As the nodes of AWSN (Aerial Wireless Sensor Networks) fly around, the network topology changes frequently with high energy consumption and high cluster head mortality, and some sensor nodes may fly away from the original cluster and interrupt network communication. To ensure the normal communication of the network, this paper proposes an improved LEACH-M protocol for aerial wireless sensor networks. The protocol is improved based on the traditional LEACH-M protocol and MCR protocol. A Cluster head selection method based on maximum energy and an efficient solution for outlier nodes is proposed to ensure that cluster heads can be replaced prior to their death and ensure outlier nodes re-home quickly and efficiently. The experiments show that, compared with the LEACH-M protocol and MCR protocol, the improved LEACH-M protocol performance is significantly optimized, increasing network data transmission efficiency, improving energy utilization, and extending network lifetime.

Sensor-data gathering using multi-hop connections in a wireless sensor network is being widely used, and a tree topology for data gathering is considered promising because it eases data aggregation. Therefore, many sensor-tree-creation algorithms have been proposed. The sensors in a tree, however, generally run on batteries, so long tree lifetime is one of the most important factors in collecting sensor data from a tree over a long period. It has been proven that creating the longest-lifetime tree is a non-deterministic-polynomial complete problem; thus, all previously proposed sensor-tree-creation algorithms are heuristic. To evaluate a heuristic algorithm, the time complexity of the algorithm is very important, as well as the quantitative evaluation of the lifetimes of the created trees and algorithm speed. This paper proposes an algorithm called assured switching with accurate graph optimization (ASAGAO) that can create a sensor tree with a much longer lifetime much faster than other sensor-tree-creation algorithms. In addition, it has much smaller time complexity.

In this paper, the problem of lifetime extension of wireless sensor networks (WSNs) with redundant sensor nodes deployed in 3D vegetation-covered fields is modeled, which includes building communication models, network model and energy model. Generally, such a problem cannot be solved by a conventional method directly. Here we propose an Artificial Bee Colony (ABC) based optimal grouping algorithm (ABC-OG) to solve it. The main contribution of the algorithm is to find the optimal number of feasible subsets (FSs) of WSN and assign them to work in rotation. It is verified that reasonably grouping sensors into FSs can average the network energy consumption and prolong the lifetime of the network. In order to further verify the effectiveness of ABC-OG, two other algorithms are included for comparison. The experimental results show that the proposed ABC-OG algorithm provides better optimization performance.

Clustering is known to be an effective means of reducing energy dissipation and prolonging network lifetime in wireless sensor networks (WSNs). Recently, game theory has been used to search for optimal solutions to clustering problems. The residual energy of each node is vital to balance a WSN, but was not used in the previous game-theory-based studies when calculating the final probability of being a cluster head. Furthermore, the node payoffs have also not been expressed in terms of energy consumption. To address these issues, the final probability of being a cluster head is determined by both the equilibrium probability in a game and a node residual energy-dependent exponential function. In the process of computing the equilibrium probability, new payoff definitions related to energy consumption are adopted. In order to further reduce the energy consumption, an assistant method is proposed, in which the candidate nodes with the most residual energy in the close point pairs completely covered by other neighboring sensors are firstly selected and then transmit same sensing data to the corresponding cluster heads. In this paper, we propose an efficient energy-aware clustering protocol based on game theory for WSNs. Although only game-based method can perform well in this paper, the protocol of the cooperation with both two methods exceeds previous by a big margin in terms of network lifetime in a series of experiments.

Wireless sensor networks usually deploy sensor nodes with limited energy resources in unattended environments so that people have difficulty in replacing or recharging the depleted devices. In order to balance the energy dissipation and prolong the network lifetime, this paper proposes a routing spanning tree-based clustering algorithm (RSTCA) which uses routing spanning tree to analyze clustering. In this study, the proposed scheme consists of three phases: setup phase, cluster head (CH) selection phase and steady phase. In the setup phase, several clusters are formed by adopting the K-means algorithm to balance network load on the basis of geographic location, which solves the randomness problem in traditional distributed clustering algorithm. Meanwhile, a conditional inter-cluster data traffic routing strategy is created to simplify the networks into subsystems. For the CH selection phase, a novel CH selection method, where CH is selected by a probability based on the residual energy of each node and its estimated next-time energy consumption as a function of distance, is formulated for optimizing the energy dissipation among the nodes in the same cluster. In the steady phase, an effective modification that counters the boundary node problem by adjusting the data traffic routing is designed. Additionally, by the simulation, the construction procedure of routing spanning tree (RST) and the effect of the three phases are presented. Finally, a comparison is made between the RSTCA and the current distributed clustering protocols such as LEACH and LEACH-DT. The results show that RSTCA outperforms other protocols in terms of network lifetime, energy dissipation and coverage ratio.

This paper proposes a Watts and Strogatz-model based routing method for wireless sensor network along with link-exchange operation. The proposed routing achieves low data-collection delay because of hub-node existence. By applying the link exchanges, node with low remaining battery level can escape from a hub node. Therefore, the proposed routing method achieves the fair battery-power consumptions among sensor nodes. It is possible for the proposed method to prolong the network lifetime with keeping the small-world properties. Simulation results show the effectiveness of the proposed method.

Optimizing lifetime of a wireless sensor network has received considerable attention in recent years. In this paper, using the feasibility and simplicity of grid-based clustering and routing schemes, we investigate optimizing lifetime of a two-dimensional wireless sensor network. Thus how to determine the optimal grid sizes in order to prolong network lifetime becomes an important problem. At first, we propose a model for lifetime of a grid in equal-grid model. We also consider that nodes can transfer packets to a grid which is two or more grids away in order to investigate the trade-off between traffic and transmission energy consumption. After developing the model for an adjustable-grid scenario, in order to optimize lifetime of the network, we derive the optimal values for dimensions of the grids. The results show that if radio ranges are adjusted appropriately, the network lifetime in adjustable-grid model is prolonged compared with the best case where an equal-grid model is used.

In this paper, we present Greedy Routing for Maximum Lifetime (GRMax) [1],[2] which can use the limited energy available to nodes in a Wireless Sensor Network (WSN) in order to delay the dropping of packets, thus extend the network lifetime. We define network lifetime as the time period until a source node starts to drop packets because it has no more paths to the destination [3]. We introduce the new concept of Network Connectivity Aiming (NCA) node. The primary goal of NCA nodes is to maintain network connectivity and avoid network partition. To evaluate GRMax, we compare its performance with Geographic and Energy Aware Routing (GEAR) [4], which is an energy efficient geographic routing protocol and Greedy Perimeter Stateless Routing (GPSR) [5], which is a milestone among geographic routing protocol. We evaluate and compare the performance of GPSR, GEAR, and GRMax using OPNET Modeler version 15. The results show that GRMax performs better than GEAR and GPSR with respect to the number of successfully delivered packets and the time period before the nodes begin to drop packets. Moreover, with GRMax, there are fewer dead nodes in the system and less energy is required to deliver packets to destination node (sink).

A key challenge in developing energy-efficient sensor networks is to extend network lifetime in resource-limited environments. As sensors are often densely distributed, they can be scheduled on alternative duty cycles to conserve energy while satisfying the system requirements. Directional sensor networks composed of a large number of directional sensors equipped with a limited battery and with a limited angle of sensing have recently attracted attention. Many types of directional sensors can rotate to face a given direction. Maximizing network lifetime while covering all of the targets in a given area and forwarding sensor data to the sink is a challenge in developing such rotatable directional sensor networks. In this paper, we address the maximum directional cover tree (MDCT) problem of organizing directional sensors into a group of non-disjoint subsets to extend network lifetime. One subset, in which the directional sensors cover all of the targets and forward the data to the sink, is activated at a time, while the others sleep to conserve energy. For the MDCT problem, we first present an energy-consumption model that mainly takes into account the energy expenditure for sensor rotation as well as for the sensing and relaying of data. We also develop a heuristic scheduling algorithm called directional coverage and connectivity (DCC)-greedy to solve the MDCT problem. To verify and evaluate the algorithm, we conduct extensive simulations and show that it extends network lifetime to a reasonable degree.

In this letter, we discuss a forwarding method for maximizing network lifetime, which combines multi-hop forwarding and direct forwarding with a direct/multi-hop forwarding ratio of each sensor node. The direct forwarding ratio refers to the forwarding amount ratio of sensor nodes' own data directly towards a sink node in one packet/instance data generation rate. We tackle an optimization problem to determine the direct forwarding ratio of each sensor node, maximizing network lifetime, as well as nearly guaranteeing energy consumption balancing characteristics. The optimization problem is tackled through the Lagrange multiplier approach. We found that the direct forwarding ratio is overall inversely proportional to the increase of node index in h < i ≤ N case. Finally, we compare energy consumption and network lifetime of the proposed forwarding method with other existing forwarding methods. The numerical results show that the proposed forwarding method balances energy consumption in most of the sensor nodes, comparing with other existing forwarding methods, such as multi-hop forwarding and direct forwarding. The proposed forwarding method also maximizes network lifetime.

Energy efficient routing is one of the key design issues to prolong the lifetime of wireless sensor networks (WSNs) since sensor nodes can not be easily re-charged once they are deployed. During routing process, the routes with only few hops or with too many hops are not energy efficient. Hop-based routing algorithms can largely improve the energy efficiency of multi-hop routing in WSNs because they can determine the optimal hop number as well as the corresponding intermediate nodes during multi-hop routing process under medium or high density network. In this paper, we not only focus on studying the relationship between energy consumption and hop number from theoretical point of view but also provide a practical selection criterion of the sub-optimal hop number under practical sensor network so as to minimize the energy consumption. We extend the theoretical deduction of optimal hop number and propose our Hop-based Energy Aware Routing (HEAR) algorithm which is totally distributed and localized. Simulation results show that our HEAR algorithm can reduce the average energy consumption about 10 times compared to the direct transmission algorithm and 2 to 10 times than other algorithms like LEACH and HEED under various network topologies.

An energy-efficient clustering scheme to maximize the network lifetime is presented in IEEE 802.15.4 networks. In the proposed clustering scheme, even though the cluster is divided into several sub-clusters in order to decrease data redundancies, the sub-CH does not transmit the beacon frame due to the problem of beacon collision. Our clustering scheme also allows the CH to control the size of the sub-cluster according to the residual energy of the sub-CH. The performance of the proposed scheme is verified by simulations that demonstrate how our scheme provides a better network lifetime than the conventional scheme.

The recent progress in sensor and wireless communication technologies has enabled the design and implementation of new applications such as sensor telemetry which is the use of wireless sensors to gather fine-grained information from products, people and places. In this work, we consider a realistic telemetry application in which an area is periodically monitored by a sensor network which gathers data from equally spaced sample points. The objective is to maximize the lifetime of the network by jointly selecting the sensing nodes, the node transmission powers and the route to the base station from each sensing node. We develop an optimization-based algorithm OPT-RE and a low complexity algorithm SP-RE for this purpose and analyze their dynamics through extensive numerical studies. Our results indicate that SP-RE is a promising algorithm which has comparable performance to that of the more computationally intensive OPT-RE algorithm. The energy consumption is significantly affected by the channel access method, and in this paper, we also compare the effects of the collision free TDMA and contention based CSMA/CA methods. We propose practical enhancements to CSMA/CA so that the energy consumption due to collisions is reduced. Our simulation results indicate that with the proposed enhancements contention based channel access can provide comparable performance to that of the collision free methods.

Sensors have very scarce resources in terms of memory, energy and computational capacities. Wireless sensor network is composed of a large number of such sensor nodes densely deployed in inhospitable physical environments. Energy efficient information dissemination throughout such a network is still a challenge. Though dissemination of information with minimum energy consumption is a key concern in wireless sensor networks, it often introduces additional delay. In this work, we first propose an energy and delay efficient multi-hop routing scheme called C2E2S (Cluster and Chain based Energy*Delay Efficient Routing Scheme) for wireless sensor networks. This scheme is a combination of cluster-based and chain-based approaches and the way to form clusters and chains in this work is center-based approach. To reduce a large number of communication overheads due to this approach, we propose a modified-center-based approach called passive-BS-based approach. Next, we propose (1) an energy and delay aware routing algorithm for sensors within each k-hop cluster, and (2) an Energy-efficient chain construction algorithm for cluster heads. To evaluate the appropriateness of our approach, we analyze the evaluated performance against existing protocols in terms of communication overhead, the number of communication rounds (network lifetime), total amount of energy dissipated in the system over time, network delay and Energy*Delay metric using SENSE simulator. The simulation results show that C2E2S consumes less energy, balances the energy and delay metrics, and extends the network lifetime as compared to other approaches.