Body sensor networks (BSNs) carry heterogeneous traffic types having diverse QoS requirements, such as delay, reliability and throughput. In this paper, we design a priority-based traffic load adaptive medium access control (MAC) protocol for BSNs, namely, PLA-MAC, which addresses the aforementioned requirements and maintains efficiency in power consumption. In PLA-MAC, we classify sensed data packets according to their QoS requirements and accordingly calculate their priorities. The transmission schedules of the packets are determined based on their priorities. Also, the superframe structure of the proposed protocol varies depending on the amount of traffic load and thereby ensures minimal power consumption. Our performance evaluation shows that the PLA-MAC achieves significant improvements over the state-of-the-art protocols.

In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.

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Opportunistic usage selection of a licensed channel by a secondary user (SU) and its contention for data transmission is a challenging problem in coexisting cognitive radio network (CCRN). This is caused by the presence of many SUs from different CRNs in a shared environment, and the problem is further intensified when the user applications, with heterogeneous quality-of-service (QoS) requirements, require prioritized access to the opportunistic spectrum. The state-of-the-art protocols did not address the problem of efficient coexistence following both the dynamic spectrum availability and prioritized medium access. In this paper, a weighted fair medium access control protocol, namely WF-MAC, has been developed for overlay CR network that gives users proportionate accesses to the opportunistic spectrum following their application QoS requirements. The channel availability prediction using autoregression (AR) model and channel utility perception using exponentially weighted moving average (EWMA) facilitate WF-MAC to achieve more stable and fair access to the opportunistic spectrum. Our simulation experiment results depict the efficiency of the proposed WF-MAC protocol in achieving better spectrum utilization, weighted fairness, throughput, and medium access delay compared to the state-of-the-art protocols.