MST-based clustering topology control algorithm for wireless sensor networks

In real-world applications, wireless sensor networks often consist of a large number of sensor nodes with constraint battery resources. How to reduce the power consumption of sensor nodes and maximize the network life, becomes the most important goal of topology control schemes in wireless sensor networks. During the operation of networks, sensor nodes may spend different levels of energy, and result in the uneven distribution of residual energy of sensor nodes. In order to extend the network life, it is essential to adjust the network burden of sensor nodes dynamically, so as to achieve energy balance among nodes under the consideration of different energy levels at nodes. In this paper, we introduce the game theory and the concept of game potential. By synthetically considering the factors of the residual energy and transmission power of nodes, a potential game based mathematical model of topology control is constructed. We prove the existence of Nash equilibrium. Through designing a payoff function, which takes into account both network connectivity and energy balance of nodes, the connectivity of sensor networks can be maintained while the power of sensor nodes is reduced. By increasing the average value of residual energy of neighbors, it enables to select nodes with more energy that reserves in neighborhood as neighbors, to improve the energy balance among nodes. Based on that, a distributed energy-balanced topology control algorithm (DEBA) is proposed. Theoretical analysis proves that the algorithm can maintain network connectivity. Compared with other existing game theory based algorithms DIA and MLPT, the topologies formed by the proposed algorithm have fewer bottleneck nodes which feature heavy traffic load and low residual energy, and smaller variance of node residual energy, thus achieving a longer life.

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.

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.

Radio transmission and reception consumes a lot of energy in a wireless sensor network (WSN), which are made of low-cost, low-power, small in size, and multifunctional sensor nodes. Thus, one of the important issues in wireless sensor network is the inherent limited battery power within the sensor nodes. Therefore, battery power is crucial parameter in the algorithm design in maximizing the lifespan of sensor nodes. It is also preferable to distribute the energy dissipated throughout the wireless sensor network in order to maximize overall network performance. Much research has been done in recent years in the area of low power routing protocol, but, there are still many design options open for improvement, and for further research targeted to the specific applications, need to be done. In this paper, we propose a new approach of an energy-efficient homogeneous clustering algorithm for wireless sensor networks in which the lifespan of the network is increased by ensuring a homogeneous distribution of nodes in the clusters. In this clustering algorithm, energy efficiency is distributed and network performance is improved by selecting cluster heads on the basis of (i) the residual energy of existing cluster heads, (ii) holdback value, and (iii) nearest hop distance of the node. In the proposed clustering algorithm, the cluster members are uniformly distributed and the life of the network is further extended.

A novel differential evolution based clustering algorithm for wireless sensor networks

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.

In recent years, vehicular ad-hoc networks (VANETs) have received considerable attentions. As a promising approach to future Intelligent Transportation System (ITS), VANET is capable of providing safety related applications, Internet accessing and various user applications for drivers and passengers. To support efficient data interaction among vehicles, clustering based topology can be applied which groups vehicle nodes in geographical vicinity together, supports direct interaction inside one cluster and inter-cluster data interaction through cluster heads (CHs). Both K-means and K-Harmonic Means (KHM) algorithms are commonly-used clustering algorithms for wireless sensor networks, however, these algorithms cannot be applied to VANETs directly due to the specific characteristics of VANETs. In this paper, we propose an improved KHM algorithms, called Adaptive K-Harmonic Means (AKHM) clustering algorithm for VANETs, which jointly considers the available bandwidth of candidate CHs, and relative distance and velocity between cluster members (CMs) and CHs. To perform the proposed algorithm, the initial values of the number of clusters and the positions of each centroids are chosen and the weighted distance between vehicles and centroids is defined, based on which the objective function can be formulated, and the optimal CHs and the association between CMs and CHs can then be determined. The simulation results demonstrate the efficiency of AKHM algorithm.

Most of the sensor nodes in a Wireless Sensor Network (WSN) have limited energy. In order to increase the network lifetime, some energy efficient algorithms were proposed earlier. It has been a challenge to design wireless sensor networks to enable applications for oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. The main objective of this work is to find out the data routing method such that the battery life of sensor nodes can be prolonged. In this paper, A Two Cluster Head Energy efficient Wireless Sensor Network (TCHE-WSN) Algorithm was put forward. WSN consists of sensor nodes which sense the physical parameters such as temperature, humidity, pressure and light etc and send them to a fusion center namely Base Station (BS) from where one can get the value of physical parameters at any time. Requirement of monitoring the environment might be anywhere, like middle of the sea or under the earth where man cannot go often to recharge the batteries which supplies the sensing device, transceiver and memory unit in the sensor node. So the usage of the battery power must be judicious in WSN. The present study demonstrates the data routing method from node to BS (Base Station) using two cluster heads to prolong the battery life of sensor nodes. The two cluster head analogy reduces the overhead of single cluster head, avoids packet collision, and improves reliable data transmission with little compromise in energy consumption and hop count compare to single cluster head method.

Wireless sensor networks (WSNs) are mainly used for systematic gathering of useful information and for the transmission of the gathered data to the base station for further processing. Despite the innumerable applications of WSNs, the sensor nodes of these networks have limited energy, limited computing power and limited bandwidth, in which energy is major constraint. Clustering has been widely used in WSNs to reduce energy consumption, which decrease the communication load, and thereby prolong the network lifetime by minimizing the redundant information using the data aggregation model on the cluster heads. In this paper, we have proposed an information similarity based clustering algorithm where transmission of the data to the sink is done using the least spanning tree algorithm. This approach firstly uses improved LEACH algorithm for election of cluster heads and then cluster formation under the elected cluster heads is done depending upon the non-cluster head node's maximum information similarity with the cluster head nodes. By utilizing the sleep scheduling & least spanning tree algorithm, this approach effectively reduces the redundant data transmission and whole energy consumed in the network. Simulation results show that network lifetime has increased effectively in comparison to other clustering algorithm such as LEACH.

A wireless ad hoc network is composed of several tiny and inexpensive device such as wireless sensor networks (WSNs) which have limited energy. In this network energy, efficiency is one of the most crucial requirements. Data transmitting in minimum power level is one way of maximizing energy efficiency. Thus, transmission power level of nodes should be managed in a smart way to improve energy efficiency. Topology control is one of the main algorithms used in a wireless network to decrease transmission power level while preserving network connectivity. Topology control could improve energy efficiency by reasonably tuning the transmission power level while preserving network connectivity in order to increase network capacity and lifetime. In pursuit of energy efficiency and connectivity, nodes can be selfish and are conflicting with each other. Therefore to overcome the conflict, game theory is used to construct energy efficient topology, as well as minimizing energy consumption. In this paper, the main goal and most recent energy efficient topology control algorithms in WSNs and ad hoc network are classified and studied according to their specific goals.

Lifetime and energy consumption are the main objectives of wireless sensor networks (WSNs). The existing methodologies have certain inhibitions which limit their applications. Clustering optimization is a major technique in any wireless sensor networks to optimize energy efficiency. Clustering technique assembles the objects of comparable shape in one shape. This technique is the excellent data assembly model for WSN, and it handles the redundant data within the network. The selection of the cluster head is an important feature in the clustering technique, then cluster heads cumulative the data and transmits to sink. Here fuzzy logic control algorithm has been proposed with hybridized particle swarm optimization algorithm and gravitational search algorithm to select cluster heads in WSN (FHPSOGSA). The algorithm is used to merge the constraints like remaining energy, node degree and distance to sink and select the best appropriate nodes as Cluster Head (CH). Simulation outcome displays that the proposed algorithm (FHPSOGSA) is establish to yield best results over other conventional algorithms.

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.

The main source of energy consumption in the wireless sensor network is due to transmission and receiving of data packets. However the cluster head (CH) near to the base station (BS) in cluster based wireless sensor network depletes energy faster and die quickly due to transmission of heavier data traffic of other CHs to the BS. In this paper we propose a particle swarm optimization (PSO) based approach which construct the routing path and distribute the routing load over the CHs near to the BS and maximize the lifetime of networks. We also consider the fault tolerance issue due to the sudden failure of CHs during data routing phase. The simulation results show the effectiveness of the proposed algorithm over the existing algorithms.

The topology of wireless sensor networks (WSNs) can be controlled through changing the transmission power of each node. We propose an adaptive energy efficient topology (AEET) algorithm where each node adaptively adjusts its transmission radius by local decisions about its residual power and optimal transmission radius. We give detailed AEET and analyses the communication radius in an ideal linear model and two cases of the adaptive topology control. The AEET mechanism and the topology control procedure are also given. AEET guarantees a suitable connected node set through interaction. The resulting network topology increases WSNs lifetime by adaptively adjusting transmission power. Through simulating AEET, the result proves AEET scheme is able to prolong lifetime.

Wireless sensor network (WSN) with mobile sink serves a lot of industrial and agricultural monitoring applications. The data collection with WSN has been popularly known in many applications such as smart environments, health monitoring, habitat monitoring, surveillance and tracking systems. The mobile sink moves to the corresponding cluster and gathers the data and moves to the next cluster head (CH) for the same. The data loss and waiting time of the data in the CH results from energy consumption and work repetition. This article provides a novel energy efficient data collection scheme. The data collection is done by the sink in a Polling based M/G/1 server model. The cluster member (CM) sends the data in N threshold model to the CH. The CH gathers the data from the CM and reports to sink once it arrives nearby. The energy efficient cluster head selection algorithm is evaluated with the proposed model and the waiting time is analyzed. The data arrival from the CM to the CH is considered as Poisson in nature. The sink mobility with respect to polling system is analyzed. The sink data collection is done through dynamic polling mechanism based on the CH arrival rate. The proposed algorithm outperforms the modified low energy adaptive clustering hierarchy and energy-aware multi-hop routing protocol (M-GEAR) protocols in terms of lifetime, waiting time and throughput. The proposed algorithm provides high resistance to energy hole and HOTSPOT problem.