Optimal Number of Nodes Deployment Method in Corona-Based WSN

Wireless sensor networks (WSNs) consist of several nodes with limited and non-rechargeable power resources. Therefore, energy efficiency and network lifetime depend on the utilize way of sensor nodes. Recently, some methods and strategies have been employed in this regard. Most of them could improve network lifespan to an acceptable level. Energy hole is one of inherent problems which can decrease the network lifetime to 89%. In multi-hop WSNs, the sensors located closer to sink must relay more data packets in comparison with other ones, thus their power supplies will be exhausted earlier than other nodes. Whereas, the sensor nodes belonging to other layers still have required energy for transmitting their data packets. This asynchronous energy depletion is considered as a problem. In this paper, we present a mathematical model for non-uniform node deployment for corona-based WSNs. According to results, Optimal Number of Nodes Deployment Method (ONNDM) enhance the network lifetime via balancing energy consumption and workload among coronas. In ONNDM, the optimum number of nodes in each corona is obtained by a mathematical formula, which can outperform other proposed strategies

Energy-efficient intra-cluster routing algorithm to enhance the coverage time of wireless sensor networks

Due to limited power supplies, the sensor nodes exhaust their energy eventually. Accordingly, data transmission mechanisms between nodes and their destinations need to be designed based on energy efficiency. Most of the existing cluster-based schemes adopt direct data transmission manner for intra-cluster communication, which leads to the unbalanced energy consumption of member nodes (MNs) and results in coverage holes in the network. Several mobile sink (MS)-based schemes focus exclusively on optimizing the sojourn locations of the MS to balance the energy consumption among sensor nodes. However, in most of them, the MS stays at only one location within a cluster which leads to the unbalanced energy consumption of MNs. Furthermore, the time limitation for balancing the energy consumption of nodes has not been considered in the previous schemes, which results in reduced network performance. In this paper, the Energy-Efficient Intra-cluster Routing (EIR) algorithm is proposed to balance the energy consumption of MNs during a limited time, which leads to enhancing the coverage time of the network. In the proposed scheme, upon arriving the MS to a cluster, the optimal sojourn locations are determined with respect to allocated sojourn time to the cluster in such a way that moving the MS along these locations leads to balancing energy consumption among MNs. Based on the simulation results, the EIR algorithm remarkably increases the network performance in terms of different performance evaluation metrics

Inter- and intra-cluster movement of mobile sink algorithms for cluster-based networks to enhance the network lifetime

Due to limited resources of the sensor nodes energy efficiency is an important performance metric to evaluate the wireless sensor networks (WSN). In cluster-based WSNs, the cluster heads (CH) located around base station (BS) consume more energy and exhaust their energy supplies faster than other ones which leads to energy hole problem and premature network death. Furthermore, single-hop transmission manner is usually applied for intra-cluster communication, therefore the member nodes (MN) located farther away from CHs die sooner than other MNs, which leads to coverage holes and reduce the network performance. In this paper, we propose mobile sink (MS) based inter- and intra-cluster routing algorithms. The first algorithm aims to solve the unbalanced energy consumption of CHs, which results in enhanced network lifetime. Likewise, the goal of the second algorithm is to balance the energy consumption of MNs, which results in improved coverage time of the network. In the proposed mechanism, MSs move along clusters and stay in each cluster for a limited sojourn time calculated via first algorithm. Then, the optimal sojourn positions of MSs in the clusters are determined via second algorithm. Simulation results reveal that the proposed algorithms enhance the network performance in terms of different performance evaluation metrics

A carrier velocity model for electrical detection of gas molecules

Nanomaterial-based sensors with high sensitivity, fast response and recovery time, large detection range, and high chemical stability are in immense demand for the detection of hazardous gas molecules. Graphene nanoribbons (GNRs) which have exceptional electrical, physical, and chemical properties can fulfil all of these requirements. The detection of gas molecules using gas sensors, particularly in medical diagnostics and safety applications, is receiving particularly high demand. GNRs exhibit remarkable changes in their electrical characteristics when exposed to different gases through molecular adsorption. In this paper, the adsorption effects of the target gas molecules (CO and NO) on the electrical properties of the armchair graphene nanoribbon (AGNR)-based sensor are analytically modelled. Thus, the energy dispersion relation of AGNR is developed considering the molecular adsorption effect using a tight binding (TB) method. The carrier velocity is calculated based on the density of states (DOS) and carrier concentration (n) to obtain I–V characteristics and to monitor its variation in the presence of the gas molecules. Furthermore, the I–V characteristics and energy band structure of the AGNR sensor are simulated using first principle calculations to investigate the gas adsorption effects on these properties. To ensure the accuracy of the proposed model, the I–V characteristics of the AGNR sensor that are simulated based both on the proposed model and first principles calculations are compared, and an acceptable agreement is achieved

Collaborative mobile sink sojourn time optimization scheme for cluster-based wireless sensor networks

Wireless sensor networks (WSNs) have grown excessively due to their various applications and low installation cost. One of the design challenges of the WSNs is to balance the energy consumption among sensor nodes, which results in enhanced network lifetime. In the past few years, several mobile sink (MS)-based schemes exclusively focus on determining the optimal sojourn time of MS to balance the energy consumption among cluster heads (CH). However, most of them are evaluated under unpredictable mobility pattern. Although they significantly improve the network lifetime, however, unpredictable mobility pattern imposes extra overheads on the network. Therefore, collaborative mobile sink sojourn time optimization (CMS2TO) scheme is proposed in this paper. The CMS2TO aims to optimize the sojourn time of MS in each cluster in order to achieve a balanced lifetime of CHs belonging to different layers of the network. The main contribution of the CMS2TO is to utilize a collaborative mechanism in order to determine the optimal sojourn time of MS in each cluster. In fact, in the proposed scheme, the CHs belonging to other layers cooperate in calculating the sojourn time of MS at the residence cluster. Based on experimental results, the proposed CMS2TO enhances the network performance in terms of different performance evaluation metrics

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs

Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network’s lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs