A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

Computational offloading, as an effective way to extend the capability of resource-limited edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for coping with delay-sensitive services. However, on one hand, applications commonly include several subtasks with dependent relations and on the other hand, the dynamic changes in network environments make offloading decision-making become a coupling and complex NP-hard problem, difficult to address. This paper proposes an intelligent Computational Offloading scheme for Dependent IoT Application ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CODIA</monospace> ), which decouples the performance enhancement problem into two processes: scheduling and offloading. First, a prioritized scheduling strategy is designed and its complexity is analyzed. Then, an offloading algorithm with offline training and online deployment is introduced. Due to the temporal continuity between subtasks, the dependency relation is transformed into a transition of device state, and the overhead for the whole application is considered to be the long-term benefit. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CODIA</monospace> leverages an Actor-Critic-based solution, where the IoT devices are able to deploy intelligent models and dynamically adjust the offloading strategy to achieve low latency, while controlling energy consumption. Finally, a series of experiments are conducted to verify the robustness and efficiency of the proposed solution in terms of convergence, latency, and energy consumption. 

Edge Intelligence: A Computational Task Offloading Scheme for Dependent IoT Application

Device-to-device (D2D) communication is one of the most promising technologies in wireless cellular networks that can be employed to improve spectral and energy efficiency, increase data rates, and reduce links latency. This paper investigates fifth generation and beyond (5GB) networks, the basics of D2D communication, applications, and classification. Herein, D2D in in-band (IBD) and out-band (OBD) modes are discussed. This paper also presents the integration of D2D communication with other prominent technologies and demonstrates the importance of integration with possible solutions in improving network performance. We further investigate the challenges of D2D communication, opportunities, and future research directions of D2D in 5GB networks. In addition, D2D communication in 6G network challenges and open research areas are introduced.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09737127.pdf

Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions

— In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delay-sensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26% and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

/pdf/intelligent-pricing-model-for-task-offloading-in-unmanned-1lkudp4u.pdf

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

High mortality rates due to cardiovascular diseases (CVDs) have attracted worldwide attention. It has been reported that mitochondrial dysfunction is one of the most important mechanisms affecting the pathogenesis of CVDs. Mitochondrial DNA (mtDNA) mutations may result in impaired oxidative phosphorylation (OXPHOS), abnormal respiratory chains, and ATP production. In dysfunctional mitochondria, the electron transport chain (ETC) is uncoupled and the energy supply is reduced, while reactive oxygen species (ROS) production is increased. Here, we discussed and analyzed the relationship between mtDNA mutations, impaired mitophagy, decreased OXPHOS, elevated ROS, and CVDs from the perspective of mitochondrial dysfunction. Furthermore, we explored current potential therapeutic strategies for CVDs by eliminating mtDNA mutations (e.g., mtDNA editing and mitochondrial replacement), enhancing mitophagy, improving OXPHOS capacity (e.g., supplement with NAD+, nicotinamide riboside (NR), nicotinamide mononucleotide (NMN), and nano-drug delivery), and reducing ROS (e.g., supplement with Coenzyme Q10 and other antioxidants), and dissected their respective advantages and limitations. In fact, some therapeutic strategies are still a long way from achieving safe and effective clinical treatment. Although establishing effective and safe therapeutic strategies for CVDs remains challenging, starting from a mitochondrial perspective holds bright prospects.

/pdf/mitochondrial-dysfunction-and-therapeutic-perspectives-in-kcct8zi3.pdf

Mitochondrial Dysfunction and Therapeutic Perspectives in Cardiovascular Diseases

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.

Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks

Computation offloading is one of the main use-cases of the Multi-access Edge Computing (MEC) paradigm which can help to save the battery life of the resource-poor mobile devices by transferring the computation-intensive tasks to the resource-rich edge cloud servers. However, the ever-increasing internet traffic can negate this benefit due to possible failures of the MEC servers. On the other hand, the growth of computation capabilities of end devices as the result of recent developments of Central Processing Units (CPUs), can help to enhance the MEC systems performance and broaden the concept of edge computing by using device to device communication (D2D). The majority of existing works on computation offloading assume the tasks are independent and can be executed in parallel; however, the dependency among tasks can introduce new problems. To investigate this issue, in this paper, we consider a basic three-node MEC system consists of a user node with sequentially-dependent tasks, a helper/relay node, and a MEC server located at the base station (BS). We formulate the offloading problem into an energy-efficiency minimization problem while satisfying the task-dependency and completion deadline requirements. The simulation results show the superior performance of our proposed method compared to the other approaches.

Energy-Aware Cooperative Offloading Framework for Inter-dependent and Delay-sensitive Tasks

The concept of Yang Qi in Traditional Chinese Medicine (TCM) has many similarities with mitochondria in modern medicine. Both are indispensable to human beings and closely related to life and death. This article discusses the similarities in various aspects between mitochondria and Yang Qi, including body temperature, aging, newborns, circadian rhythm, immunity, and meridian. It is well-known that Yang Qi is vital for human health. Interestingly, decreased mitochondrial function is thought to be key to the development of various diseases. Here, we further explain diseases induced by Yang Qi deficiency, such as cancer, chronic fatigue syndrome, sleep disorder, senile dementia, and metabolic diseases, from the perspective of mitochondrial function. We aim to establish similarities and connections between two important concepts, and hope our essay can stimulate further discussion and investigation on unifying important concepts in western medicine and alternative medicine, especially TCM, and provide unique holistic insights into understanding human health. 

https://link.springer.com/content/pdf/10.1007/s43657-022-00060-3.pdf

Mitochondria as the Essence of Yang Qi in the Human Body

6G attempts to provide a network that seamlessly integrates terrestrial, air, and satellite networks. 6G3D network has proven to be highly advantageous in remote and rural areas, where terrestrial network deployment is complex or unfeasible due to network infrastructure constraints. However, such a 3D network is highly dynamic and resource-constrained, making task scheduling critical. Based on 6G’s vision, the 3D network is to be operated with in-network intelligence, and many papers have proved that deep reinforcement learning (DRL) can improve task scheduling performance. However, they have not covered the online process for the on-demand or fast-deployed 3D network for 6G. This paper proposes an online DRL-based task scheduling algorithm, namely 3D-TS. The simulation results show that the DRL-based task scheduling algorithm improves scheduling quality by increasing training data in 3D networks while balancing scheduling quality and efficiency.

Shiwei Shen

Papers

Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks

Energy-Aware Cooperative Offloading Framework for Inter-dependent and Delay-sensitive Tasks

Mitochondria as the Essence of Yang Qi in the Human Body

Evaluation of an Intelligent Task Scheduling Algorithm for 6G 3D Networking