Coverage Optimization Algorithm of Wireless Sensor Network

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

In order to improve the location accuracy of unknown nodes in APIT algorithm in mobile wireless sensor networks, an improved APIT algorithm is proposed. A new test method called gravity center combination has been proposed for the InToOut and OutToIn errors in APIT. The algorithm further builds an anchor box by combining one-hop and two-hop information of nodes to limit the range and finally determine the location of unknown nodes to reduce the number of nodes that cannot be located. By improving the coverage and node availability, the positioning accuracy is eventually improved. The complexity is lowered by the lack of requirement of additional hardware, thus the improved algorithm is relatively easy to implement. The simulation results of the verification demonstrate that the updated algorithm improves the coverage ratio and the node usage efficiency compared with the other algorithm, thus makes the mobile node location more accurate.

The wireless sensor networks (WSNs) are usually deployed among widely area for environmental monitoring, data collection and other applications. In order to reduce the energy consumption and extend the lifetime of WSNs, as well as receive the data quickly, a source-based data aggregation algorithm (SBDA) for clustering wireless sensor networks is proposed. The method aggregated the data from sensor nodes and marked different transmission priority for the data. Simulation results show that, compared to existing protocols, the proposed method achieves a longer life time, and a lower packet drop ratio.

This paper proposes a novel deployment algorithm for wireless sensor networks namely VFIPO based on individual particle optimization (IPO) incorporated with virtual force (VF) algorithm, which overcomes the shortcomings of applying the VF algorithm. Simulation results verify that mobile nodes deployment with VFIPO outperforms the previous deployment algorithms such as VF, PSO, VFPSO and IPO based approaches with respect to effective coverage area and computation time.

For the node deployment problem in three-dimensional heterogeneous sensor networks, the traditional virtual force method is prone to local optimization and the parameters required for calculation are uncertain. A spatial deployment algorithm for 3D mobile wireless sensor networks based on weighted Voronoi diagram is proposed (TDWVADA) to solve the problem. Based on the positions and weights of all nodes in the monitoring area, a three-dimensional weighted Voronoi diagram is constructed. Next, the central position of each node’s the Voronoi region is calculated and the position is regarded as target position of the node movement. Each node moves from the original position to the target position to complete one iteration. After multiple iterations, each node is moved to the optimal deployment location and network coverage is improved. In view of the initial centralized placement of sensor nodes, the addition of virtual force factors is added to the TWDVDA algorithm. An improved algorithm TDWVADA-I was proposed. The algorithm enables nodes that are centrally placed to spread quickly and speeds up deployment. The simulation results show that TDWVADA and TDWVADA-I effectively improve the network coverage of the monitored area compared to the virtual force algorithm and the unweighted Voronoi method. Compared with the virtual force method, the coverage of TDWVADA has increased from 90.53% to 96.70%, and the coverage of TDWVADA-I has increased from 81.12% to 96.56%. Compared with the Voronoi diagram method, the coverage of TDWVADA has increased from 85.01% to 96.70%, and the coverage of TDWVADA-I has increased from 80.82% to 96.56%. TDWVADA and TDWVADA-I also greatly reduce the energy consumption of the network. Experimental results demonstrate the effectiveness of the algorithms.

Popularity of Mobile Wireless Sensor Network (MWSN) has increased tremendously due to the ability to connect the physical world with the virtual world. In MWSN, Sensor nodes move freely in a target area without the need of any special infrastructure. MWSN has attracted many researchers as it has introduced unique challenges in aspects like resource management, coverage, routing protocols, security in many applications such as target tracking in military applications, detection of catastrophic events, environment monitoring, health applications etc. Due to mobility, routing process in MWSN has become more complicated as connections in the network can change dynamically. This paper proposes a Mobility-Aware Multi-Objective Hybrid Routing Algorithm (MMOHRA) for Wireless Sensor Network (WSN). MMOHRA selects the best route and forwards the data packets from source to sink using multiple metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. Performance of MMOHRA is compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-objective Routing Algorithm (DyMORA) using metrics such as Average Energy Consumption, Packet Delivery Ratio, and Jitter. MMOHRA improves the Average Energy Consumption (AEC) by a factor of 14.1% as compared with SHRP and 8.13 % as compared to DyMORA. MMOHRA outperformed SHRP and DyMORA in terms of packet Delivery Ratio (PDR) by 10.17 % and 9.83 % respectively.

A large number of small sensors in wireless sensor network are battery-powered; one of the most important design criteria for this type of network is energy efficiency. Clustering provides an effective way for extending the lifetime of a sensor network. In this paper, we propose a maximum-votes and load-balance clustering algorithm (VLCA) for wireless sensor network Each sensor collects votes from their neighbors and calculates the total vote received. The more votes a sensor accumulates, the more important it is in the whole network During the clustering phase, sensors compete with each other based on the total votes each has received. The algorithm is completely distributed, locating-unaware and independent of network size and topology. Simulation results show that our VLCA can reduce the number of clusters by 20-50% and prolong the lifetime of a sensor network.

Recently, network localization systems that are based on inter-node ranges have received significant attention. Geographic routing has been considered an application which can utilize the location information from these localization systems. In this paper, we firstly recognize that sensor network localization algorithms generate positioning data with different error patterns compared to those networks where node positions are determined directly from GNSS measurements. Secondly, by simulating practical sensor network scenarios using data from our localization algorithm, we observe that existing geographic routing algorithms in wireless sensor networks (WSNs) adopt very simplistic methods in the treatment of position error, without due consideration of error distribution. Additionally, an insight is given into localization algorithms for WSNs with inhomogeneous error environments. Our observations represent an initial step toward a detailed understanding and design of efficient geographic routing algorithms in location aware WSNs.

Energy balance is an important performance index in the design of routing algorithm for wireless sensor networks. A swarm intelligence optimization based routing algorithm for wireless sensor networks is proposed, whose kernel idea is taking less hop numbers into consideration and choosing the nodes with less pheromone as next hop to avoid some nodespsila prematurely exhausting their energy because of too concentrated routes through the nodes. The experiments show that the algorithm proposed in this paper is better than the directed diffusion routing protocol both in end-to-end delay and global energy balance and can effectively balance the global energy consumption and prolong the network lifetime.

This paper proposes a particle swarm optimization algorithm for Wireless Sensor Network (WSN) path optimization. It designs and increases the mutation operator. This algorithm can find effective optimization of WSN routing, not only the solution quality is superior to genetic algorithm, but also increases in the success rate. In experimental results verified that proposed PSO-WSN intelligent method can escape from local minima, so has better convergence than GA-WSN. Meanwhile, it achieves also very high accuracy rate of recognition and thus provides support in classification.

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a while since its last discharging. Accordingly, the battery can work for a longer time when it takes some rest between consecutive discharging processes than when it works all the time. However, to the best of our knowledge, this impact has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm for wireless sensor networks. In this algorithm, each node in the network periodically decides to be in the dominating set or not. Nodes that have taken sleep in the preceding round are encouraged to involve in the dominating set in the current round while nodes that have worked in the preceding round are encouraged to sleep in the current round for battery recovery. Detailed design description is presented. The complexity of the proposed algorithm is deduced to be O(D2), where D represents node degree. Simulation results show that our algorithm can significantly improve the network lifetime performance as compared with existing work.

Energy is critical for typical wireless sensor networks (WSN) and how to energy consumption and maximize network lifetime are big challenges for Wireless sensor networks; cross layer algorithm is main method to solve this problem. In this paper, firstly, we analyze current layer-based optimal methods in wireless sensor network and summarize the physical, link and routing optimization techniques. Secondly we compare some strategies in cross-layer optimization algorithms. According to the analysis and summary of the current lifetime algorithms in wireless sensor network A cross layer optimization algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional Leach routing protocol. Simulation results show that this algorithm is an excellent cross layer algorithm for reducing energy consumption.

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.

Localization with mobile beacons is one of the most efficient ways to deploy sensor nodes and locate them. However, most of the current mobile beacon-assisted localization algorithms only discuss the application in rectangular sensor region. In order to achieve the localization in arbitrary sensor region, a novel mobile beacon-assisted localization algorithm based on virtual force and ant colony algorithm for wireless sensor networks is presented. Experimental results show that the proposed localization can achieve a proper balance between localization accuracy and energy consumption.