existing algorithms.

Improving network lifetime and reliability for machine type communications based on LOADng routing protocol

With the advent of machine type communication (MTC) as part of the Internet of Things (IoT), there is an increasing need to address the challenges for the machine to machine (M2M) communications. Among the two routing protocols specifically designed for low-power lossy networks, RPL has limited support for M2M communications; whereas, LOADng can address the resource constraints of the nodes and demands for M2M communication. This paper proposes a new composite routing metric, LRRE, for LOADng to address the node congestion due to heavy traffic through the node and early node death. The LOADng with LRRE is implemented in ns-3 simulator. The simulation results show improved performance of LOADng with LRRE compared to that with the traditional hop count and residual energy metrics. We also propose a new multipath data forwarding scheme, namely, weighted forwarding (WF) to distribute the load evenly across the network. We extend the ns-3 simulation module for LOADng to multipath routing and integrate it with the newly proposed routing metric and multipath data forwarding technique. The simulation results show a significant improvement in network lifetime, packet delivery ratio, and energy utilization with the proposed LRRE metric and WF multipath data forwarding scheme. Analytical results are also obtained for simplified cases.

Energy-efficiency opportunistic spectrum allocation in cognitive wireless sensor network

The developments in wireless sensor network (WSN) that enriches with the unique capabilities of cognitive radio technique are giving impetus to the evolution of Cognitive Wireless Sensor Network (CWSN). In a CWSN, wireless sensor nodes can opportunistically transmit on vacant licensed frequencies and operate under a strict interference avoidance policy with the other licensed users. However, typical constraints of energy conservation from battery-driven design, local spectrum availability, reachability with other sensor nodes, and large-scale network architecture with complex topology are factors that maintain an acceptable network performance in the design of CWSN. In addition, the distributed nature of sensor networks also forces each sensor node to act cooperatively for a goal of maximizing the performance of overall network. The desirable features of CWSN make Multi-agent Reinforcement Learning (RL) technique an attractive choice. In this paper, we propose a reinforcement learning-based transmission power and spectrum selection scheme that allows individual sensors to adapt and learn from their past choices and those of their neighbors. Our proposed scheme is multi-agent distributed and is adaptive to both the end-to-end source to sink data requirements and the level of residual energy contained within the sensors in the network. Results show significant improvement in network lifetime when compared with greedy-based resource allocation schemes.

Performance Evaluation of Energy Efficient Clustering Protocol for Cluster Head Selection in Wireless Sensor Network

Wireless sensor network are emerging in various fields like environmental monitoring, mining, surveillance system, medical monitoring. LEACH protocol is one of the predominantly used clustering routing protocols in wireless sensor networks. In Leach each node has equal chance to become a cluster head which make the energy dissipated of each node be moderately balanced. We have pioneered an improved algorithm named as Novel Leach based on Leach protocol. The proposed algorithm shows the significant improvement in network lifetime .Comparison of proposed algorithm is done with basic leach in terms of network life time, cluster head selection, energy consumption, and data transmission to base station. The simulation results shows that proposed algorithm can reduce network energy consumption and prolong network life commendably. Simulation of our protocol is done with Matlab.

Distributed lifetime-maximized target coverage game

Wireless sensor nodes are usually densely deployed to completely cover (monitor) a set of targets. Consequently, redundant sensor nodes that are not currently needed in the covering task can be powered off to conserve energy. These sensors can take over the covering task later to prolong network lifetime. The coverage problem, concerns picking up a set of working sensors that collectively meet the coverage requirements. The problem is complicated by the possibility that targets may have different coverage requirements while sensor nodes may have different amounts of energy. This article proposes a game-theoretic approach to the coverage problem, where each sensor autonomously decides its state with a simple rule based on local information. We give rigorous proofs to show stability, correctness, and efficiency of the proposed game. Implementation variants of the game consider specific issues, such as game convergence time and different amounts of sensor energy. Simulation results show significant improvement in network lifetime by the proposed approach when compared with representative alternatives.

Maximum lifetime broadcast communications in cooperative multihop wireless ad hoc networks: Centralized and distributed approaches

We investigate the problem of broadcast routing in energy constrained stationary wireless ad hoc networks with an aim to maximizing the network lifetime measured as the number of successive broadcast sessions that can be supported. We propose an energy-aware spanning tree construction scheme supporting a broadcast request, considering three different signal transmission schemes in the physical layer: (a) point-to-point, (b) point-to-multipoint, and (c) multipoint-to-point. First we present a centralized algorithm that requires global topology information. Next, we extend this to design an approximate distributed algorithm, assuming the availability of k-hop neighborhood information at each node, with k as a parameter. We prove that the centralized scheme has time complexity polynomial in the number of nodes and the distributed scheme has a message complexity that is linear in the number of nodes. Results of numerical experiments demonstrate significant improvement in network lifetime following our centralized scheme compared to existing prominent non-cooperative broadcasting schemes proposed to solve the same lifetime maximization problem in wireless ad hoc networks. Due to lack of global topology information, the distributed solution does not produce as much advantage as the centralized solution. However, we demonstrate that with increasing value of k, the performance of the distributed scheme also improves significantly.

A New Design Scheme for a Disperse Two Tiered Wireless Sensor Network

In energy constrained Wireless Sensor Networks (WSN), network lifetime is considered as one of the most important metrics from the design point of view. In our paper, we have addressed the impact of a disperse node on network lifetime and have proposed a new design approach, Lifetime Enhancement for Disperse Networks (LEDN) to reduce its effect on a two tiered WSN. A mathematical analysis has also been presented in our paper to discuss the impact of a disperse node on network lifetime. Simulation studies confirm the significant improvement in network lifetime using our proposed design scheme.

EAMTR: energy aware multi-tree routing for wireless sensor networks

IEEE 802.15.4 is the prevailing standard for low-rate wireless personal area networks. It specifies the physical layer and medium access control sub-layer. Some emerging standards such as ZigBee define the network layer on top of these lower levels to support routing and multi-hop communication. Tree routing is a favourable basis for ZigBee routing because of its simplicity and limited use of resources. However, in data collection systems that are based on spanning trees rooted at a sink node, non-optimal route selection, congestion and uneven distribution of traffic in tree routing can adversely contribute to network performance and lifetime. The imbalance in workload can result in hotspot problems and early energy depletion of specific nodes that are normally the crucial routers of the network. The authors propose a novel light-weight routing protocol, energy aware multi-tree routing (EAMTR) protocol, to balance the workload of data gathering and alleviate the hotspot and single points of failure problems for high-density sink-type networks. In this scheme, multiple trees are formed in the initialisation phase and according to network traffic, each node selects the least congested route to the root node. The results of simulation and performance evaluation of EAMTR show significant improvement in network lifetime and traffic distribution.