The world in future will consist of smart environments that would primarily rely on sensory data from real world. Therefore, requirement of large scale Wireless Sensor Networks (WSNs) is inevitable in future. WSNs are made up of spatially distributed wireless sensor nodes that have limited resources, among which the power resource is the most crucial one. It has been seen previously that the sensor nodes consume around hundred to thousand times more energy in transmission of data than in execution of instructions. This has made the collection of data from large scale WSNs with minimal energy consumption as a big challenge. Various techniques have been adopted to reduce the consumption of energy in data collection. Among these, one signi cant means of energy conservation is the use of small sized data packets. In WSNs small sized data packets have shown to be more energy e cient than large and variable sized packets. However, this limitation in packet sizes require compression of data during its aggregation on the intermediate nodes in between the data collection points and the destination. In large-scale dynamic cluster based WSNs, the clusters are not created uniformly causing highly variable number of nodes in di erent clus- ters of the same network. Problem arises in large sized clusters where large amount of data is required to be transmitted within small packets. Predetermined compres- sion techniques require di erent sizes of packets for di erent sized clusters to maintain same level of losses in data. On the other hand, for small sized clusters, predetermined compression techniques may unnecessarily compress the data and incur losses. There- fore an aggregation technique is required that can control the compression of variable amount of data according to the space available in data packet while causing minimal data distortion. This thesis proposes an adaptive data aggregation algorithm that can adjust the com- pression level of data on a cluster head according to given payload size in real time while ensuring minimal distortion in the data. Thus wide range of data can optimally be adjusted in packets whose sizes have been regulated based on the channel conditions and transmission energy utilization. To the best of our knowledge no other work has been done in this domain where an adaptive aggregation of data is performed considering together the size of cluster, size of data packet available at cluster head and the spatial correlation among the data. To improve the performance of the proposed aggregation algorithm, uniformity in clus- ter sizes were required. Existing uniform clustering mechanisms have shown extra net- work energy consumption when applied on dynamic clustering protocols. Therefore, a uniform clustering technique is proposed in this thesis that can reduce the variability of cluster sizes in dynamic cluster based networks without consuming additional energy of the network. During the simulation of dynamic cluster based WSNs it was also ob- served that the broadcasting of control packets for cluster setup consumes considerable amount of network energy. Therefore, to reduce this overhead energy consumption, a mechanism is proposed that reduces the amount of broadcast packets for cluster setup. Altogether, in this thesis, a set of energy consumption issues are addressed that arise due to redundant data and control transmissions in dynamic cluster based WSNs. The main objective is to reduce the size of data that is transmitted in the network with minimal losses and to reduce the amount of control information during the cluster setup. The net result of the proposed set of solutions is the network energy conservation, network lifetime enhancement, optimal utilization of limited sized payload and load balancing in dynamic cluster based wireless sensor networks.