The seamless integration of low-power, miniaturised, invasive/non-invasive lightweight sensor nodes have contributed to the development of a proactive and unobtrusive Wireless Body Area Network (WBAN). A WBAN provides long-term health monitoring of a patient without any constraint on his/her normal dailylife activities. This monitoring requires the low-power operation of invasive/non-invasive sensor nodes. In other words, a power-efficient Medium Access Control (MAC) protocol is required to satisfy the stringent WBAN requirements, including low-power consumption. In this paper, we first outline the WBAN requirements that are important for the design of a low-power MAC protocol. Then we study low-power MAC protocols proposed/investigated for a WBAN with emphasis on their strengths and weaknesses. We also review different power-efficient mechanisms for a WBAN. In addition, useful suggestions are given to help the MAC designers to develop a low-power MAC protocol that will satisfy the stringent requirements.

The Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure of IEEE 802.15.6 Medium Access Control (MAC) protocols for the Wireless Body Area Network (WBAN) use an Alternative Binary Exponential Backoff (ABEB) procedure. The backoff algorithm plays an important role to avoid collision in wireless networks. The Binary Exponential Backoff (BEB) algorithm used in different standards does not obtain the optimum performance due to enormous Contention Window (CW) gaps induced from packet collisions. Therefore, The IEEE 802.15.6 CSMA/CA has developed the ABEB procedure to avoid the large CW gaps upon each collision. However, the ABEB algorithm may lead to a high collision rate (as the CW size is incremented on every alternative collision) and poor utilization of the channel due to the gap between the subsequent CW. To minimize the gap between subsequent CW sizes, we adopted the Prioritized Fibonacci Backoff (PFB) procedure. This procedure leads to a smooth and gradual increase in the CW size, after each collision, which eventually decreases the waiting time, and the contending node can access the channel promptly with little delay; while ABEB leads to irregular and fluctuated CW values, which eventually increase collision and waiting time before a re-transmission attempt. We analytically approach this problem by employing a Markov chain to design the PFB scheme for the CSMA/CA procedure of the IEEE 80.15.6 standard. The performance of the PFB algorithm is compared against the ABEB function of WBAN CSMA/CA. The results show that the PFB procedure adopted for IEEE 802.15.6 CSMA/CA outperforms the ABEB procedure.

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

Proactive caching of the most popular contents in the cache memory of fog-access points (F-APs) is regarded as a promising solution for the 5G and beyond cellular communication to address latency-related issues caused by the unprecedented demand of multimedia data traffic. However, it is still challenging to correctly predict the user’s content and store it in the cache memory of the F-APs efficiently as the user preference is dynamic. In this article, to solve this issue to some extent, the deep learning-based content caching (DLCC) method is proposed due to recent advances in deep learning. In DLCC, a 2D CNN-based method is exploited to formulate the caching model. The simulation results in terms of deep learning (DL) accuracy, mean square error (MSE), the cache hit ratio, and the overall system delay is displayed to show that the proposed method outperforms the performance of known DL-based caching strategies, as well as transfer learning-based cooperative caching (LECC) strategy, randomized replacement (RR), and the Zipf’s probability distribution.

Wireless Body Area Networks (WBANs) consist of a limited number of battery operated nodes that are used to monitor the vital signs of a patient over long periods of time without restricting the patient’s movements. They are an easy and fast way to diagnose the patient’s status and to consult the doctor. Device as well as network lifetime are among the most important factors in a WBAN. Prolonging the lifetime of the WBAN strongly depends on controlling the energy consumption of sensor nodes. To achieve energy efficiency, low duty cycle MAC protocols are used, but for medical applications, especially in the case of pacemakers where data have time-limited relevance, these protocols increase latency which is highly undesirable and leads to system instability. In this paper, we propose a low power MAC protocol (VLPM) based on existing wakeup radio approaches which reduce energy consumption as well as improving the response time of a node. We categorize the traffic into uplink and downlink traffic. The nodes are equipped with both a low power wake-up transmitter and receiver. The low power wake-up receiver monitors the activity on channel all the time with a very low power and keeps the MCU (Micro Controller Unit) along with main radio in sleep mode. When a node [BN or BNC (BAN Coordinator)] wants to communicate with another node, it uses the low-power radio to send a wakeup packet, which will prompt the receiver to power up its primary radio to listen for the message that follows shortly. The wake-up packet contains the desired node’s ID along with some other information to let the targeted node to wake-up and take part in communication and let all other nodes to go to sleep mode quickly. The VLPM protocol is proposed for applications having low traffic conditions. For high traffic rates, optimization is needed. Analytical results show that the proposed protocol outperforms both synchronized and unsynchronized MAC protocols like T-MAC, SCP-MAC, B-MAC and X-MAC in terms of energy consumption and response time.