A new routing algorithm for energy reduction in wireless sensor networks

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self‐organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near‐optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

Routing in sensor networks is very challenging compared to contemporary communication and wireless ad–hoc networks. This is attributed to several characteristics that distinguish them from such networks. Transmitting data from every sensor node within the deployment region might result in significant and unnecessary redundancy in data and incur avoidable energy and traffic expenditure; thus, a routing protocol which is able to select a set of sensor nodes and utilise data aggregation during the relaying of data is of great importance. Furthermore, timely delivery of data is required in many applications of Wireless Sensor Networks (WSNs), such as real–time target tracking in battle environments, emergent event triggering in monitoring applications impose the need of efficient routing protocol. In this paper, we propose a data routing scheme which ensures increased network lifetime by exploiting energy efficiently, supporting reliability, fast delivery of delay–sensitive data and achieving low–cost sensor design. Simulation results demonstrate that, the proposed routing scheme outperforms related existing approaches in terms of all the evaluation metrics such as packet delivery ratio, energy efficiency and timely delivery of data.

In this study, a triangular fuzzy-based spectral cluster routing (TF-SCR) mechanism is proposed in order to improve network lifetime and reliability of data packet transmission in WSN with minimum routing overhead. The designed TF-SCR mechanism contains three steps, namely preprocessing, clustering and post-processing. TF-SCR mechanism initially carried out preprocessing where the residual energy and received signal strength of all sensor nodes in WSN is determined. After preprocessing, TF-SCR mechanism performs clustering process with the application of spectral clustering in which the sensor nodes in WSN are grouped based on residual energy level. After completing clustering process, TF-SCR mechanism implements post-processing step where triangular fuzzy membership function is applied to select the sensor node with higher residual energy and signal strength as cluster head for routing data packets to sink nodes in WSN. This helps for TF-SCR mechanism to obtain energy-efficient routing in WSN. Thus, TF-SCR mechanism enhances the network lifetime and reliability of data broadcasting in WSN as compared to state-of-the-art works. The simulation of TF-SCR mechanism is conducted on factors such as energy consumption, network lifetime, routing overhead and reliability with respect to a number of sensor nodes and data packets. The simulation results demonstrate that the TF-SCR mechanism is able to improve the network lifetime and also minimize the overhead of routing with minimal energy consumption as compared to state-of-the-art works.

In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications.Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station.But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime.In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network.The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station.GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value.Also, GCR uses a greedy route finding strategy for balancing energy consumption.Experimental results show that GCR achieves significant energy savings and prolong network lifetime.

Information processing and information routing in wireless sensor network is one of the most important key area of recent research. Building an optimal routing protocol for information routing is a tough task in dealing with the sensor nodes. Information exchange can be done with the major possible way in order to provide a secure communication and reliable data delivery, here in this study we focus on routing protocol with reliable delivery. Hence we proposed a new algorithm/protocol which optimally routes the data packets from source to destination with efficient utilization of energy resources. The proposed protocol is based on next hop graph node in terms of reliable data packet routing by framing the route in the form of graphs. The simulation results showed in the performance analysis phase denotes the proposed method outperforms the existing routing protocol in terms of reliability and robustness in efficient routing. The proposed methodology achieves the connectivity and connectivity path in WSN which is critical in WSN.

Energy and congestion-aware load balanced multi-path routing for wireless sensor networks in ambient environments

Energy efficiency is a critical problem for wireless sensor networks. In past decades, network coding has been proved as a promising technology to reduce the number of transmissions, save energy, and improve network throughput, especially suitable for the wireless network that motivates the emergence of network coding aware routings. However, existing network coding aware routings have the failure decoding problem, because of defective network coding conditions. On the other hand, existing network coding aware routings usually consider the ideal traffic scheduling and neglect nodes’ energy and load which are important to wireless sensor networks. Therefore, it is not appropriate to directly apply existing network coding aware routings in wireless sensor networks. Regarding the problems above problems, this paper proposes traffic-shaped network coding aware routing (TSCAR) for wireless sensor networks. In the TSCAR, a universal network coding condition is presented to solve the false decoding problem. And traffic shaping mechanism is proposed to shape the traffic of different flows to create more coding actions when coding opportunities exist. In addition, network calculus is used to analyze the delay of the TSCAR. Besides, a coding, load, and energy aware routing metrics are proposed to evaluate the discovered paths. Extensive simulation results demonstrate that the TSCAR increases the number of coding opportunities and the proportion of coded packets, improves network throughput, and extends the network lifetime of wireless sensor networks.

Wireless sensor network (WSN) has key constraint on energy of sensor nodes i.e. each nodes have finite amount of energy. Seeing energy as key constraint WSN must has robust energy optimized routing algorithm. Mostly routing approach use shortest path and minimal energy depletion model. Network operate on these routing scheme early partitioned due to unbalanced energy consumption of sensor nodes i.e. a single shortest route may used most of the time leads to faster consumption of energy of sensor nodes coming in route path. In current paper attempts has made to develop energy efficient routing scheme based on optimization techniques "particle swarm optimization (PSO) and genetic algorithm (GA)". We implemented all four routing scheme i.e. shortest path approach, PSO approach, GA approach and hybrid PSO-GA approach for small size as well as large size sensor network. Simulation results depicts hybrid PSO-GA routing scheme raised 12% to 23%, 8% to 15% and 5% to 13% lifetime compared to shortest path approach, PSO approach and GA approach respectively for large scale network. For large scale network hybrid PSO-GA raised 9% to 16%, 6% to 11% and 5% to 9% packet delivery ratio compared to shortest path approach, PSO approach and GA approach respectively.

Congestion severely affects the performance of a wireless sensor network in two aspects: increased data loss and reduced lifetime. This paper addresses these problems by introducing a mobile sink based routing scheme for congestion avoidance and energy efficient routing in wireless sensor networks. The proposed scheme utilizes the sink mobility and an in-network storage model that is used to set up mini-sinks along the mobility trajectory of the sink. Mini-sinks are responsible for collecting data from the sensor nodes located in their vicinity, thus avoiding data flow to a single data collection point, e.g., a static sink that is the major cause of congestion, data loss and reduced lifetime of the sensor network. Also, in the proposed scheme data only has to travel a limited number of hops to reach the nearest mini-sink which helps to improve the energy consumption of the sensor nodes. Through simulation we show the effectiveness of the given routing scheme in terms of congestion avoidance and increased lifetime of the wireless sensor network.

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design.

Wireless Sensor Networks (WSNs) is the vast area and developing very speedily now-a-days. This paper gives some of the major applications and critical limitations of WSNs like power and lifespan, communication bandwidth, memory size. Sensor nodes are working by node energy so the energy consumption is the essential issue in WSN. Routing in WSN consumes more energy than any other phase. Various routing techniques in WSNs are discussed and different routing protocols are illustrated. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the popular and feasible hierarchical clustering routing protocols in WSN. Working of LEACH is explained in brief. Furthermore, the descendants of LEACH e.g. LEACH-C (Centralized LEACH), LEACH-B (Balanced LEACH), LEACH-F, Multihop LEACH, MS-LEACH (Multihop and Single hop LEACH), MH-LEACH etc. are discussed in terms of the energy utilization. By analyzing the variants of LEACH, it is observed that the energy requirement in the cluster set up phase or data transmission phase is minimized to minimize the total energy consumption of the WSN. To elect the proper cluster head node, centralized approach is followed. Afterwards, data transmission will use the multihop communication technique. This will help to save some energy when base station is located far away from the cluster head node. Proposed protocol aims to decrease the energy overhead of the sensor nodes. The new approach is proposed entitled PV-LEACH which will save the energy consumption in cluster setup phase and also in data transmission phase of the conventional LEACH protocol. So, this will enhance the performance ratio in terms of energy consumption and life time of the sensor nodes.

Wireless Sensor Networks (WSN) are set of thousands or more micro sensor nodes that one of the most important constraints of them is their limited energy. Since these nodes have very small dimensions, so their needed energy is provided by small and limited energy batteries. Therefore, the problem of energy reduction will be one of the biggest challenges in these networks. In this paper, we will propose a new cluster-based method that can reduce the average energy consumption in each round using a new method for data packet transmission to BS . In this method, all normal nodes and all cluster heads (CHs) use a tree structure for transmit their data packets toward the cluster heads and BS respectively. In fact, the energy consumption is reduced in contrast to LEACH through the following modifications. 1) minimization of hop lengths which are necessary to transmission of data packets inside the clusters 2) via continuously data aggregation in and out of the clusters 3) the selection of the nearest cluster head to the BS for transmission of final data packet toward the BS in each round. Simulation results show that the new algorithm decreases the average energy consumption of sensor network in contrast to LEACH.

In this paper, we have proposed a two-tiered wireless sensor network (WSN) in which sensor nodes transmit data packets to their connected relay nodes (cluster heads). Each sensor node is associated with at least one relay node in the network. The proposed strategy determines the best set of relay nodes for upper-tier communication. These relay nodes are periodically reselected on the basis of residual energy information of sensor nodes. Relay node reselection mechanism balances the energy consumption of sensor nodes in a network. Moreover, greedy algorithm is used to create a set of possible routing solutions. With the help of genetic algorithm (GA), we find a best routing path from a set of possible routing solutions.

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.

A Wireless Sensor Network (WSN) is an emerging technology for a wide range of potential applications, including the environmental monitoring, medical, and target tracking systems. A WSN comprises a set of sensor nodes deployed in an area of interest. Sensor nodes are normally located in a dense and ad hoc manner, communicating each other in a multi-hop fashion in order to collect, process, and relay data. Most of WSNs are built on wireless, battery-powered sensor nodes. In such networks, it is hard to recharge or replace the energy-depleted nodes due to the desolate or harsh environment of the target area. Therefore, the efficient use of limited energy is a primary concern in designing protocols and usage strategies for WSNs. In this work an emergency scenario is considered and a WSN is used to monitoring the emergency area. To limit the power consumption, the WSN is activated only when the emergency occurs. Moreover an integrated protocol for routing and localization is developed to reduce communication, which is the most power-consuming operation in WSNs.