Enhancing energy utilization for high power node multicasting in wireless sensor networks

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

In Wireless Sensor Networks (WSNs), the reduction of energy consumption in the batteries of a sensor node is an important task. Sensor nodes of WSNs perform three significant functions such as data sensing, data transmitting and data relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for sensing and transmitting the data. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Network simulator 2 is used for experimentation purpose. This work also compares with the existing routing protocols like Energy-efficient Low Duty Cycle (ELDC), Threshold sensitive Energy Efficient sensor Network (TEEN) and Adaptive clustering protocol. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.

Maximisation of network lifetime is the issue of prime importance in Wireless Sensor Network (WSN). An energy–efficient clustering algorithm with load balancing can achieve this challenging goal. We propose Density–based Dynamic Clustering (DDC) algorithm for clustering and cluster head election mechanism with the use of independence set. Furthermore, a distributed algorithm (DISD - Distributed Independence Set Discovery) is designed for cluster head election in O(1) complexity per sensor node. In this, we dynamically find an optimal sensor cover to dominate maximum sensors within a cluster, therefore forming an InDependence Set (IDS). Only the members of IDS are allowed to participate in the cluster head selection (rotation), thereby establishing load balancing and energy management. Our model is designed for dual modes: low– and high–traffic session. According to the flow of packets, the sleep management technique is modified. Network simulator is used for the simulation of our proposed algorithms.

The primary factor influencing the wireless sensor network (WSN) is the energy consumption of the sensor node. One of the key factors influencing WSN energy consumption is the high power consumption and packet delivery ratio needed for WSN processing. The suggested Energy Efficient Spherical Grid Routing (EESGR) protocol reduces the node's energy consumption to meet the requirements. To choose the cluster heads, the WSN is clustered into a collection of nodes using the pillar k-means clustering method defined in the proposed protocol. One optimization algorithm inspired by nature, the ant lion, is used to create cluster heads for assessing energy consumption in WSNs. The behavior concept of the ant lion is utilized for choosing the best nodes for the selection of the cluster head. The multi-tier spherical grid routing proposed in the paper is used to grid the cluster head generated by the ant-lion optimization algorithm to evaluate the total energy consumed for processing the sensor network. The overall performance of this method is evaluated in Network Simulator 2 (NS2). The proposed method improves performance in throughput, end-to-end delay, packet delivery ratio, and energy consumption compared to the existing techniques.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases[17]. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytical solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

Wireless Sensor Network (WSN) has many applications in day to day life. One of the applications of WSN is target tracking that uses sensors to track and localize the targets. This makes sensor node deployment in a network necessary and highly important. But the main challenge faced by sensor networks is the power consumption by sensor nodes. Hence an energy optimization technique must be in place that ultimately reduces the actual power consumption of the sensor nodes. Here, the shortest path algorithms are used in routing and it helps in reducing the energy consumption. The shortest path algorithms used in wireless sensor network are Dijkstra's algorithm and Floyd-Warshall's algorithm. They are also used for calculating throughput, packet delivery ratio and energy consumption for given number of nodes. The Floyd-Warshall's algorithm is not scalable and efficient for routing in WSN. Hence, we use modified Floyd-Warshall's algorithm that obtains better throughput, packet delivery ratio and minimized energy consumption. The new Modified Floyd-Warshall's algorithm calculates the shortest path from node to node by adding an acknowledgment mechanism. We have done a comparative analysis of Dijkstra's algorithm, Floyd-Warshall's algorithm, and modified Floyd-Warshall's algorithm for parameters like packet deliver ratio, throughput, and energy optimization for multiple nodes. From simulation results it is observed that the modified Floyd-Warshall's algorithm works better than Dijkstra's and Floyd Warshall's algorithm in terms of Packet Delivery ratio, throughput and energy consumption.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

SummaryEnergy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.

Energy efficiency in data collection and dissemination is a very important factor in wireless sensor networks. Therefore, minimizing energy consumption and maximizing network lifetime are the key factors for the design of wireless sensor network protocols. One of the energy conservation techniques is to select proper cluster head in a cluster. In this paper, a protocol has been proposed to select the sensor node as cluster head, in the sense that choosing the closest sensor node to low-energy nodes will result in lesser energy consumption and early death prevention of nodes. The simulation results have been shown that the proposed protocol could enhance the network lifetime.

In Wireless Sensor Networks (WSN), cluster networks structures have the advantage of providing scalable and energy-efficient solutions as the sensor nodes are highly energy-constrained.In this paper, we propose a dynamic clustering algorithm for WSN which is in application to event monitoring.In this clustering algorithm, the cluster head selection approach takes the received signal strength residual energy of node and distance between the cluster head and the sink node into consideration.Through this method, the cluster size is constrained and the energy consumption of intra-cluster communication is reduced.The simulation results show that the proposed algorithm can efficiently conserve energy and prolong the lifetime of WSN, especially when the sink node is far from the network.

SummaryWireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes.

As it is well known, in Wireless Sensor Networks, the sensor nodes will be either mobile or static. When mobility is concerned, on the whole network performance could be degraded, since the sensor nodes are furnished with restricted battery power, restricted memory, less computational ability and lower range of communication. So, a mechanism which is effective is needed there for forwarding the data packets with efficient energy management and coverage. With that note, the principle target of this work is to propose systematic method of CH selection based on the factors such as low mobility, density of the nodes and their remaining energy. Moreover, an innovative method called Node-Grade Based Clustering (NGBC) is proposed in this paper so as to select the CHs, studying the node’s energy and position regarding to their Base Station (BS), which will act as a sink for collected information. The CHs are replaced in every round based on its duty cycle on sensor nodes and Threshold Energy Rate (TER). Since the BS evaluates the quantity of every round a CH (Cluster Head) can sustain, it minimizes the quantity of energy consumed and increases the WSN’s lifetime. The results of the simulation demonstrate that the proposed algorithm attains higher coverage, efficiency in energy and network lifetime. Furthermore, the performance results in the work which is proposed, are distinguished with the algorithms proposed previously such as LEACH and HEED using some evaluation metrics like packet delivery ratio, throughput, energy consumption and end-to-end delay to prove the efficiency of energy efficient NGBC.

It is challenging to design a sustainable sensor network if sensors are battery powered. When designing a sensor network with a previously known area of deployment, finding the minimum number of sensors to maximize the network's lifetimes is a key issue. Sensor deployment is critical when we use sensors in security and military sensing, since we cannot reach the sensor field. In such cases we have no control over the distance from cluster head (CH) to sink node. Therefore, it is important to know the optimal number of sensors to be used. The main objective of this paper is to compute the optimal number of sensor nodes needed to design a sensor network sustainable for a given lifetime. This can vary according to the distance from cluster head to sink node. Therefore, we can choose the optimal number of sensors needed to get the maximum network lifetime according to the distance between the cluster head and sink node. Similarly, if we know the time period we need to get information, we can select the maximum number of sensors needed. This can save the total cost of the network, which is significant in wireless sensor networks.