Aarti Jain

A Novel Method of Modeling Wireless Sensor Network Using Fuzzy Graph and Energy Efficient Fuzzy Based k-Hop Clustering Algorithm

Clustering is one of the widely used methods to save energy, increase spatial re usability, and scalability. In this paper, we have proposed a new fuzzy graph based modeling approach for wireless sensor network which takes into account the dynamic nature of network, volatile aspects of radio links and physical layer uncertainty. The fuzzy graph constructs fuzzy neighborhoods which are used to identify all the prospective member nodes of a cluster. For computation of optimum centrality of a cluster, we have defined a new centrality metric namely fuzzy k-hop centrality. The proposed centrality metric considers residual energy of individual nodes, link quality, hop distance between the prospective cluster head and respective member nodes to ensure better cluster head selection and cluster quality. Finally, a new computationally inexpensive clustering algorithm has been developed. The simulation results demonstrate that the proposed algorithm resulted in prolonged network lifetime in terms of clustering rounds, scalability, higher energy efficiency and uniform cluster head and cluster members distribution, as compare to LEACH-ERE and CHEF.

Ant Colony Optimization Based Orthogonal Directional Proactive---Reactive Routing Protocol for Wireless Sensor Networks

Routing protocols for wireless sensor networks are important in addressing the various quality-of-service (QoS) issues pertaining to different applications. The most important QoS issues while designing routing protocols for WSN are energy awareness, scalability and network lifetime. However to deal with these issues the solutions provided in related literature have certain inherent disadvantages like high control overhead, low packet delivery ratio and requirement of global location information. In order to resolve these issues, we propose an orthogonal transmission based scalable, lightweight and energy aware routing protocol named as OD-PPRP which does not require global location information and has low control overhead. The proposed protocol OD-PRRP has the characteristics of both reactive and proactive routing protocols and utilizes fuzzy logic and Ant Colony Optimization to identify energy efficient and optimal paths. The simulation results show in both static and dynamic environment, OD-PRRP has better network lifetime, low end to end transmission delay, less overhead and high packet delivery ratio than other state of art QoS aware routing protocol viz. EARQ, EAODV and EEABR.

Betweenness centrality based connectivity aware routing algorithm for prolonging network lifetime in wireless sensor networks

Abstract Network lifetime is the key design parameter for wireless sensor network protocols. In recent years, based on energy efficient routing techniques numerous methods have been proposed for enhancing network lifetime. These methods have mainly considered residual energy, number of hops and communication cost as route selection metrics. This paper introduces a method for further improvement in the network lifetime by considering network connectivity along with energy efficiency for the selection of data transmission routes. The network lifetime is enhanced by preserving highly connected nodes at initial rounds of data communication to ensure network connectivity during later rounds. Bassed on the above mentioned concept, a connectivity aware routing algorithm: CARA has been proposed. In the proposed algorithm, connectivity factor of a node is calculated on the basis of Betweenness centrality of a node and energy efficient routes are found by using fuzzy logic and ant colony optimization. The simulation results show that the proposed algorithm CARA performs better than other related state-of-the-art energy efficient routing algorithms viz. FML, EEABR and FACOR in terms of network lifetime, connectivity, energy dissipation, load balancing and packet delivery ratio.

Eigenvector centrality based cluster size control in randomly deployed wireless sensor networks

We propose a new Eigenvector Based Cluster Size Control method Ev-CSC.Ev-CSC utilizes PageRank and α cut set to address uneven energy consumption.WSN is modeled as fuzzy graph to address dynamic and uncertain characteristics.Ev-CSC is independent of node density, shape of deployment region and sink position.Simulation results indicate improved network lifetime and energy efficiency. Cluster size control plays a significant role in balancing energy consumption and mitigating hot spot problem in wireless sensor networks. Cluster size control is necessary as clusters having higher member nodes consume significantly higher amount of energy while low member nodes in a cluster lead to under utilization of channel capacity. Further, cluster-heads located near to sink have to perform additional function of relaying data of other nodes. All these factors are responsible for hotspots or energy holes creation which in turn affect the network lifetime. In this paper, we propose a heuristic approach based upon Eigenvector centrality for cluster size control which we have named as Ev-CSC. While existing methods in literature consider distance of a cluster-head from sink, layered architecture, uniform deployment of nodes, prefixed sink location as a precondition, our proposed method does not have these constraints and is applicable to any kind of deployment, traffic pattern and node types. We have applied Ev-CSC on equal clustering methods and have also compared the same with state-of-the-art cluster size control methods. The experimental results demonstrate that our proposed method enhances the performance of respective equal clustering methods and performs better as compared to cluster size control methods.

Bio inspired optimal relocation of mobile sink nodes in wireless sensor networks

In wireless sensor networks (WSNs), during data transmission, sensor nodes which are closer to the sink nodes use up their energy earlier than nodes which are away as they relay more data packets. This cause to the energy imbalance in between sensor nodes, and leads to the connectivity holes and coverage holes, and finally lead to network failure. In this paper, we introduce a new method to tackle with this problem by optimal relocation of the mobile sink nodes and hence to balance load between the sensors. The sink nodes relocation is performed by using the bio-inspired Digital Hormone Model. Through this method the sink nodes are being guided to move in an intelligent way towards the optimal location, which basically improves the network lifetime and reduces the energy imbalance. After simulations, has been observed that the proposed method greatly improves the network lifetime on comparing with other available methods. The simulations are performed in java using jdk 6 and jre 6 version software.

Sink as Cluster head: An energy efficient clustering method for wireless sensor networks

Clustering of nodes is one of the basic techniques used for data collection in wireless sensor networks. It has been observed that in most of these clustering techniques based upon the local attributes of sensor nodes, some of them are selected as cluster-heads. The nodes which are selected as cluster-head have to dissipate more energy as they have to receive and aggregate data of their respective member nodes. Moreover, Cluster-head nodes near to sink dissipate more energy as these nodes have to relay data of far off placed nodes. Due to this fast dissipation of energy, energy holes are created near to sink which in turn decreases network lifetime. In order to address these issues, we present energy efficient clustering method and propose to select sink as one of the cluster-heads. By selecting sink as cluster-head, the nodes placed near to it can be spared from performing duties of cluster-heads and thus the problem of energy hole creation near to sink can be avoided. The proposed method has been named as energy efficient clustering with sink as cluster-head - EEC-SCH. We compare EEC-SCH with LEACH-ERE, LEACH and WCA. Our simulation results show that EEC-SCH results into enhanced network lifetime, throughput and balanced energy consumption among sensor nodes.

Traffic Aware Channel Access Algorithm for Cluster Based Wireless Sensor Networks

Partition of nodes into clusters is one of the most accepted method for achieving energy efficiency and scalability in wireless sensor networks. In this paper, we have modified the Fuzzy C-Means algorithm to partition the network into clusters such as to ensure that the resulted clusters are both spatially efficient and are sharing equal data transmission load. Further in this paper, we have re-defined the medium access protocol for cluster heads. The proposed medium access protocol is dependent upon the data traffic at the Cluster heads. Cluster heads with high traffic are given preference to access the channel and cluster head(s) having low traffic are made to wait for comparatively higher back-off time. By giving more time to cluster heads with lower initial data to collect more data, energy efficiency of the system is increased and contention losses are decreased due to reduction in number of transmissions between cluster heads and sink. The proposed method has been simulated and compared with LEACH protocol, a FCM based clustering protocol and Zonal based Deterministic Energy Efficient Clustering Protocol. The simulation results show that our proposed method performs better in terms of network performance parameters viz. network lifetime, energy dissipation, throughput and packet delivery ratio.

Optimal degree centrality based algorithm for cluster head selection in wireless sensor networks

Clustering is one of the popular methods in wireless sensor networks for achieving energy efficiency, scalability and efficient routing. Residual energy and topological features that are related to a node with respect of its structural position in the network are used for electing cluster heads. However optimal numbers of nodes that may belong to a cluster are not taken into consideration while selecting cluster heads. The proposed paper aims to define a new centrality metric “cluster optimal degree centrality”. Our proposed centrality metric addresses the optimal numbers of member nodes as well as energy efficiency of a cluster. Finally based upon the defined centrality metric, a Fuzzy Inference System based cluster head selection method has been proposed. The experimental results have demonstrated that the method can effectively prolong the network lifetime and enhance cluster head selection and results in high throughput as compared to LEACH, CHEF and LEACH-ERE.

Key player based optimal deployment of sink nodes in wireless sensor network

The performance of wireless sensor network heavily depends on efficiency with which the available energy resources are utilized and required QoS are ensured. Optimally placed multiple sinks play a significant role in enhancing network lifetime and reducing response time in wireless sensor networks. In this paper, we propose a computational intelligent method for the optimal placement of multiple sink nodes so that worst-case delay is minimized while keeping the energy dissipation during transmissions as low as possible. Our proposed method computes the optimal locations for sink nodes by identifying key players using Genetic Algorithm. Here Key players refer to the nodes that can be reached by as many remaining nodes as possible via direct links or short paths. The proposed method has been named KPP-MSP (key player problem based multiple sink positioning) and is simulated using Matlab. We also compare our method with Geographic Sink Placement (GSP) and Genetic Algorithm Sink Placement (GASP). The simulation results show that KPP-MSP has led to better network coverage, network lifetime and response time.

QoS aware orthogonal routing protocol for WSN using directional antennas

In this paper, we propose a QoS aware ORRP (Q-ORRP) routing protocol to minimise the energy consumption by reducing overhead and delay. The method of intersection of signals is used for transferring the data with improved QoS. Sink is enabled with orthogonal transmission, in which it sends an interest request message to all neighbouring nodes in all orthogonal directions. This process continues until the interest request matches with intended data. Then, the node sends the interest acknowledgement back to the sink through the reverse path. The intersection point of these two messages is considered as the rendezvous point (RP). When the RP receives these packets, it changes their direction and sends them toward destination node. Finally, sink establishes data dissemination tree towards the source through RP nodes. RPs with high residual energy and queue lengths are selected for establishing tree, so that the data transmission will be energy efficient and robust.