Relay-Enabled Task Offloading Management for Wireless Body Area Networks

Abstract

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.

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

“…Various types of applications are recognized with enhanced sensing and communication capability, such as biomedical and wearable solutions for health monitoring, human activities control, organ implantation monitoring and remote surgical interventions. These applications require a high data rate, low latency and high quality of services (QoS) [39,40,52], in order to ensure precise, real-time and secure medical applications. With the integration of the IoMT, it is more challenging to identify the most appropriate strategy that enables efficient handling of the intensive and continuous requests from the installed wireless devices on the human body promptly.…”

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

“…A, N, SW [46], [111] Energy awareness regarding neighboring nodes to select the optimal route [50] Multi parameter cost function for the next hop selection [60] Selective data routing based on the data priority Securityrelated aspects HW, DP, SW [110] Content agnostic privacy and encryption protocol eliminating the need for asymmetric encryption [180], [181] Integration of lightweight cryptography solutions including more appropriate elliptic curve types or algorithm implementations [186] More efficient utilization of manufacturer-provide SoCs accelerated for cryptographic primitives execution [187] Finding trade-offs between the primitive and required level of the provided security Processing limitations HW, DP, SW [54] The use of heterogeneous multicore processor gateway as compared to little cores gateway working as a router [64], [86], [104], [105] Task offloading to leverage high computing resources of nearby devices for improved performance [106] Edge/fog/cloud computing techniques for optimal performance [107] Seamless resource sharing between heterogeneous mobile devices Storage limitations HW [47], [55] Data compression to reduce the size of the dataset for efficient data processing and storage [106] Edge/Fog/Cloud computing techniques for better performance [173] Data summarization and aggregation Lack of hardware acceleration HW, SW [47], [55] Data compression to reduce the size of the data set for more efficient data processing and storage [64], [86], [104], [105] Task offloading to leverage high computing resources of the nearby devices for the improved performance [186] Identifying and use of present hardware acceleration, which may not be accessible by the default Inefficient use of energy consuming modules HW, SW [62] Configurable data acquisition modules [88] Replacing high power consumption modules with low power alternates, e.g., using two accelerometers instead of a gyroscope as...…”

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

“…However, with the digitalization of the new power system, various new power services emerging in the power grid have brought massive data and diverse demands [2], imposing higher requirements on the service quality and processing capabilities of the network [3]. The traditional approach of centralized processing through the IoT management platform not only results in significant processing delays, making it difficult to meet business needs, but also puts tremendous pressure on the backbone communication network's carrying capacity and the cloud server's computing capabilities due to the transmission of massive data [4]. To meet the multi-dimensional resource and delay requirements of new power services and reduce transmission delays, it is necessary to allocate resources such as communication, storage and computing in the local communication network.…”