Nt1310 Unit 5 Paper

Geographic Adaptive Fidelity (GAF): Geographic Adaptive Fidelity is an energy-aware location based routing algorithm planned for mobile ad-hoc networks but has been used to WSNs. Geographic Adaptive Fidelity conserving energy by switching off redundant sensors nodes. In this routing protocol, the entire network is classified into number of static zones and a virtual grid is made for the covered region. Every node utilizes its GPS-indicated location to link itself with a point in the virtual grid. Nodes linked to the same point on the grid are assumed equivalent with respect to packet routing costs. Nodes within a zone cooperate by choosing one node to show the zone for a period of time whereas the rest of the nodes sleep. A sample situation is considered from

In this section, we present the details of proposed protocol. Our protocol implements the idea of probabilities for cluster heads selection based on initial energy and residual energy of sensor nodes as well as the average energy of the sensor network.

A group of wireless sensor nodes (devices) dynamically constructs a temporary network without the exercise of any pre-existing network infrastructure or centralized administration. The main goal of ad-hoc networking is multihop broadcasting in which packets are transferred from source node to destination node through the intermediate nodes (hops). The main function of multi hop WSN is to enable communication between two terminal devices through a bit of middle nodes, which are transferring information from one level to another level. On the foundation of network connectivity, it dynamically gets to determine that which nodes should get included in routing, each node involved in routing transmit the data to further

GEAR does not require the need for a location database and assumes the static sensor node in the network field. It is assumed that each node is attached to a GPS device to get its current location in the network and also assumed that each node knows its remaining energy level and its neighbor’s location and remaining energy level through a simple neighbor hello protocol.

A WSN is a type of wireless networks that consists of collection sensor nodes which are tiny devices. Each sensor node of the network has different processing capability. It may contain multiple types of memory (program, data and flash memories), have a RF transceiver, have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion [13, 14]. Optimum need of each sensor node is to maximize its own utility function. Also the whole network requires resource assignments balance to perform in a useful and efficient way. This chapter presents a brief survey on WSNs showing its types, characterizing features, protocols and applications.

E-LEACH [12] is based on LEACH protocol to balance the energy consumption of sensor nodes in order to solve the overload energy consumption problem. This protocol adopts the same round concept with the original LEACH. The

3.2.10 HSA: Harmony Search Algorithms This is music based metaherustic optimization algorithm which is closely resembling with a music improvisation process where artist keep on polish the contributes request to get better harmony. By which it optimizing the energy utilization and limiting intra- cluster separation of the network. In this the base station figures and apportions nodes into clusters as indicated by the data of their remaining energy and area. The operation has two stages: clustering setup and data transmission. This algorithm gives change in term of energy utilization and network life time over LEACH protocol. With a little network distance across, vitality utilization of the network is practically same when utilizing diverse clustering protocols.

Abstract: LEACH TDMA schedules of each cluster are built independently. This can lead to collisions among clusters if the same channel is allotted to more than one overlapping clusters at the same time. To reduce this type of interference, in LEACH each cluster communicates using different CDMA codes. Thus, when a node is elected as CH, it chooses randomly from a list of spreading codes and informs all the nodes in the cluster to transmit using this spreading code. Hence all clusters can have intra-cluster communication in parallel. However, in large WSNs, as number of clusters increases self-noise generated by these codes may cross threshold limit and reach capacity limit. This forces to look at multiple channel use for improved throughput and energy-efficiency. We present an energy-efficient multi-channel routing protocol (CFCA) for wireless sensor networks. By computing the size of each cluster and identifying non-overlapping clusters, the proposed method allows channel reuse within WSN. This algorithm optimizes number of channels required for the given WSN.

Abstract—Hierarchical routing is a promising approach for point-to point routing with very small routing state. While there are many theoretical analyses and high-level simulations demonstrating its benefits, there has been little work to evaluate it in a realistic wireless sensor network setting. Based on numerous proposed hierarchical routing infrastructures, we surveyed some hierarchical clustering algorithms and briefly discussed them. Main purpose of this paper is to present some recent hierarchical protocols and point out silent features of them. These routing protocols very much benefit in prolonging network lifetime and save energy of sensor nodes.

Above methodologies recommend that vitality mindful directing conventions spare vitality. In any case, that convention brings about extra control overhead this can expend superfluous transmission capacity of the system. Extra equipment or programming is required in a few conventions, which may not be plausible for the portable nodes since versatile nodes ordinarily have low preparing force and constrained equipment assets. In this paper, we propose a base vitality directing convention called Improvised Energy Dynamic Source Routing (IEDSR) convention. Our approach is to keep away from extra control message. Existing control packets of DSR convention have been utilized as a part of request to execute the IEDSR convention. The IEDSR convention works in two stages' route disclosure and connection by connection control change. Two power levels have been utilized amid the route revelation period of the convention. At initial, a source node starts the route revelation to discover a route to its goal by communicating a demand bundle at low power level. On

The main goal of geographical routing is to use location information to define an efficient route search toward the destination. Geographical routing is very fitting to sensor networks, where data aggregation technique is used to minimize the number of transmissions send to the base station by eliminating redundancy among data packets from different sources. The need for data aggregation is to minimize energy consumption changes the computation and communications model in sensor networks from a traditional address paradigm to a data centric paradigm, where the content of the data is more significant than the identity of the sensor node that gathers the data. In data paradigm, an application may send a query to enquire about a phenomenon

Advertisement phase: It is the first step. The cluster head issues a notification to the nodes to become a cluster member in its cluster within its transmission range. The nodes recognize the information based upon the Received Signal Strength [10].

Wireless sensor networks are usually deployed in infrastructure-less environment with the sensor nodes themselves being battery powered (low powered) devices so, data aggregation is an attractive method to reduce the power requirements of the individual sensor nodes that sense, record and transmit the data about their surroundings. Data aggregation may refer to any number of methods that allow for the reduction of the volume of data that the sensor nodes must exchange by combining data from multiple sources and removing redundancies, thereby reducing the power consumption by the sensor nodes.

Abstract :- Due to recent improvement technologies in wireless communication ,there has been a fastest growth in wireless sensor networks technologies during the past few years. Many different architectures as well as algorithms and applications have been determined and implemented. The efficiency of these wireless sensor networks is as much as dependent on routing protocol directly affecting the network life and in wireless sensor network every sensor has a limited transmission range of every sensor node to obtain the sensing data. Due to this limitation of wireless sensor network Clustering of node is most popular techniques preferred in routing operations. In this paper, cluster based energy efficient clustering technique based on artificial bee colony algorithm and genetic algorithm is presented to increase the network lifetime. The name of artificial bee colony algorithm is come from bees cause It has been inspired by collective behavior of Bees as seeking food. To minimize the energy consumption it is significant to plan a data collection path with the minimum length to complete the data collection work. So in this paper, we determine that this problem which can be solve as traveling salesman problem, it 's also called as

NDAPSO in [67], is presented an energy-aware and clustered nodes architecture with using modern cluster head competition mechanism, which selecting an optimal cluster