| dc.description.abstract | Data collection is an important application of wireless sensor networks. Sensors are deployed in given region of interest. They sense physical quantity like temperature, pressure, solar radiation, speed and many others. One or more sinks are also deployed in the network along with sensor nodes. The sensor nodes send sensed data to the sink(s). This operation is known as convergecast operation. Once nodes are deployed, logical tree is formed. Every node identi es its parent node to transmit data towards sink. As TDMA (Time Division Multiple Access) completely prevents collisions, it is preferred over CSMA (Carrier Sense Multiple Access). The next step after tree formation is to assign time slot to every node of the tree. A node transmits only during the assigned slot. Once tree formation and scheduling is done, data transfer from sensors to sink takes place. Tree formation and scheduling algorithms may be implemented in centralized manner. In that case, sink node executes the algorithms and informs every node about its parent and time-slot. The alternate approach is to use distributed algorithms. In distributed approach, every node decides parent and slot on its own. Our focus is on distributed scheduling and tree formation. Most of the researchers consider scheduling and parent selection as two di erent prob- lems. Tree structure constrains e ciency of scheduling. So it is better to treat scheduling and tree formation as a single problem. One algorithm should address both in a joint manner. We use a single algorithm to perform both i.e. slot and parent selection. The main contributions of this thesis are explained in subsequent paragraphs. In the rst place, we have addressed scheduling and tree formation for single-sink heterogeneous sensor networks. In a homogeneous network, all nodes are of same type. For example, temperature sensors are deployed in given region. Many applications require use of more than one types of nodes in the same region. For example, sensors are deployed on a bridge to monitor several parameters like vibration, tilt, cracks, shocks and others. So, a network having more than one types of nodes is known as heterogeneous network. If all the nodes of network are of same type, the parent selection is trivial. A node can select the neighbor nearest to sink as parent. In heterogeneous networks, a node may receive di erent types of packets from di erent children. To maximize aggregation, appropriate parent should be selected for each outgoing packet such that packet can be aggregated at parent node. If aggregation is maximized, nodes need to forward less number of packets. So, less number of slots are required and energy consumption would be reduced. We have proposed AAJST (Attribute Aware Joint Scheduling and Tree formation) algorithm for heterogeneous networks. The objective of the algorithm is to maximize aggregation. The algorithm is evaluated using simulations. It is found that compared to traditional approach of parent selection, the proposed algorithm results in 5% to 10% smaller schedule length and 15% to 30% less energy consumption during data transfer phase. Also energy consumption during control phase is reduced by 5%. When large number of nodes are deployed in the network, it is better to use more than one sinks rather than a single sink. It provides fault tolerance and load balancing. Every sink becomes root of one tree. If ner observations are required from a region, more number of nodes are deployed there. That is, node deployment is dense. But the deployment in other regions may not be dense because application does not require the same. When trees are formed, tree passing through the dense region results in higher schedule length compared to the one passing through the sparse region. Thus schedule lengths are not balanced. For example, trees are T1 and T2. Their schedule lengths are SH1 and SH2 respec- tively. Every node in tree Ti will get its turn to transmit after SHi time-slots. If there is a large di erence between SH1 and SH2, nodes of one tree (having large value of SHi) will wait for very long time to get turn to transmit compared to the nodes of the other tree (having small value of SHi). But if SH1 and SH2 are balanced, waiting time would be almost same for all the nodes. Thus schedule lengths should be balanced. Overall sched- ule length (SH) of the network can be de ned as max(SH1,SH2). If schedule lengths are balanced, SH would also be reduced. We have proposed an algorithm known as SLBMHM (Schedule Length Balancing for Multi-sink HoMogeneous Networks). It guides every node to join a tree such that the schedule lengths of resulting trees are balanced. Through simulations, it is found that SLBMHM results 13% to 74% reduction in schedule length di erence. The overall schedule length is reduced by 9% to 24% compared to existing mechanisms. The algorithm results in 3% to 20% more energy consumption during control phase. The control phase involves transfer of control messages for schedule length balancing and for slot & parent selection. The control phase does not take place frequently. It takes place at longer intervals. So, additional energy consumption may not a ect the network lifetime much. No change in energy consumption during data transmission phase is found. The schedule lengths may be unbalanced also due to di erence in heterogeneity levels of regions. For example, in one region, two di erent types of sensors are deployed. But in the other region, four di erent types of sensors are present. When heterogeneity is high, aggregation becomes di cult. As a result, more packets ow through the network. Thus schedule length of the tree passing through region of two types of nodes will have smaller schedule length than the tree passing through the region of four types of nodes. We have proposed an algorithm known as SLBMHT (Schedule Length Balancing for Multi-sink HeTerogeneous Networks). It is an extension of SLBMHM. The proposed algorithm is capable of balancing schedule lengths no matter whether imbalance is caused due to di erence in density or di erence in heterogeneity. It is also evaluated through simulations. It is found that the SLBMHT algorithm results in maximum upto 56% reduction in schedule length di erence, maximum upto 20% reduction in overall schedule length and 2% to 17% reduction in energy consumption per TDMA frame during data transfer phase. It results in maximum 7% more energy consumption during control phase. As control phase does not take place very frequently, increase in energy consumption during control phase can be balanced by reduction in energy consumption during data phase. As a result, network lifetime is going to increase. | |
