Advances in the development of low powered sensors has meant they can now provide solutions to IoT networks that suffer from restricted power supply and a lack of resource facilities. In this paper, a hybrid TDMA-CSMA/CA MAC protocol has been proposed that efficiently utilises the energy of the nodes and dynamically adapts the sleep/wake-up periods according to the variance in the network loads. This hybrid protocol first schedules the TDMA time slots (T DM A slots), and then allocates each slot to a group of devices that compete for the medium using the CSMA/CA. This case is different from the traditional CSMA/CA-TDMA hybrid protocol, in which all the devices compete to access the channel, following which, each successful device is allocated an individual time slot. At the commencement of each superframe, the base station broadcasts a scheduler table, which includes network grouping information that is used by the IoT devices to categorise themselves into wake-up and sleep groups. To reduce the number of collisions or channel access failures, this information permits only one group to compete for each T DM A slot. A three-dimensional Markov model is used to develop a per user stochastic behaviour for the proposed hybrid MAC protocol-based adaptable sleep mode. The simulation results demonstrate the effectiveness of the proposed protocol, which improves the network throughput and enhances energy conservation by 40%-60% more than the IEEE 802.15.4based MAC protocol.

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Extensive efforts have been undertaken to enhance the centralised monitoring-based software defined network (SDN) concept of the large-scale Intelligent-Internet of Things (I-IoT). Furthermore, the number of IoT devices in vast environments is increasing and a scalable routing protocol has therefore become essential. However, due to associated resource restrictions, only very small functions can be configured using IoT nodes, principally those related to the power supply. One solution for increasing network scalability and prolonging the life of the network is to use the mobile sink (MS). However, determining the optimal set of data gathering points (SDG), optimal path, scheduling the entire network with the MS in an energy efficient manner and prolonging the life of the network present huge challenges, particularly in large-scale networks. This paper therefore proposes an energy efficient routing protocol based on artificial intelligence (AI), i.e., particle swarm optimisation (PSO) and genetic algorithm (GA), for large scale I-IoT networks under the SDN and cloud architecture. The basic premise is to exploit cloud resources such as storage and data-centre units by using a centralised SDN controller-based AI to calculate: a load-balanced table of clusters (CT), an optimal SDG, and an optimal path for the MS (M S Opath ). Moreover, the proposed new routing technique will prevent significant energy dissipation by the cluster head (CH) and by all nodes in general by scheduling the whole network. Consequently, the SDN controller essentially balances energy consumption by the network during the routing construction process as it considers both the SDG and the movement of the MS. Simulation results demonstrate the effectiveness of the suggested model by improving the network lifespan up to 54%, volume of data aggregated by the MS up to 93% and reducing the delay of the M S Opath by 61% in comparison to other approaches.

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