Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Energy conservation and fault tolerance are the most two important challenging issues for the development of large scale wireless sensor networks (WSNs). Failure of cluster heads in a cluster based WSN is more catastrophic as they are responsible not only for data aggregation and transmission of the aggregated data to the base station but also relaying data for multi-hop communication. Therefore, a routing algorithm in WSNs should be energy aware as well as fault tolerant. In this paper, we present a distributed energy efficient and fault tolerant routing algorithm for WSNs. The algorithm selects next-hop cluster head in energy efficient manner in the data routing phase and carefully restores the connectivity of the neighbours of a cluster head in case of its failure. The algorithm is tested extensively by considering several scenarios of WSN. The experimental results are compared with the existing algorithms in terms of several metrics to show the effectiveness of the proposed algorithm.

Due to the limited energy of wireless sensor networks (WSN), lots of research works have been done with topology algorithm in WSN. To solve the problem of the unbalanced energy consumption and improve the life circle, this paper proposes an Improved Energy Efficient Unequal Clustering (IEEUC) algorithm based on node degree, the clusters closer to the base station have less members than the farther one. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. Theoretic analysis and simulation results prove that the proposed algorithm can effectively balance the energy consumption and improve the lifetime of the networks.

In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection

Energy efficiency is critical in wireless sensor networks (WSN) for system reliability and deployment cost. The power consumption of the communication in multi-hop WSN is primarily decided by three factors: routing distance, signal interference, and computation cost of routing. Several routing algorithms designed for energy efficiency or interference avoidance had been proposed. However, they are either too complex to be useful in practices or specialized for certain WSN architectures. In this paper, we propose two energy efficient geographic routing algorithms (EEGRA) for wireless sensor networks, which are based on existing geographic routing algorithms and take all three factors into account. The first algorithm combines the interference into the routing cost function, and uses it in the routing decision. The second algorithm transforms the problem into a constrained optimization problem, and solves it by searching the optimal discretized interference level. We integrate four geographic routing algorithms: GOAFR+, Face Routing, GPSR, and RandHT, to both EEGRA algorithms and compare them with three other routing methods in terms of power consumption and computation cost for the grid and irregular sensor topologies. The results of our experiments show both algorithms conserve sensor's routing energy 30% ~ 50% comparing to general geographic routing algorithms. In addition, the time complexity of EEGRA algorithms is similar to the geographic greedy routing methods, which is much faster than the optimal SINR-based algorithm.

Clustering is an efficient technique to improve scalability and life time of wireless sensor networks (WSNs). However, energy efficiency of the sensor nodes and load balancing of the cluster heads (CHs) are the two most important issues that must be addressed in designing clustering algorithm for WSNs. In this paper, we first present a clustering algorithm called energy efficient load-balanced clustering algorithm (EELBCA) that addresses energy efficiency of the sensor nodes and load balancing of the CHs in terms of cardinality of the clusters. EELBCA is a min-heap-based clustering algorithm which is shown to run in O(n log m) time for n sensor nodes and m CHs. We next propose a parameter-based clustering algorithm (PBCA) that considers the energy efficiency of the sensor nodes and also the load balancing of the CHs in terms of overall energy consumption by incorporating communication load of the CHs with respect to the base station. The algorithm requires O(n log n) time. The experimental results show the efficiency of the proposed algorithms in terms of execution time, load balancing, energy consumption, number of live sensor nodes and the number of live CHs.

This paper presents a Multihop Routing Algorithm for Wireless Sensor Networks (MBEEC) based on the Dijkstra Algorithm. An important challenge in wireless sensor networks is to balance the energy consumption. The Bio-Inspired Algorithm for Energy Efficient Routing for Continuous Data Dissemination Networks Wireless Sensor (BEE-C) can provide important savings on energy consumption but does not provide routing calculations that ensures a long lived topology. Lifetime is an extremely important factor in a Wireless Sensor Network, because of the difficulty of maintenance, limited battery, memory and processing that sensors have. MBEEC inherits the properties of BEE-C but uses Dijkstra Algorithm for calculating the routes. To ensure the longer lifetime of the network, MBEEC addresses a multihop algorithm in terms of consumption in a matter more energy efficient than BEE-C using Dijkstra to calculate routes, thus balancing the entire network.

Providing route stability isconsidered to be crucial task in Wireless Sensor Networks (WSNs). Hierarchical topologyis considered to preserve the energy of the sensor networks in which the nodes with the higher residual energy is used to gather the data. However, most of the existingmethods on clustering have adopted a two-layer hierarchy. This paper proposes an Enhanced Three Layer Energy Efficient Clustering (ETLEEC) algorithm for WSNs. The proposed method uses an extra layer called the lattice head where the cluster head collects the information form sensor nodes and sends it to the lattice head which in turn send to the base station. Simulations are performed in network simulator and the results show that the proposed system is more efficient in terms of energy and quality of service compared to all other existing distributed algorithms.

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.

Wireless sensor networks (WSNs) have potential applications in environment monitoring, military surveillance, disaster systems, healthcare and many other areas. Clustering sensor nodes is an effective topology control method to reduce energy consumption of the sensor nodes for maximising network lifetime. However, cluster heads bear some extra load for data collection, data aggregation and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a challenging issue for the long run operation of the WSNs. In this paper, we first present a centralised algorithm called centralised energy efficient load balancing algorithm (CELBA) that addresses the issues of energy consumption of the sensor nodes and the load balancing of the cluster heads. We next extend our work to propose a distributed version of the CELBA, called distributed energy efficient load balancing algorithm (DELBA) for the same purpose. Simulation results demonstrate that the proposed algorithms perform better than the existing approaches.

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 this paper, we propose an efficient centralized clustering algorithm for wireless sensor networks. The algorithm which organizes the sensors into clusters uses memetic algorithm to determine cluster heads and sizes. Memetic algorithms which are similar to genetic algorithms are population-based heuristic search approaches for optimization problems. To assess the efficiency of the proposed clustering technique, we compare the network lifetime with those of other clustering algorithms. The results show that the proposed algorithm considerably increases the network lifetime.

Wireless Sensor Networks (WSNs) are usually comprised of a large number of sensor nodes which have limited power resources and require energy efficient routing algorithms. We have presented an Adaptive Transmission Power - Geographical and Energy Aware Routing (ATP-GEAR) algorithm in this paper, which routes the packet towards the destination by calculating the cost of the neighbors of a node in terms of their location and current battery level and transmitting the packet to the lowest costing node with the minimum required transmission power. We analyze the performance of our algorithm based on total power consumption and end-to-end delay from source to destination. Simulation results show that ATP-GEAR yields almost 25% less power consumption and 17% less end-to-end delay when compared with traditional Cluster Based Routing (CBR).

Due to the characteristics of energy-limited, how to reduce the energy consumption of the wireless sensors network has become the crucial point. Research shows that it can reduce energy consumption to organize the nodes in the network into a hierarchical cluster structure. This paper synthetically analyzes several existing cluster algorithms, and then proposes an energy-efficient and variation-sensitive clustering algorithm. This algorithm makes some modification on the basis of some existing classical clustering algorithms. In order to reduce energy consumption, we adopt some special measures to form cluster, elect cluster head and change the way in which the sense data is constantly sent to cluster head by non-cluster head node.

Quality of Service based and Energy Efficient Routing Algorithm in Wireless Sensor Networks

The main goal of research concerning energy aware routing algorithm for wireless sensor network is to increase the lifetime and long-term connectivity of the wireless sensor networks. However, most of energy aware routing algorithms do not take into account the clustering mechanism efficiently. In this paper, we present an efficient energy aware routing algorithm for the wireless sensor networks. In our algorithm, the data aggregation technique and adaptive clustering mechanism are considered for reducing and compacting the cumulative size of packets on the wireless sensor networks. Simulation results show that the energy usage of EAR-ACM is significantly reduced compared with the previous clustering based routing algorithm for the sensor networks.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkCluster HeadData PacketThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.