Wireless sensor networks (WSNs) and MOBILE ad hoc networks (MANETs) are used increasingly in many applications like wild habitat monitoring, forest fire detection, and military surveillance. After deployment in the field sensor nodes organize themselves into a multihop network, the base station being the central control point. Usually sensor nodes are greatly hampered by due to computation capabilities and energy reserves. A direct sensed information collection method from networks would be to forward each sensor node’s reading to the base station, through other intermediate nodes, before the base station processes received data. But its disadvantage is its high cost regarding communication overhead (or energy spent). Wireless networks gained interest due to advantages brought about by multihop, infrastructure-less transmission. However, high node mobility remains an issue due to error prone wireless channels and dynamic network topology, affecting even reliable data delivery in MANETs, especially in challenged environments. Conventional topology-based MANET routing protocols DSDV, AODV, DSR are vulnerable to node mobility a reason being predetermination of an end-to-end route before data transmission. Due to constantly and fast changing network topology, deterministic route maintenance is a problem. Discovery and recovery procedures consumer both time and energy. Data packets are either lost or delayed for long once a path breaks, until route reconstruction leading to transmission interruption. Computing aggregates in-network (combining partial results at intermediate nodes during message routing) in large WSNs significantly lowers the amount of communication and the resultant energy consumed. Data acquisition systems for WSNs construct a spanning tree rooted at the base station to perform aggregation along the tree. Important aggregates considered include Count, and Sum. It is straightforward to generalize aggregates to predicate Count (e.g., sensors with readings higher than 100 units) and Sum. Also, Count and Sum can be used to computer Average. Sum algorithm can also compute Standard Deviation and Statistical Moment of any order. Tree-based aggregation approaches do not accept communication losses due to node and transmission failures, common in WSNs.