In wireless body area networks (WBANs), various sensors and actuators are placed on/inside the human body and connected wirelessly. WBANs have specific requirements for healthcare and medical applications, hence, standard protocols like the IEEE 802.15.4 cannot fulfill all the requirements. Consequently, many medium access control (MAC) protocols, mostly derived from the IEEE 802.15.4 superframe structure, have been studied. Nevertheless, they do not support a differentiated quality of service (QoS) for the various forms of traffic coexisting in a WBAN. In particular, a QoS-aware MAC protocol is essential for WBANs operating in the unlicensed Industrial, Scientific, and Medical (ISM) bands, because different wireless services like Bluetooth, WiFi, and Zigbee may coexist there and cause severe interference. In this paper, we propose a priority-based adaptive MAC (PA-MAC) protocol for WBANs in unlicensed bands, which allocates time slots dynamically, based on the traffic priority. Further, multiple channels are effectively utilized to reduce access delays in a WBAN, in the presence of coexisting systems. Our performance evaluation results show that the proposed PA-MAC outperforms the IEEE 802.15.4 MAC and the conventional priority-based MAC in terms of the average transmission time, throughput, energy consumption, and data collision ratio.
In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a locationbased k-means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods. INDEX TERMS Unmanned aerial vehicles (UAVs), coverage probability, stable clustering, k-means clustering.
The advancement in electronics, wireless communications and integrated circuits has enabled the development of small low-power sensors and actuators that can be placed on, in or around the human body. A wireless body area network (WBAN) can be effectively used to deliver the sensory data to a central server, where it can be monitored, stored and analyzed. For more than a decade, cognitive radio (CR) technology has been widely adopted in wireless networks, as it utilizes the available spectra of licensed, as well as unlicensed bands. A cognitive radio body area network (CRBAN) is a CR-enabled WBAN. Unlike other wireless networks, CRBANs have specific requirements, such as being able to automatically sense their environments and to utilize unused, licensed spectra without interfering with licensed users, but existing protocols cannot fulfill them. In particular, the medium access control (MAC) layer plays a key role in cognitive radio functions, such as channel sensing, resource allocation, spectrum mobility and spectrum sharing. To address various application-specific requirements in CRBANs, several MAC protocols have been proposed in the literature. In this paper, we survey MAC protocols for CRBANs. We then compare the different MAC protocols with one another and discuss challenging open issues in the relevant research.
Future Internet of Things (IoT) networks are expected to support a massive number of heterogeneous devices/sensors in diverse applications ranging from eHealthcare to industrial control systems. In highly-dense deployment scenarios such as industrial IoT systems, providing reliable communication links with low-latency becomes challenging due to the involved system delay including data acquisition and processing latencies at the edge-side of IoT networks. In this regard, this paper proposes a priority-based channel access and data aggregation scheme at the Cluster Head (CH) to reduce channel access and queuing delays in a clustered industrial IoT network. First, a prioritized channel access mechanism is developed by assigning different Medium Access Control (MAC) layer attributes to the packets coming from two types of IoT nodes, namely, highpriority and low-priority nodes, based on the application-specific information provided from the cloud-center. Subsequently, a preemptive M/G/1 queuing model is employed by using separate low-priority and high-priority queues before sending aggregated data to the Cloud. Our results show that the proposed prioritybased method significantly improves the system latency and reliability as compared to the non-prioritized scheme.
The recent advances in Internet of Things (IoT) have led to numerous emerging applications ranging from eHealthcare to industrial control, which often demand stringent Quality of Service (QoS) requirements such as low-latency and high system reliability. However, the ever-increasing number of connected devices in ultra-dense IoT networks and the dynamic traffic patterns increase the channel access delay and packet collision rate. In this regard, this paper proposes a sectorbased device grouping scheme for fast and efficient channel access in IEEE 802.11ah based IoT networks such that the total number of the connected devices within each sector is dramatically reduced. In the proposed framework, the Access Point (AP) divides its coverage area into different sectors, and then each sector is further divided into distinct groups based on the number of devices and their location information available from the cloud-center. Subsequently, individual groups within a sector are assigned to specific Random Access Window (RAW) slots, and the devices within distinct groups in different sectors access the allocated RAW slots by employing a spatial orthogonal access mechanism. The performance of the proposed sectorized device grouping scheme has been analyzed in terms of system delay and network throughput. Our simulation results show that the proposed scheme can significantly enhance the network throughput while simultaneously decreasing the system delay as compared to the conventional Distributed Coordination Function (DCF) and IEEE 802.11ah grouping scheme.
One of the crucial challenges in the recently emerging Internet of Things (IoT) applications is how to handle the massive heterogeneous data generated from a large number of resource-constrained sensors. In this context, cloud computing has emerged as a promising paradigm due to its enormous storage and computing capabilities, thus leading to the IoT-Cloud convergence. In such a framework, IoT devices can be grouped into several clusters and each cluster head can send the aggregated information to the cloud via a gateway for further processing. Although a number of clustering methods have been proposed for the conventional Wireless Sensor Networks (WSNs), it is important to consider specific IoT characteristics while adapting these techniques for wireless IoT networks. One of the important features of IoT networks that can be exploited while developing clustering techniques is the collaborations among heterogeneous IoT devices. In this regard, the network-wide knowledge at the cloud center can be useful to provide information about the device relations to the IoT gateway. Motivated by this, we propose and evaluate a cloud-assisted device interaction-aware clustering scheme for heterogeneous IoT networks. The proposed method considers the joint impact of residual energy and device closeness factor for the effective selection of cluster heads. Our results show that the proposed clustering scheme can significantly prolong the network lifetime, and enhance the overall throughput of a wireless IoT network.
In a wireless body area network (WBAN), wireless biomedical sensors are placed around, on, or inside the human body. Given specific requirements, WBANs can significantly improve healthcare, diagnostic monitoring, and other medical services. However, the existing standards such as IEEE 802.11 and IEEE 802.15.4 have some limitations to meet all the requirements of WBANs. Many medium access control (MAC) protocols have been studied so far, most of which are derived from the IEEE 802.15.4 superframe structure with some improvements and adjustments. However, the MAC protocols do not provide the required quality of service (QoS) for various types of traffic in a WBAN. In this paper, a traffic-aware MAC (TA-MAC) protocol for WBANs is proposed, in which time slots are dynamically allocated on the basis of traffic priority, providing the required QoS. According to the performance evaluation results, the proposed TA-MAC is better than IEEE 802.15.4 MAC and the conventional priority-based MAC in terms of transmission time, system throughput, energy efficiency, and collision ratio.
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