An area coverage algorithm for wireless sensor networks based on differential evolution

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.

A novel differential evolution based clustering algorithm for wireless sensor networks

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.

A Wireless Sensor Network (WSN) represents a set of deployed sensors in an area to track or monitor specific activities. Sensors are utilized to gather certain data and forward it to the sink or a Base Station (BS). Sensors are operated by limited-energy batteries. Optimizing the consumed energy will prolong the WSN's lifetime. One approach to reducing the consumed energy is by applying a cluster-based routing protocol. Optimizing the Cluster Head (CH) selection represents another approach to prolonging the WSN lifetime. This paper proposes an optimization algorithm to select the CH. The residual energy, the distance between sensors, and the centrality of the sensor node are used as significant features in selecting the CHs in this paper. The proposed algorithm is based on Particle Swarm Optimization (PSO) algorithm and K-Means algorithm. This study proposed approach is called the PSO-K algorithm. It helps in reducing energy consumption and increasing the network lifetime. MATLAB is used to model and simulate this developed algorithm. the number of sensors, number of rounds, number of CHs, the first dead nodes, the half-dead nodes, and the last dead nodes are used to evaluate the WSN performance. A comparison with other closely related works such as LEACH, EAMMH, and iLEACH shows that the PSO-K algorithm is the best in energy consumption and network lifetime.

Wireless sensor networks (WSNs) have been an emerging field in recent years. WSNs have major advantages in battlefields and emergency situations. However, due to the limited power of the sensor nodes, WSNs are restricted with their applications. The effective management of energy over the sensor nodes leads to good performance of the network. In this paper, we are proposing the energy-aware cluster head selection algorithm (ECHSA) for WSNs. The cluster head selection plays a major role in reducing the energy consumption of the cluster. To support our approach, the minimum energy transmission is proposed, and the simulation of the ECHSA is carried out with the NS-2 simulation. The results are compared with the sectored low energy adaptive cluster head (S-LEACH) and Theins algorithms. The results proved the efficiency of the proposed algorithm.

Wireless sensor network (WSN) is usually deployed in harsh environments for collecting and delivering the data to the remotely located base station. Due to the recent achievements in WSN, tiny and expensive sensors are used with capability of sensing large information and to propagate over longer distances. In range-based localization algorithms, anchor node whose location is known plays an important role. In order to resolve the challenges of traditional localization algorithms, an anchor-based node localization algorithm is proposed for WSN. The distance between anchor or known node and unknown node is measured by optimizing anchors and creating database of optimized anchors. Using trilateration method unknown nodes are located and are used as new anchors which will reduce localization algorithm’s dependency on anchors. The objective of the proposed work is to maximize the life time of the network by minimizing the energy consumption. From the simulation results, we show that the proposed algorithm increases lifetime of wireless sensor network.

The GA(genetic algorithms) was applied to find routing trees with minimal maximal sensor load thus extend network lifetime in wireless sensor networks. However, the existing algorithmlimits search space of GA to avoid generating cycles by dividing sensors into layers and stipulating that a sensor only can select a sensor in its lower layer as its new parent. In this paper, we have found that how cycles are generated during GA operations, and proposed a solution based on subtree to avoid generating cycles during GA operations.The solution only requires a sensor not to change its parent to a sensor in the subtree with it as the root. And layers are no longer considered. As a result, GA has more search space and GA operations can be performed more freely. Thus more optimal routing trees are obtained and network lifetime is extended. The experiment shows that our proposed algorithm extend network lifetime notably.

Limited energy supply (battery-powered) is a crucial problem in wireless sensor networks (WSNs). Sensor node placement schemes and routing protocols are mostly proposed to address this problem. In this paper, we first present how to place sensor nodes by use of a minimal number of them to maximize the coverage area when the communication radius of the sensor node is different from the sensing radius, which results in the application of regular topology to WSNs deployment. With nodes placed at an equal distance and equipped with an equal power supply, the problem of unbalanced energy consumption in 2-D regular topologies becomes more severe and much more difficult to tackle than that in 1-D chains, though the latter is known as an already quite hard problem. We address this problem and propose an adaptive data collection scheme by employing different communication radii for nodes in different locations to balance the energy consumption in WSNs. In order to achieve the ultimate goal of maximizing network lifetime in grid-based WSNs, we give a mathematical formulation, which shows the problem of maximizing network lifetime is a nonlinear programming problem and NP-hard even in the 1-D case. We discuss several heuristic solutions and show that the halving shift data collection scheme is the best solution among them. We also generalize the maximizing network lifetime problem to the randomly-deployed WSNs, which shows the significance of our mathematical formulation for this crucial problem in WSNs.

Moving the sink node is an effective solution for improving the lifetime of wireless sensor networks (WSNs). This paper provides another solution to the sink mobility problem in WSNs by incorporating node-load parameters into a matrix based on alternative sink positions and using this matrix to determine the next sink site to visit in each round.We first present a packet-load based sink movement algorithm that considers how many packets each node would receive and transmit for each different sink position. We extend this algorithm by also considering the distances the packets are transmitted, and in this manner obtain an energy-load based algorithm. We also provide an integer programming (IP) model to compute the optimal results. Our extensive simulations show that our packet-and energy-load based algorithms significantly improve network lifetime without introducing much overhead and only remain around 5% behind the optimal solution. Copyright © 2017 Inderscience Enterprises Ltd.

Connected dominated subsets (CDS) of nodes in wireless sensor network are always selected to form a virtual backbone. Construction of r-hop 2-connected dominating sets can reduce the size of CDS and the total amount of information to be transmitted. The 2-connected properties can provide higher fault tolerance and robustness as well. In this paper an approximation r-hop 2-connected dominating set algorithm for wireless sensor and ad-hoc networks is presented. The main strategy is partition to three phases: in the first phase, an r-hop independent dominating set is established; in the second phase, the r-hop dominating set is interconnected successively to form a connected subgraph; in the third phase, more vertices are selected and added into dominating set to make it 2-connected. Extensive simulations according to the algorithm steps have been conducted. Performance with detailed analysis has been shown at last.

Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems has increased greatly. Differential Evolution (DE) belongs to the group of evolutionary algorithms which operate in continuous search spaces. DE has been successfully applied to many stationary problem domains. Recently there has been some research into applying DE to dynamic optimization problems too. Many real-world problems consist of decision variables which require the optimization algorithm to work with binary parameters. This makes it impossible to apply DE in its basic form. For this purpose, binary differential evolution (BDE) approaches have been introduced. The main focus of this paper is to perform a series of experiments to test the behavior of a simple BDE under different change conditions. A simple bit-matching problem is chosen as the test environment. The results of this preliminary study show that further study is needed to make BDEs suitable to work in dynamic environments.

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.

Maximize coverage and prolong the network lifetime for wireless sensor networks has become one of the key topics of research. For this, put forward a kind of node scheduling strategy based on covering algorithm. The algorithm by Poisson distribution model structure node density formula, according to the node density formula in the monitoring region deploying to effective coverage; On the other hand, through the node state scheduling mechanism updates as well as to the neighbor node, can be made of residual energy of nodes and monitoring the area sensor the node energy consumption balance, so as to prolong the network lifetime goal. The simulation results show that, the algorithm cans not only useless nodes completely too effective coverage, improve the coverage while optimizing the cyber source configuration, prolong the network lifetime.

Division of the entire wireless sensor network geographic area into number of equal sized regions by applying virtual grid and strategy to appoint head node of the cluster in each region improves stability, consistency, and lifetime of the wireless sensor network. Applying the same size of grid to the different size of wireless sensor networks will not give consistent performance. In this paper, we are presenting a protocol which will dynamically determine the size of the virtual grid, which will give optimal performance of any geographic sized wireless sensor network. The protocol also allows to place sink node to any location and not at some fixed specific location. Protocol discussed in this paper can also support multiple sink nodes. Protocol dynamically takes decision to transmit the data directly to the sink node or by using multi-hop transmission by considering distances between sensor nodes internally and sink node(s), which will optimize energy efficiency of the sensor nodes and increases the lifetime of wireless sensor network.

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.