Summary Machine‐type communication (MTC) such as device‐to‐device and machine‐to‐machine communication is a technology used to realize the concept of Internet of things (IoT). The fifth generation (5G) IoT is composed of large density of wireless sensor device. The massive roll out of the MTC devices is a serious challenge for wireless communication networks from operational and management perspective, including massive access, route selection, energy efficiency and network congestion. Many proposals have been put forward by the research community to cater to the issues and difficulties of massive MTC (mMTC) access in large density sensor network‐enabled IoT communication environment. Recently, data aggregation with efficient route selection has attracted a lot of research attention owing to its robust ability to resolve the above‐mentioned challenges. Existing data routing and aggregation methodologies are not efficient when adopted to heterogeneous IoT applications as they fail to bring trade‐off between energy efficiency and latency minimization. This research work proposes reliable and energy‐efficient route selection (REERS) scheme to reduce energy dissipation and latency for catering both real‐time and non‐real‐time applications. Experiment outcomes shows the REERS schemes achieves much superior performance than existing routing and aggregation methodologies over latency reduction and energy efficiency.

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Workload execution is composed by mapping larger tasks onto heterogeneous environments such as cloud platforms for enhancing the efficiency of workload resource management techniques. Execution of scientific workflow on a cloud platform is time-consuming, expensive, and requires a fault-tolerance guarantee. The existing methodology has emphasized on minimizing processing time to reduce costs. However, the processing cost can be reduced by minimizing energy consumption. In providing fault-tolerance while meeting the workload quality of service requirement the task is offloaded to a new physical machine; Hence, this increases energy consumption and thereby increases the cost of workload execution. In addressing the research challenges this paper presents the fault-tolerant aware (FTA) workload resource management (WRM) technique.First, the FTA-WRM optimizes processing and communication costs as an energy constraint leveraging the dynamic voltage frequency scaling technique.Then, a task offloading mechanism is modeled as an energy constraint with an application delay requirement for providing fault tolerance into the FTA-WRM. The experiment outcome shows the FTA-WRM significantly improves processing and energy efficiency in comparison with the existing workload resource management technique.

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