INTRODUCTION VARIOUS TYPES OF ROUTING HIERARCHICAL ROUTING NEED FOR PEGASIS PEGASIS-DESCRIPTION STEPS OF PEGASIS ADVANTAGES AND DRAWBACKS OF PEGASIS VARIOUS CHARTS AND TABLES OF PEGASIS HIERARCHICAL PEGASIS WORKING OF HIERARCHICAL PEGASIS
Routing in sensor networks is very challenging due to several characteristics that distinguish them from contemporary communication and wireless ad hoc networks. ◦ First of all, it is not possible to build a global addressing scheme for the deployment of sheer number of sensor nodes. ◦ Second, in contrary to typical communication networks almost all applications of sensor networks require the ﬂow of sensed data from multiple regions (sources) to a particular sink (command center).
◦ Third, generated data traﬃc has signiﬁcant redundancy in it since multiple sensors may generate same data within the vicinity of a phenomenon. ◦ Fourth, sensor nodes are tightly constrained in terms of transmission power, on-board energy, processing capacity and storage and thus require careful resource management. Due to such diﬀerences, many new algorithms have been proposed for the problem of routing data in sensor networks.
Routing in Sensor Networks is of the following Pattern:-
The main aim of hierarchical routing is to efficiently maintain the energy consumption of sensor nodes by involving them in multi- hop communication within a particular cluster and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the sink.
Cluster formation is typically based on the energy reserves of sensors and their proximity to the cluster heads. To allow the system to cope with additional load and to be able to cover a large area of interest without degrading the service, networking clustering has been pursued in some routing approaches.
In sensor networks, data fusion helps to reduce the amount of data transmitted between sensor nodes and the Base Station. Data fusion combines one or more data packets from different sensor measurements to produce a single packet as described in. The LEACH protocol presented is an elegant solution to this data collection problem, where a small number of clusters are formed in a self-organized manner. A designated node in each cluster collects and fuses data from nodes in its cluster and transmits the result to the Base Station. LEACH uses randomization to rotate the cluster heads and achieves a factor of 8 improvement compared to the direct approach, before the first node dies. Further improvements can be obtained if each node communicates only with close neighbours, and only one designated node sends the combined data to the Base Station in each round.
Though LEACH was able to provide hierarchical routing the objective of data fusion it still had some major drawbacks, the major of them are ◦ It used stationary sink which may be unpractical in some cases. ◦ The basic algorithm assumes any node can communicate with the sink. This gives the algorithm a limited scale to work upon.
An improved protocol called PEGASIS (Power-Efficient Gathering in Sensor Information Systems), which is near optimal for this data gathering application in sensor networks. The key idea in PEGASIS is to form a chain among the sensor nodes so that each node will receive from and transmit to a close neighbour. Gathered data moves from node to node, get fused, and eventually a designated node transmits to the BS. Nodes take turns transmitting to the BS so that the average energy spent by each node per round is reduced. Building a chain to minimize the total length is similar to the travelling salesman problem, which is known to be intractable. However, with the radio communication energy parameters, a simple chain built with a greedy approach performs quite well. The PEGASIS protocol achieves between 100 to 300% improvement when 1%, 20%, 50% and 100% of nodes node die compared to the LEACH protocol.
The main idea in PEGASIS is for each node to receive from and transmit to close neighbours and take turns being the leader for transmission to the BS. This approach will distribute the energy load evenly among the sensor nodes in the network. We initially place the nodes randomly in the play field, and therefore, the i –th node is at a random location. The nodes will be organized to form a chain, which can either be accomplished by the sensor nodes themselves using a greedy algorithm starting from some node. Alternatively, the Base Station can compute this chain and broadcast it to all the sensor nodes.
Chain Construction: To construct the chain we start from the furthest node from the Base Station and the greedy approach is used to construct the chain.
Gathering Data: Leader of each round is selected randomly. If N is the number of node I mod N node is selected as head for the I round. Randomly selecting head node also provides benefit as it is more likely to die for nodes at random locations thus providing robust network. When a node dies chain is reconstructed to bypass the dead node. Head node received all the fused data and sends it to the Base Station.
In a given round, we can use a simple control token passing approach initiated by the leader to start the data transmission from the ends of the chain. The cost is very small since the token size is very small. In Figure 3, node c2 is the leader, and it will pass the token along the chain to node c0.Node c0 will pass its data towards node c2. After node c2 receives data from node c1, it will pass the token to node c4,and node c4 will pass its data towards node c2.
PEGASIS performs data fusion at every node except the end nodes in the chain. Each node will fuse its neighbour’s data with its own to generate a single packet of the same length and then transmit that to its other neighbour (if it has two neighbours). In the above example, node c0 will pass its data to node c1. Node c1 fuses node c0’s data with its own and then transmits to the leader. After node c2 passes the token to node c4, node c4 transmits its data to node c3. Node c3 fuses node c4’s data with its own and then transmits to the leader. Node c2 waits to receive data from both neighbours and then fuses its data with its neighbours’ data. Finally, node c2 transmits one message to the Base Station.
Token Passing, Chain Based Considered near optimal-in a sense. Nodes die in random. Stationary nodes and sink. Every node has a global network map. Data Fusion. Greedy Chain Construction.
LEACH transmitting distances for most of the nodes reduces in PEGASIS. Messages received by each head node is 2 in PEGASIS which is less than LEACH. Experimental results show that PEGASIS provides improvement by factor 2 as compared to LEACH protocol for 50*50 m network and improvement by factor 3 for 100*100 m network. Since each node gets selected once energy dissipation is balanced in sensor nodes.
When a head node is selected there is consideration how far the Base Station is located from the head node. When a head node is selected its energy level is not considered. Since there is only one head node it may be bottleneck of the network causing delay. Redundant transmission of the data as only one head node is selected.
Hierarchical-PEGASIS is an extension to PEGASIS, which aims at decreasing the delay incurred for packets during transmission to the base station and proposes a solution to the data gathering problem by considering energy ☓ delay metric. In order to reduce the delay in PEGASIS, simultaneous transmissions of data messages are pursued.
In order to reduce the delay in PEGASIS, simultaneous transmission of data messages is pursued. This is a chain based protocol with CDMA capable nodes. It constructs a chain of nodes that forms a hierarchy tree where each selected node in a particular level transmits data to the node to the upper level of the hierarchy. This method ensures that the data transmission is done in parallel and the delay is significantly reduced.
Nodes that are at the receiving end in each level are promoted to the next higher level in the hierarchy. This chain based hierarchical protocol performs better than the PEGASIS. Although it avoids the clustering in LEACH and dynamic topology adjustment in LEACH it does not track the sensor’s energy.
Every node needs to be aware of the status of its neighbor, so that it knows where to route the data packet. This topology adjustment introduces significant overheads for specially large networks.