The IEEE 802.11ah, also known as WiFi HaLow, is a scalable solution for medium-range communication in Internet of Things (IoT). While provisioning support for the IoT and machine-to-machine (M2M) communication, IEEE 802.11ah leverages various innovative medium access control (MAC) layer concepts, such as restricted access window (RAW), hierarchical association identification (AID), traffic indication map (TIM) segmentation, etc. This article presents a survey on various MAC protocols for IEEE 802.11ah. While discussing the essential features of IEEE 802.11ah, this survey points out various issues and limitations of such MAC protocols. Although there are some surveys available for MAC protocols of IEEE 802.11ah, they do not include a large number of schemes that have been recently proposed to solve different standardization and implementation-based issues. This article individually surveys issues and challenges in the different problem domains of the IEEE 802.11ah MAC protocol and analyzes the recently proposed solutions. Moreover, this article identifies various factors for further improvement of these protocols. Compared to other relevant surveys, this article emphasizes the issues and challenges to enable researchers to easily identify the problem domain.

Cloud computing has several favourable characteristics for supporting Internet of things (IoT) applications and services. While provisioning support for heterogeneous devices adapting different semantics in IoT, cloud‐based technology is highly used. In semantic model, attaching additional information to raw sensory data is accomplished with the help of ontology. However, the longer distance between the cloud and IoT devices becomes a bottleneck for critical IoT applications. In this study, the authors proposed a fog‐based semantic model called semantic‐fog to reduce this gap. The proposed model shifts some of the frequently used semantic services of the cloud to the edge of the sensor network. Additionally, it provides an efficient off‐loading technique among fog–fog and fog–cloud devices to reduce task execution time and energy consumption of the fog nodes. Furthermore, an effective mapping method for converting raw sensory data into Resource Description Framework format is also presented in this work. A comparative analysis among the relevant cloud‐based computing models and the proposed semantic‐fog model is performed in terms of service delay, energy consumption, network usage, and the total cost of the system. Simulation results show that semantic‐fog outperforms the cloud‐based models almost in all the aspects considered.

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.