A TAXONOMY OF ROUTING PROTOCOLS IN SENSOR NETWORKS in .NET framework

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A TAXONOMY OF ROUTING PROTOCOLS IN SENSOR NETWORKS
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The elected local leader in the chain waits for data from its closest neighbors. These neighbors rst receive data from their own respective closest neighbors and aggregate the data before transmitting to the leader. The leader then sends the data received from its closest neighbors to the sink. Even though PEGASIS is similar to LEACH, it differs in the following ways. It uses multihop routing while only one node is selected to transmit to the base station. The reduction in overhead due to dynamic clustering as in LEACH leads to a performance gain of almost 100 to 300 percent in PEGASIS. This overhead is reduced when the following occur: (i) Transmission distances of non-leader nodes are minimized. (ii) One transmission is made to the sink per round by aggregating all the data. This reduces local energy consumption but introduces a large delay for nodes farther away from the leader node of that chain. It also results in a single point of failure by the bottleneck created at the chain leader. (iii) The number of transmission among the nodes are reduced leading to overall energy ef ciency.
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Threshold-Sensitive Energy-Ef cient Sensor Network Protocol (TEEN) [25]. The TEEN protocol is designed to respond to sudden changes in the sensed attributes and uses a hierarchical model along with a data-centric mechanism. Clusters are formed in a hierarchical fashion at different levels with elected clusterheads serving as communication links between each other and the data sink. Initially, the clusters are formed after which each clusterhead broadcasts two threshold values to all the nodes. These are hard and soft thresholds for the sensed attributes. The hard threshold is the minimum possible value of an attribute based on which the sensor will be transmitting data to the sink. When the sensed value of the attribute is greater than this threshold, the data are sent to the clusterhead. This enables the nodes to transmit only relevant data. Once a value above the hard threshold is sensed, the node checks if the difference in the current and earlier values is greater than the soft threshold; if so, the new data are transmitted. Hard and soft threshold values can be adjusted per requirements allowing to control the packet transmissions. This protocol is succeeded by the adaptive threshold-sensitive energy-ef cient sensor network protocol (APTEEN) [26], which aims at capturing periodic data collections and respond to time-critical events. While the architecture remains the same as TEEN, APTEEN supports three types of data query: (1) historical, to analyze and monitor past data values and take decisions based on these recorded values; (2) one-time, to take a snap view of the current network situation and visualize it at a particular time instant; (3) persistent, to monitor the network over a continuous time interval especially during an event taking place.
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Data Aggregation Exact and Approximate Algorithms [27]. The aim is to develop better data aggregation and in-network data processing schemes for energy savings in sensor networks. These lead to lesser packet transmissions and reduce redundancy, thereby helping in increasing the network lifetime.
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SENSOR NETWORKS ROUTING PROTOCOLS
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The protocol employs a hierarchical model that uses data aggregation and innetwork processing at two different levels of the network hierarchy. A set of nodes, called the local aggregators (LA), are elected. These form a backbone routing architecture on which the rst instance of data aggregation and routing is performed. From the set of LAs, another set of aggregators, called master aggregators (MA), are selected to form the second level of nodes to carry out the second level of data aggregation. Since choosing the optimal set of MAs is an NP hard problem, a separate integer linear program (ILP) is developed to identify the optimal MA set. The goal of maximizing network lifetime must be considered during the selection process of MAs. The time required to solve the ILP increases exponentially with the number of LAs. Three near-optimal approximation algorithms, which serve dual purposes of selecting MAs and employing routing to the external traf c sink, are proposed. The differences from conventional data aggregation approaches are as follows: (i) Data aggregation is performed at optimal network points rather than at arbitrary points in other approaches. The selected points maximize the network lifetime. (ii) Routing and data aggregation are carried out simultaneously with the proposed algorithms. (iii) There are no additional overheads due to dynamic clustering and topologies. The architecture is simple and uses a xed hierarchy of nodes with no additional complications. 6.4.5 Multipath Routing Multipath routing techniques compute multiple paths from source to destination to effectively route around failed nodes or invalid links. In single-path routing protocols, if a link fails, additional control packets have to be generated and broadcast to discover newer paths. Multipath routing avoids such pitfalls, and there is always a secondary route ready in the event of a route failure. Meshed Multipath Routing (M-MPR) [28]. Two ways of affecting disjoint multipath routing (MPR) include (i) Disjoint (or split) MPR (D-MPR) with selective forwarding (SF) in which case each packet is sent along different disjoint routes and the decision of path selection is made by the source on packet-by-packet basis and (ii) D-MPR with packet replication (PR) (or limited ooding) where multiple copies of a data packet are transmitted simultaneously along multiple disjoint routes from a source to a destination. A meshed multipath is set up in three steps: (i) acquiring neighborhood information, (ii) route discovery, and (iii) route reply as described below. r Acquiring Neighborhood Information: Each active node broadcasts its ID, residual battery power, and location information to local neighbors. For each active neighbor i, a node maintains the following information in its database: IDi , locationi , residual poweri . Since the sensor nodes are considered stationary, period update on neighborhood status is not needed unless the node is
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