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With the development of semiconductors and the resulting effect on manufacturing costs, wireless interface platforms have become increasingly powerful and popular. This has resulted in widespread applications ranging from daily life activities to military services. In large-scale applications of wireless sensor networks such as military surveillanc...
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In most of the real-life application scenarios, where Wireless Sensor Networks (WSNs) are deployed, the energy is a limited and valuable resource. Many energy-based strategies
were proposed in the literature to reduce the power consumption of sensor nodes and thus enhance the network lifetime. When those algorithms are developed and tested with som...
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Energy efficiency is the key requirement to maximize sensor node lifetime. Sensor nodes are typically powered by a battery source that has finite lifetime. Most Internet of Thing (IoT) applications require sensor nodes to operate reliably for an extended period of time. To design an autonomous sensor node, it is important to model its energy consum...
Most of Wireless Sensor Networks researches focus on reducing the amount of energy consumed by nodes and network to increase the network lifetime. Thus, several papers have been presented and published to optimize energy consumption in each area of WSNs, such as routing, localization, coverage, security, etc. To test and evaluate their propositions...
Increasing the lifespan of a group of distributed wireless sensors is one of the major challenges in research. This is especially important for distributed wireless sensor nodes used in harsh environments since it is not feasible to replace or recharge their batteries. Thus, the popular low-energy adaptive clustering hierarchy (LEACH) algorithm use...
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... By improving energy efficiency and minimizing risks, accurate energy consumption predictions play a vital role in enhancing the scalability and reliability of UAV operations. [4]- [5]. ...
Unmanned Aerial Vehicles (UAVs) often face challenges that eventually occur with their power consumption, this is because UAVs have small battery capacity and continuous operating systems. To overcome this uncertainty, accuracy in predicting power consumption is needed so that UAVs can fly for a longer time. This study explores the prediction of UAV energy consumption using four different deep learning models, such as Long LSTM, GRU, LSTM-SA and GRU-SA. The results show that the model incorporating self-attention mechanisms, especially GRU-SA, significantly outperforms the other models, achieving the lowest MAE (0.0343), RMSE (0.0567) and MSE (0.0032). Self-attention improves prediction accuracy by focusing on important input features during dynamic transitions. This work highlights a strong foundation for improving UAV energy consumption.
... An important metric of network performance is the network's dependability [32,33]. The connection C1 between the end nodes, the network connectivity rate C2, and the network capacity C3 are three factors that may be used to assess the dependability of Rs. ...
... The connection C1 between the end nodes, the network connectivity rate C2, and the network capacity C3 are three factors that may be used to assess the dependability of Rs. Formula (19) equals [34]: An important metric of network performance is the network's dependability [32,33]. The connection C 1 between the end nodes, the network connectivity rate C 2 , and the network capacity C 3 are three factors that may be used to assess the dependability of Rs. ...
In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm’s sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population’s variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person’s location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network’s average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.
... Thilagavathi et al. developed the dynamic-cluster-chosen scheme to increase the network lifetime. 19 The monitoring zone was divided in polygonal shaped clusters. The CH was selected based on probability and remaining energy. ...
Maintaining high residual energy is one of the major challenges of wireless sensor networks (WSNs), where nodes are moving at a random speed. In addition, data availability is also a major concern that must be maintained constantly during the packet transmission phase. In this research work, a Residual‐Energy‐Based Data Availability Approach (REDAA) for WSNs is developed to increase the network lifetime by focusing on the selection of stable routing paths and cluster heads. During the first phase of the work, the network model, and assumptions for route creations are adopted to effectively initialize the data transmission phase. During the second phase, a cluster was formed, and two categories of cluster heads were created based on quadrature low energy adaptive clustering hierarchy (Q‐LEACH) and multi‐hop low energy adaptive clustering (MH‐LEACH) algorithms to formalize the route and to make data available whenever requested. During the third phase, energy conservation routes are initialized, and data gathering is improved through slot‐based code division multiple access schemes. Simulation results demonstrate that the proposed REDAA approach can achieve an improvement in terms of throughput and energy consumption by 37% and 70% respectively when compared to MH‐LEACH approach, whereas in comparison to Q‐LEACH approach, these improvements are found to be 30% and 73% respectively.
... The recent state-based scheduling scheme is Coverage and Energy Strategy for WSNs (CES) [26] is another enhancement, where two states and four conditions play a vital role in changing the state. Active nodes change their location information, and neighbours change their identities. ...
Today is the era of super-connectivity, where real-world things connect, gather real-time data from their surroundings and disseminate the recorded data into the environment. The users can access services without understanding the basic composite structure of heterogeneous devices and hybrid IoT infrastructure. Data is collected, managed, and processed by minute and plug-able sensors for the IoT paradigm. Due to the resource-constrained nature of these sensors, massive and recurrent tasks create congestion in the network and drain the energy of sensors. Sending unnecessary and redundant data packets is life-threatening and affects the availability of other resources. This paper proposes a novel scheme, an ”Energy-Efficient Dynamic and Adaptive State-based Scheduling” (EDASS) for Wireless Sensor Network. The suggested method switches nodes between states dynamically and adapts to new states based on the contents of sensed data packets. Four distinct states of energy are derived from a combination of internal modules of the sensor. The typical sequence of operation is abruptly changed, and all sensors become active when a new event occurs. EDASS decreases energy consumption by 29% in live nodes, 41% in message overhead and 33% in the cluster head selection process. At the same time, the average delay in EDASS increases from 1.26 ms to 1.39 ms due to control message overhead.
... The disadvantages of this method are high energy consumption and as a result low network lifetime. In [22], a method called coverage and energy strategy has been proposed for WSNs. In this method, each sensor node has four modes including INITIAL, WORKING, SLEEPING and CHECKING. ...
Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption.
... It minimizes the redundant node. Thus SPEC removes unnecessary transitions by scheduling strategy and prevents the unwanted eligibility executions (Chenaita et al. 2017;Gu et al. 2012;Le and Jang 2015;Mini et al. 2012;Mishra et al. 2015;Pichamuthu and Peri-asamy2016;Tian and Georganas 2003). ...
Heterogeneous wireless sensor network (HWSN) is a network which contains sensor nodes with dissimilar capabilities, such as varying energy, sensing range etc. In Heterogeneous wireless sensor network, Coverage along with connectivity is one of the major issues. Coverage with no connectivity is meaningless in wireless sensor networks. In most of the existing work, coverage and connectivity is not provided efficiently for HWSN. The existing methods used in HWSN to attain coverage and connectivity are static and not applied in the dynamic environment. Optimal Coverage and connectivity maintenance not provided. Energy consumption should be concentrated to maximize the network lifetime. Preserving network connectivity at the same time maximizing coverage by using the less number of energy constrained sensor nodes is the most serious problem. To overcome these issues, in this work we put forward a technique called Coverage Scheduling and Power Aware Connectivity (CSPAC). Grouping the sensors into cluster by the strategy of Dynamic cluster formation. Identifying the minimum active set to provide Q-coverage by Artificial Bee Colony Optimization technique and maximize lifetime by mapping Power Aware Scheduling and Battery Discharge Value. Then Optimal Connectivity Management Technique for reachability to provide strong connectivity. By simulation results, we give you an idea that the proposed technique enhances the network coverage and connectivity in HWSN.
... Algorithms for self-organization of sensor networks have been proposed [22,23] to minimize the risk of data loss during transmission and to maximize the battery life of individual sensors. Various coverage optimization protocols have been studied [24,25] where a number of sensor nodes are deployed to ensure adequate coverage of a region. Using a coverage optimization protocol, nodes with overlapping sensing areas are turned off to reduce energy consumption. ...
Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems’ parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with n (n>2) types of flows.
... Algorithms for self-organization of sensor networks have been proposed [22,23] to minimize the risk of data loss during transmission and to maximize the battery life of individual sensors. Various coverage optimization protocols have been studied [24,25] where a number of sensor nodes are deployed to ensure adequate coverage of a region. Using a coverage optimization protocol, nodes with overlapping sensing areas are turned off to reduce energy consumption. ...
Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems' parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with n (n > 2) types of flows.
... This algorithm only activates sleepy nodes when an active node is near to drain completely. Coverage and Energy Strategy for WSNs (CES) [45] is working in two stages and each node can adapt four conditions. In first stage, all nodes are working and interchange their location information to other neighbors. ...
Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as ''The Internet of Things''. WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network lifetime , but also affects the availability of these resources for other tasks. To increase lifetime and provide an energy-efficient WSN, here we have proposed a new scheme called ''A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs''. CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network lifetime by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes. INDEX TERMS Adaptive, dynamic, wireless sensor networks (WSNs), two-way communication, analyzer, scheduling.
... This algorithm only activates sleepy nodes when an active node is near to drain completely. Coverage and Energy Strategy for WSNs (CES) [45] is working in two stages and each node can adapt four conditions. In first stage, all nodes are working and interchange their location information to other neighbors. ...
Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as “The Internet of Things”. WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network life-time, but also affects the availability of these resources for other tasks. To increase life-time and provide an energy-efficient WSN, here we have proposed a new scheme called “A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs”. CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network life-time by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.
