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Mobile edge computing (MEC) based solutions are essential and of great significance for a wide range of promising 5G wireless big data services such as remote healthcare systems and AR/VR games. Present research in this area focuses on the downlink resource allocation scenarios from MEC servers to user equipments (UEs). This paper considers a multi-user MEC-enabled wireless communication system, where UEs suffer limited communication and computation resources. To achieve higher energy efficiency and the better experience for UEs, we aim to maximize the number of offloaded tasks for all UEs in uplink communication while maintaining the computation resources of MEC at an acceptable level. The formulated problem is an NP-hard mixed-integer nonlinear programming problem and it is a challenge to solve it efficiently. As such, an efficient low-complexity heuristic algorithm is proposed, which provides a near-optimal solution with a low time cost. The results show that the proposed scheme achieves the higher number of successful offloaded tasks than the existing centralized resource allocation algorithm (CRAA) and centralized decision and resource allocation algorithm that UEs with the largest saved energy consumption accepted first (CAR-E) under different scenarios. Moreover, the relationship between the optimal transmission power and the computation resource of MEC is investigated. The results obtained in this paper can be extended to design a novel framework of communication, computation and smart coded caching MEC networks.

Recently streaming virtual reality (VR) video has become increasingly accessible to a growing number of consumers following the rapid development of network capacity and widespread high specification devices. Various solutions have been proposed including adaptive streaming methods for VR videos. However, the high bandwidth requirement of VR videos brings new challenges in designing proper adaptive streaming methods with respect to VR characteristics in terms of both coding and transmission. Streaming VR videos differ from traditional 2D videos as their users' perceptual Quality of Experience (QoE) is more sensitive to video stalls, quality variations, and as a matter of novelty, viewport dependent. The viewport movement is associated with new issues in adaptive streaming scheme design. In order to adapt to the fast viewport movement and restrict time constraints in VR videos, a tile-based VR video format adjustment is adopted in our research. In this paper, we present Q360AS, a novel adaptive streaming method for 360° VR videos that aims at maximizing the overall QoE during the transmission in environments with varying network conditions. The proposed method shows a notable increase in real-time QoE in comparison to existing adaptive VR video streaming methods.

The emerging fifth-generation wireless networks aim at ensuring that various low latency wireless services can be timely and satisfactorily served in any time, any location, and any way. However, even with well-developed infrastructure-based wireless networks, it is still challenged to realize the goal of communicating in low latency for emerging medical services or entertainments such as virtual reality and augmented reality. In this paper, an enhanced computing architecture is proposed to handle the wireless big data applications by integrating the wireless body area networks (WBANs) and mobile edge computing (MEC). Specifically, access points in WBANs are located next to the remote radio heads, which are designed to handle the delay sensitive computation tasks, while MEC is assigned to execute tasks with high computation. UEs are divided into local execution and reschedule sets, according to the energy consumption, delay and channel condition requirements. Moreover, a task offloading request algorithm is proposed, tasks are offloaded to AP or MEC based on the latency requirements. Consider the limited computation resources of AP, a task arrangement algorithm is given, tasks with lower priority will be offloaded to MEC to execute. The results demonstrate that UEs with higher computation capacity achieve lower average service time and higher number of successfully executed tasks at the cost of the network lifetime.

The developments of wireless body areas networks (WBANs) are heavily influenced by information and communications technologies, which have inspirited innovations in numerous aspects, including networking architecture, communication, computation and caching. However, these important enabling technologies have not been fully analyzed integrality in the existing work on WBANs. In this paper, a novel network architecture is proposed, which combines WBANs with mobile edge computing (MEC) to handle wireless big data services. By integrating the access point (AP) with remote radio head (RRH), latency sensitive tasks can be executed by AP while tasks with high computation resource can be offloaded to MEC. Moreover, we investigate the task offloading strategy in this framework, and some algorithms are given to support task classification, priority assignment. The results with different schemes are presented in details to show the effectiveness of the proposed caching based task offloading strategy.

The increasing amount of user equipment (UE) and the rapid advances in wireless body area networks bring revolutionary changes in healthcare systems. However, due to the strict requirements on size, reliability and battery lifetime of UE devices, it is difficult for them to execute latency sensitive or computation intensive tasks effectively. In this paper, we aim to enhance the UE computation capacity by utilizing small size coordinator-based mobile edge computing (C-MEC) servers. In this way, the system complexity, computation resources, and energy consumption are considerably transferred from the UE to the C-MEC, which is a practical approach since C-MEC is power charged, in contrast to the UE. First, the system architecture and the mobility model are presented. Second, several transmission mechanisms are analyzed along with the proposed mobility-aware cooperative task offloading scheme. Numerous selected performance metrics are investigated regarding the number of executed tasks, the percentage of failed tasks, average service time, and the energy consumption of each MEC. The results validate the advantage of task offloading schemes compared with the traditional relay-based technique regarding the number of executed tasks. Moreover, one can obtain that the proposed scheme archives noteworthy benefits, such as low latency and efficiently balance the energy consumption of C-MECs.

Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets.

Wireless in-body sensor networks are attracting increasing attention for numerous healthcare applications and medical services. Implant devices are widely employed to measure physiological signals inside the human body in a simple, long lifetime and continuous approach. However, due to the strict technical requirements of implant devices, there exist numerous key research challenges in this area. Considering realistic transmission power requirements based on the IEEE 802.15.6 standard safety regulations, the channel characteristics of implant to deep implant and implant to body surface communication scenarios are investigated. Furthermore, multiple vital quality of service (QoS) factors of implant communication channels are studied regarding channel capacity, bit error rate (BER), link quality and data rate. The system performance evaluation indicates that the data transmission rate can be optimized up to 10 Mbps. Furthermore, the transmission distance supported by in-body to on-body communication path is 2 meters, at 1 µW while the in-body to deep tissue region communication channel achieves less than 1 meter. The results proposed in this paper give an outlook of the intra-body networking system performance and can be applied to in-body communication system implementation research.

In this paper, we aim to minimize in-body sensor node energy consumption and to prolong the network lifetime by utilizing a relay strategy based on our proposed flexible quality of service (QoS) radio frequency communication module and advanced In-to-out body path loss (PL) model. A selection algorithm is derived and investigated in which the relay nodes with lower energy consumption and minimum distance to the coordinator will be chosen in each round. In this way, time division multiple access (TDMA) can be investigated to schedule data transmission from implants to the corresponding relays, and thus minimize the overall length of communication links. Moreover, a linear programming network lifetime model is proposed along with various subjective functions. Followed by constraints, simulations are conducted on a proposed topology network assuming two commercial transceivers. Results show that the existing two-relay based protocol achieves higher transmission data rates due to the availability of redundant communication pathways. However, our proposed incremental relay-based cooperative routing protocol outperforms the existing two-relay based scheme regarding total network lifetime, overall throughput, average power consumption and propagation delay.

In this paper, an analytical and accurate in-to-out (I2O) human body path loss (PL) model at 2.45 GHz is derived based on a 3D heterogeneous human body model under safety constraints. The bit error rate (BER) performance for this channel using multiple efficient modulation schemes is investigated and the link budget is analyzed based on a predetermined satisfactory BER of 10−3. In addition, an incremental relay-based cooperative quality of service-aware (QoS-aware) routing protocol for the proposed I2O WBAN is presented and compared with an existing scheme. Linear programming QoS metric expressions are derived and employed to maximize the network lifetime, throughput, minimizing delay. Results show that binary phase-shift keying (BPSK) outperforms other modulation techniques for the proposed I2O WBAN systems, enabling the support of a 30 Mbps data transmission rate up to 1.6 m and affording more reliable communication links when the transmitter power is increased. Moreover, the proposed incremental cooperative routing protocol outperforms the existing two-relay technique in terms of energy efficiency. Open issues and on-going research within the I2O WBAN area are presented and discussed as an inspiration towards developments in health IoT applications.

Wireless body area sensor networks (WBASNs) are becoming an increasingly significant breakthrough technology for smart healthcare systems, enabling improved clinical decision-making in daily medical care. Recently, radio frequency ultra-wideband technology has developed substantially for physiological signal monitoring due to its advantages such as low-power consumption, high transmission data rate, and miniature antenna size. Applications of future ubiquitous healthcare systems offer the prospect of collecting human vital signs, early detection of abnormal medical conditions, real-time healthcare data transmission and remote telemedicine support. However, due to the technical constraints of sensor batteries, the supply of power is a major bottleneck for healthcare system design. Moreover, medium access control (MAC) needs to support reliable transmission links that allow sensors to transmit data safely and stably. In this Letter, the authors provide a flexible quality of service model for ad hoc networks that can support fast data transmission, adaptive schedule MAC control, and energy efficient ubiquitous WBASN networks. Results show that the proposed multi-hop communication ad hoc network model can balance information packet collisions and power consumption. Additionally, wireless communications link in WBASNs can effectively overcome multi-user interference and offer high transmission data rates for healthcare systems.

The performance of six different shapes of pulses is compared for Packet Collision (PC) and Standard Gaussian Approximation (SGA) communication systems, based on the requirements of the Federal Communications Commission (FCC) emission mask. The shapes are a second derivative sinusoid Gaussian pulse, a second order Gaussian pulse and the first four orders of modified Hermite pulses. For the SGA model, the performance of time-hopping (TH) pulse position modulation (PPM) and pulse amplitude modulation (PAM) are compared, in terms of Bit Error Rate (BER). For the PC model, we investigated pulse position modulation (PPM) to analysis the performance from the energy attenuation issue. Results show that the zero order modified Hermite pulse outperforms other pulses in terms of BER in the PC model. It is also found that the performance of both the PC and SGA models will get worse as the number of users increases. The results can be applied to deal with interference issues in wireless body area networks (WBAN) in field of remote health monitoring and telemedicine systems design.