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 (TDMAslots), 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 TDMAslot. 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 802.15.4-based MAC protocol.

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 (MSOpath). 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 MSOpath by 61% in comparison to other approaches.

Considerable efforts have been expended on the centralised administration of WSNs. However, only very small functions, such as processing, memory, battery unit, and communication ability, can be configured using WSN nodes due to the associated resource restrictions. Moreover, unbalanced cluster construction and unbalanced energy dissipation can reduce network lifespan by a large extent. Therefore, this paper proposes an efficient clustering algorithm based on load adjustment for the IoT under the SDN architecture. The basic idea is to utilise cloud resources such as data-centres and storage units by a centralised SDN controller located over the cloud to calculate a load-balanced PSO clustering algorithm. The SDN controller implements the PSO that considers load-balancing, communication cost and remaining energy factors to construct a clustering table (CT). This CT contains information utilised in cluster formulation, such as an optimal set of cluster heads (CHs) and cluster members (CMs). Simulation results demonstrate that the suggested load-balancing algorithm is comparatively more efficient in terms of network lifetime, energy dissipation and volume of data send to the sink.

The recent advent of Software Defined Networking (SDN) and intelligent networks have prompted the researcher to conduct further investigations into the high-density Wireless Sensor Network (WSN). WSNs have inherent issues that limit their performance, such as sensor resource restrictions that affect power supply, memory, processing units and communications capabilities. This paper proposes new clustering, using a Whale Optimisation Algorithm (WOA) based on the concept of SDN. The proposed protocol considers both sensor resource restrictions and the random diversification of node density in the geographical area. It begins by dividing the sensing area by the SDN controller into virtual zones (VZs) to balance the number of cluster heads (CHs) according to the node density in each VZ; it then uses the WOA, which considers residual energy, communication cost and node density, to define the optimal set of CHs. Simulation results show that the proposed protocol has improved network lifetime and resulted in efficient energy dissipation; furthermore, it has increased the number of packets sent to the sink by approximately 55% compared to traditional protocols, and by about 20% compared to an optimisation based routing protocol.

Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, to find the optimal solution. This method also included multiple adaptive and random variables to guarantee that the solution space was explored and used in various optimization tasks. When all criteria are considered, the results of this study show that the SSA is efficient for least-distance path allocation. The simulation findings reveal a significant improvement over the well-known particle swarm optimization (PSO) algorithm, with recycling and disposal costs decreasing by 10% to 30%.

With the fast advancement of Internet of Things (IoT) technology, significant recent research into IoT in Wireless Sensor Networks (WSNs) has resulted in a significant reliance on Software Defined Networking (SDN). WSN has the potential for integration into the IoT. WSNs consume some problems that reduce system performance, for instance, resource limits for sensors that may affect power supply, communication capacity, memory, and processing units. In this paper suggests an optimized routing protocol built on Artificial Intelligence (AI), i.e., Future Search Algorithm (FSA) and Particle Swarm Optimization (PSO). Also, recent technologies for the large-scale IoT network to choose the optimum set of Clusters Head (CHs) named Future Search Software Defined Network (FSSDN) are based on the distances between nodes and the remaining energy. The cloud technology and SDN architecture have been used for the large-scale network in the proposed protocol. Finally, the experimental results showed an enhanced system performance of many features, such as a lifetime of the network, energy dissipation, and the number of packets sent to the same sink's base station. For instance, the network lifetime of the suggested protocol has been better by approximately 61%, 46% and 23% compared to LEACH, SEP and optimized routing protocol PSO-C, consecutively.