Implementation of DEEC Protocol Using Optimization Technique in Wireless Sensor Technology

Wireless sensor networks are employed in several applications like military, medical, household and environmental. In these applications energy factor is the determining factor in the performance of wireless sensor networks. In wireless sensor network, clustering is used as an effective technique to achieve scalability, self-organization, power saving, channel access, routing etc. Lifetime of sensor nodes determines the lifetime of the network and is crucial for the sensing capability. Clustering is the key technique used to extend the lifetime of a sensor network and also reduce energy consumption etc,. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks[1]. DEEC which is named as distributed energy efficient clustering protocol is selected as clustering protocol[1]. In DEEC, the cluster heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. Since in DEEC, the lifetime of sensors as well as network degrades very quickly. Hence in order to increase the network lifetime a new algorithm is proposed. This technique balances the cluster by using some backup nodes. The backup high energy and high processing power nodes replace the cluster head after the cluster reaches to its threshold limit. This approach will increase the network lifetime and will provide high throughput

Multi -Layer Based Data Aggregation Algorithm for Convergence Platform of IoT and Cloud Computing

Sensor Networks (SN) are deployed in smart domain to sense the environment which is essential to provide the services according to the users need. Hundreds or sometimes thousands of sensors are involved in sensor networks for monitoring the target phenomenon. Large scale of sensory data have to be handle by the sensor network which create several problems such as waste of sensors energy, data redundancy. To overcome these deficiencies one most practice solution is data aggregation which can effectively decrease the massive amount of data generated in SNs by lessening occurrence in the sensing data. The aim of this method is to lessen the massive use of data generated by surrounding nodes, thus saving network energy and providing valuable information for the end user. The effectiveness of any data aggregation technique is largely dependent on topology of the network. Among the various network topologies clustering is preferred as it provides better controllability, scalability and network maintenance phenomenon. In this research, a data aggregation technique is proposed based on Periodic Sensor Network (PSN) which achieved aggregation of data at two layers: the sensor nodes layer and the cluster head layer. In sensor node layer set similarity function is used for checking the redundant data for each sensor node whereas Euclidean distance function is utilized in cluster head layer for discarding the redundancy of data between different sensor nodes. This aggregation technique is implemented in smart home where sensor network is deployed to capture environment related information (temperature, moisture, light, H2 level). Collected information is analyzed using ThinkSpeak cloud platform. For performance evaluation amount of aggregated data, number of pairs of redundant data, energy consumption, data latency, and data accuracy are analyzed and compared with the other state-of-art techniques. The result shows the important improvement of the performance of sensor networks

Clustering algorithms for sensor networks and mobile ad hoc networks to improve energy efficiency

Includes bibliographical references (leaves 161-172).Many clustering algorithms have been proposed to improve energy efficiency of ad hoc networks as this is one primary challenge in ad hoc networks. The design of these clustering algorithms in sensor networks is different from that in mobile ad hoc networks in accordance with their specific characteristics and application purposes. A typical sensor network, which consists of stationary sensor nodes, usually has a data sink because of the limitation on processing capability of sensor nodes. The data traffic of the entire network is directional towards the sink. This directional traffic burdens the nodes/clusters differently according to their distance to the sink. Most clustering algorithms assign a similar number of nodes to each cluster to balance the burden of the clusters without considering the directional data traffic. They thus fail to maximize network lifetime. This dissertation proposes two clustering algorithms. These consider the directional data traffic in order to improve energy efficiency of homogeneous sensor networks with identical sensor nodes and uniform node distribution. One algorithm is for sensor networks with low to medium node density. The other is for sensor networks with high node density. Both algorithms organize the clusters in such a way that the cluster load is proportional to the cluster energy stored, thereby equalizing cluster lifetimes and preventing premature node/cluster death. Furthermore, in a homogeneous sensor network with low to medium node density, the clusterhead is maintained in the central area of the cluster through re-clustering without ripple effect to save more energy. The simulation results show that the proposed algorithms improve both the lifetime of the networks and performance of data being delivered to the sink. A typical mobile ad hoc network, which usually consists of moveable nodes, does not have a data sink. Existing energy-efficient clustering algorithms maintain clusters by periodically broadcasting control messages. In a typical mobile ad hoc network, a greater speed of node usually needs more frequent broadcasting. To efficiently maintain the clusters, the frequency of this periodic broadcasting needs to meet the requirement of the potentially maximum speed of node. When the node speed is low, the unnecessary broadcasting may waste significant energy. Furthermore, some clustering algorithms limit the maximum cluster size to moderate the difference in cluster sizes. Unfortunately, the cluster sizes in these algorithms still experience significant difference. The larger clusters will have higher burdens. Some clustering algorithms restrict the cluster sizes between the maximum and minimum limits. The energy required to maintain these clusters within the maximum and minimum sizes is quite extensive, especially when the nodes are moving quickly. Thus, energy efficiency is not optimized

An energy efficient routing scheme by using GPS information for wireless sensor networks

In the process of transmission in wireless sensor networks (WSN), a vital problem is that a centre region close to the sink will form in which sensors have to cost vast amount of energy. To communicate in an energy-efficient manner, compressed sensing (CS) has been employed gradually. However, the performance of plain CS is significantly dependant on the specific data gathering strategy in practice. In this paper, we propose an energy-efficient data gathering scheme based on regionalisation CS. Subsequently, advanced methods for practical applications are considered. Experiments reveal that our scheme outperforms distributed CS, the straight forward and the mixed schemes by comparing different parameters of the data package, and the considered methods also guarantee its feasibility.N/