Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low‐powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault‐tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault‐tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put‐forth suggestions for further research.

Node faults in WSNs are often attributed to battery energy drain outs. While data aggregation is widely acknowledged as an energy conservation tool; the aggregator nodes are susceptible to battery energy drain out faults. When an aggregator/intermediary node in a data aggregation tree fails, the child nodes of the faulty node get detached from the aggregation tree and form disconnected nodes, termed as Affected Nodes (ANs) and/or subtrees. To tolerate such faults, we use the concepts of component-based graph theory and consider the subtrees as components that need to be connected back to the root-component tree. Based on this, we develop two component based self-healing fault-tolerant algorithms SCR and SCR-DTRA, that exploit the inherent redundancy in WSNs to determine alternate paths from the affected components to the root node. Unlike most other approaches, the hallmark of the component-based algorithms is that they preserve precedence relations in the data aggregation hierarchy. The results of our simulations show that the two algorithms succeed in the recovery of more than 90% of ANs and subtrees affected due to faults. In terms of longevity, the two algorithms, last more than twice the number of rounds compared to other algorithms used for comparison.

The primary focus of most approaches that model interference in WSN is to understand the conditions under which the signals received by a destination node are interfered by the transmissions of other nodes. While these models support the basic understanding of interference phenomenon between two nodes; models that attempt to map the nodes whose transmissions can potentially interfere with other nodes is highly desirable. To this end, we propose the Composite Interference Mapping (CIM) model that provides a holistic framework for determining a comprehensive map of all potential interferers for each and every active link in the WSN. Based on CIM model, we propose three Interference-Fault Free Transmission (IFFT) scheduling algorithms viz., (i) IFFT-STDMA and IFFT-ESTDMA for topology-free, and (ii) LL-IFFT-ESTDMA for tree-structured WSNs, which also addresses the Exposed Station (ES) and Hidden Terminal (HT) problems. We present the analytical studies to support the CIM model developed in the paper. The results of the simulation experiments have shown that the proposed CIM-based IFFT algorithms perform much better compared to some of the well-known algorithms in performance metrics such as: number of time slots, packet drop, number of dead nodes, honouring precedence relations, and addressing ES and HT problems.